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Paper Citation Record · LEDGER

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate

As of 12 August 2026, this Paper Citation Record lists 85 of 85 outbound references and 0 inbound Pith citation observations for arXiv:2509.00397.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.00397 v1

Coverage vector

measured 85 of 85 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:45:22.027286Z

measured 85 of 85 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

85 of 85 outbound references displayed

  • verified exact5
  • verified fuzzy68
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 808146ee-e4c6-443c-93d2-32ea135c0f1f · outbound

This paper cites Pensando.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Pensando

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:14.551825Z digest=sha256:028fcea7b0b54da1a3a5ad66dca68c444edfd125334ee236ee4105ab3d877bd3

Observation dadc3b3b-12c8-44ce-ba4d-26773d201d71 · outbound

This paper cites Machine Learning for En- crypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Machine Learning for En- crypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity

Reference 2

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:14.619501Z digest=sha256:cc6fbd2529979381d62047840024f6364d6c7cd4c379a4376aad64ca4edc402c

Observation 97d3d645-b62b-4109-ad90-43c28fd5b9ef · outbound

This paper cites Opentuner: An Extensible Framework for Program Autotuning.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Opentuner: An Extensible Framework for Program Autotuning

Reference 3

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no resolver link, observed 2026-08-05T13:45:14.744583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:14.744583Z digest=sha256:7f17b7467ca7b05a15d1cc49f0572d55da6363dab2ee36c95c5fee1242b14fb4

Observation 687fa6a5-a512-4bf4-bba2-0a943dc1a1a1 · outbound

This paper cites Practical Traffic Analysis Attacks on Secure Messaging Applications.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Practical Traffic Analysis Attacks on Secure Messaging Applications

Reference 4

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no resolver link, observed 2026-08-05T13:45:14.848848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:14.848848Z digest=sha256:86d7d5f95cde4509fabcf3d6780edd28ebb0414ca0cec237a0093891ae8df24a

Observation 963e09f2-3ee3-4e90-98ea-1be18ce5a1ec · outbound

This paper cites an unresolved cited work.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Unresolved cited work

Reference 5

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no resolver link, observed 2026-08-05T13:45:14.924866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:14.924866Z digest=sha256:a8bda4944008d603d37ecde13e5d7059da777dc22e06fb21a24bb29c85831f6b

Observation 617ece89-a22e-40e9-97c8-e025adcc4fee · outbound

This paper cites Understanding data center traffic characteristics.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Understanding data center traffic characteristics

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:35.328483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:15.018076Z digest=sha256:f443b44d4b8d5119842968f83524cd0558c3ac07d0141a63ded1e22ee16f267f

Observation 32c08f3b-cbc7-4729-b3c7-97b843652c3f · outbound

This paper cites Bergstra, D.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Bergstra, D

Reference 7

Resolution
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raw_fallback, observed 2026-08-05T13:45:35.161459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:15.123779Z digest=sha256:878bbbf2fdc2782852b639b79d7aba9df587a7e8562246eddbbd83045220e980

Observation 6e362a41-9021-43af-9341-a40c15626b2b · outbound

This paper cites P4: Programming Protocol-Independent Packet Processors.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate P4: Programming Protocol-Independent Packet Processors

Reference 8

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raw_fallback, observed 2026-08-05T13:45:34.949513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:15.199449Z digest=sha256:645acb182b4104af887431cff54e43732b135a4b596c803e0b4c3810dcb7effc

Observation 1316a1b3-5344-49e4-bc49-f32595919601 · outbound

This paper cites Forwarding Metamorphosis: Fast Programmable Match-Action Processing in Hardware for SDN.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Forwarding Metamorphosis: Fast Programmable Match-Action Processing in Hardware for SDN

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:15.353963Z digest=sha256:3e9f035c0819426950b1c7ac4e9cdfb821f374d0944651a7789f22cbfe2e178d

Observation ca783d82-edb3-4da9-87ae-1a82fae2475e · outbound

This paper cites Trident 5 / BCM78800 Series.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Trident 5 / BCM78800 Series

Reference 10

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raw_fallback, observed 2026-08-05T13:45:34.561128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation bf655304-bd9b-4dc8-9a81-4dbee42ae0c1 · outbound

This paper cites Trident4/BCM56880 Series.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Trident4/BCM56880 Series

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:15.563106Z digest=sha256:561e53c2888c8d9904ec9ff6978fd110f390a7bc9f58c990c98246fb135be1e2

Observation 05d2fe06-b58d-4a5f-9a3a-59e4d0177cc2 · outbound

This paper cites pForest: In-Network Inference with Random Forests.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate pForest: In-Network Inference with Random Forests

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-05T13:45:15.647845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:15.647845Z digest=sha256:7272fb5a9717e182b681b2113951a913f51631e79db83adb4ae7225a3ed76a33

Observation 237f2abb-00db-4ac2-a226-5870c1a63625 · outbound

This paper cites CIC IDS 2017 Dataset.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CIC IDS 2017 Dataset

Reference 13

Resolution
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raw_fallback, observed 2026-08-05T13:45:34.095072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:15.772555Z digest=sha256:24b446428bb79fd146d098992f5af853da0d08c78695d61f1fb70bd7ee6511f0

Observation bb1fd775-9cda-42f0-9884-f7dc01a3a21f · outbound

This paper cites CIC IDS 2018 Dataset.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CIC IDS 2018 Dataset

Reference 14

Resolution
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raw_fallback, observed 2026-08-05T13:45:33.841401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:15.880045Z digest=sha256:554e301df3b49e6474f875c3f520dc663deaa1821f2cfbbba4feace5121511af

Observation 027f0a3a-35f7-4306-9fda-734e90f55112 · outbound

This paper cites CIC IoMT 2024 Dataset.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CIC IoMT 2024 Dataset

Reference 15

Resolution
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raw_fallback, observed 2026-08-05T13:45:33.593958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:15.976328Z digest=sha256:7a9cba0ffc1cd2faf93c493c9ee9da39acb9aec7eccedcb01a54cacdb8595614

Observation 3952ffdc-076c-4bd3-a427-0b31a72762f7 · outbound

This paper cites CIC IoT 2023 Dataset.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CIC IoT 2023 Dataset

Reference 16

Resolution
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raw_fallback, observed 2026-08-05T13:45:33.363639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:16.074417Z digest=sha256:91e8c660ce3abfe8fbe68bffc198c48ae8cf8cbd516272284a81efa176e339d7

Observation 8674f8a3-a048-45a6-878f-1038911e2a5a · outbound

This paper cites CIC VPN Dataset.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CIC VPN Dataset

Reference 17

Resolution
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raw_fallback, observed 2026-08-05T13:45:33.114248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:16.173635Z digest=sha256:14f6418bf073acdcba4b5371ddb5f3473a165e1f649a92822a60bb4d37f875a8

Observation 31ecd33f-d5fe-4a8f-923f-d6fecfce8a7a · outbound

This paper cites Tensor Processing Units (TPUs).

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Tensor Processing Units (TPUs)

Reference 18

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raw_fallback, observed 2026-08-05T13:45:32.871545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:16.290927Z digest=sha256:722dc859b73497f201a878fb300816dd47ea420d975b00f5b491d1f2969e8cd1

Observation 627c3739-721e-4fb6-9c89-e77c8d280d8f · outbound

This paper cites Intel P4 Insight.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Intel P4 Insight

Reference 19

Resolution
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raw_fallback, observed 2026-08-05T13:45:32.690700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:16.369018Z digest=sha256:c8a1373342d4bcb14f8d5f2ce7a25a47754001c26832f603ba5d0a49ec801f3e

Observation ad111f03-27ff-4a6c-a7ff-a905a5607eb8 · outbound

This paper cites Intel ® P4 Studio.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Intel ® P4 Studio

Reference 20

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raw_fallback, observed 2026-08-05T13:45:32.449087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:16.482291Z digest=sha256:ee7bcf2741a237a0a201315e07bf0f516688260a377dc5de15b7796f2ccec420

Observation f6d3a88f-1f21-4ac0-9cc3-2ecdfd79489d · outbound

This paper cites NVIDIA T4 Tensor Core GPU.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate NVIDIA T4 Tensor Core GPU

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:16.600791Z digest=sha256:f4981c76bc4c635979f4090456a637a34e645e7843e51c9b17d26324f07f95a8

Observation 79aea255-d9d3-44ca-a3be-b678d3370a14 · outbound

This paper cites an unresolved cited work.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Unresolved cited work

Reference 22

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raw_fallback, observed 2026-08-05T13:45:32.108315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:16.723102Z digest=sha256:213e9bd040329c0018636fcb4e6b11908b13dbffd668d88169c55ffb9de0922a

Observation 681c94fa-f3c4-4d48-a507-756ec66db875 · outbound

This paper cites Brighten Godfrey, and Michael Schapira.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Brighten Godfrey, and Michael Schapira

Reference 23

Resolution
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raw_fallback, observed 2026-08-05T13:45:32.017685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:16.878446Z digest=sha256:290e87b77c0dbb02cff3c7fe1ba0fbf57acb58d24cb820ec1426fc64afa88d1e

Observation 4ef27ebb-3d19-4a8b-a3f3-6951a972910d · outbound

This paper cites HorusEye: A Realtime IoT Malicious Traffic Detec- tion Framework using Programmable Switches.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate HorusEye: A Realtime IoT Malicious Traffic Detec- tion Framework using Programmable Switches

Reference 24

Resolution
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raw_fallback, observed 2026-08-05T13:45:31.828227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:16.955177Z digest=sha256:7c953f925dee5d38b55ab451852c59c04a07943e0531e5cfaba7259d127f5613

Observation d75a6c65-7630-475c-bf2a-102e603afec8 · outbound

This paper cites Doriguzzi-Corin, S.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Doriguzzi-Corin, S

Reference 25

Resolution
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raw_fallback, observed 2026-08-05T13:45:31.679376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:17.038347Z digest=sha256:2b72045e777106acab7cd65dbfb2b75a1d39b7ba7a6985e567b23e21b96331b6

Observation f2e3230b-55d4-48c7-86ca-a95def77673d · outbound

This paper cites Moongen: A Scriptable High- Speed Packet Generator.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Moongen: A Scriptable High- Speed Packet Generator

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:31.499877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:17.088403Z digest=sha256:18229959f1b7bc635ba6dbbc49f5008f64ce66bf093b32ff3d7f8b9a8bc3c62d

Observation 665756db-67d5-4d54-af5f-8a2adb1f8148 · outbound

This paper cites BOHB: Robust and efficient hyperparameter optimization at scale.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate BOHB: Robust and efficient hyperparameter optimization at scale

Reference 27

Resolution
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raw_fallback, observed 2026-08-05T13:45:31.391719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:17.174082Z digest=sha256:f57ca6c6ec8aa2397920362c521fecd9a86e49fe12c2f27e8058a02f50c8ffe1

Observation 61a228ef-9c52-4300-a5f3-a582ab5e725f · outbound

This paper cites Stratum OS.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Stratum OS

Reference 28

Resolution
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raw_fallback, observed 2026-08-05T13:45:31.225380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:17.264369Z digest=sha256:1dc59be314d40af68356690af1f3343be198019b3d353241f6fc449af403d4b9

Observation 6752a8a9-1dc3-4797-9685-62a0b20868b2 · outbound

This paper cites Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysis.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysis

Reference 29

Resolution
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raw_fallback, observed 2026-08-05T13:45:31.118771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:17.341890Z digest=sha256:343e3daff0517e6d83b043be2262c8a0ce3002a23ac1822bb80de3a2a3c1c497

Observation 909ad67c-5ff8-46e3-92d0-c3ffd09d2e4a · outbound

This paper cites Network Pro- gramming Language (NPL) Specification.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Network Pro- gramming Language (NPL) Specification

Reference 30

Resolution
verified exact
raw_fallback, observed 2026-08-05T13:45:22.990057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:17.397767Z digest=sha256:f1b6d3abbb241a20570348d3f3895a791cb20dd8ad8120d0e0ba4012399e2c2f

Observation f7bc04bb-4be8-4fcd-8453-b10e4379463c · outbound

This paper cites Characterization of encrypted and VPN traffic using time-related features.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Characterization of encrypted and VPN traffic using time-related features

Reference 31

Resolution
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raw_fallback, observed 2026-08-05T13:45:30.984614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:17.482104Z digest=sha256:954d881a73823a58d9e882b301fedbfd4afb6d2a3b349dfe08cacedc878d88ae

Observation b0f07b16-dad3-41cd-a02f-92b5f16d125f · outbound

This paper cites CICFlowMeter.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CICFlowMeter

Reference 32

Resolution
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raw_fallback, observed 2026-08-05T13:45:30.839238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:17.538690Z digest=sha256:299566af8b54c6edbb0ac658496171b22e5ca1150259734bdd127bde18fcbd1b

Observation 10c75ba9-75f0-4a70-a254-b9d840b3f936 · outbound

This paper cites PostgreSQL.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate PostgreSQL

Reference 33

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raw_fallback, observed 2026-08-05T13:45:30.727005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:17.595077Z digest=sha256:164409d7f5222e62e3fdd2460c641f3b41959163a4c12b121c670e9e9956533b

Observation 4eec58aa-cb0a-417e-9b1b-70de1629af72 · outbound

This paper cites Gupta, R.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Gupta, R

Reference 34

Resolution
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raw_fallback, observed 2026-08-05T13:45:30.611022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:17.681576Z digest=sha256:fa8c3f5455f070e3e709895b90374213e78bbcebd0e53efab8cc5066444bb528

Observation 27f123ae-f313-4594-99e7-18a3075eacae · outbound

This paper cites netFound: Principled Design for Network Foundation Models.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate netFound: Principled Design for Network Foundation Models

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-05T13:45:17.761178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:45:17.761178Z digest=sha256:5d1acd4dd2f79708815c332fc6c549e944d3cdc9483c51bb01f9ae8e967b4d7d

Observation 0dc098d3-1f61-415f-bac4-16d01e3eb220 · outbound

This paper cites CUBIC: A New TCP- Friendly High-Speed TCP Variant.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate CUBIC: A New TCP- Friendly High-Speed TCP Variant

Reference 36

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d39b64df-3f74-4ca7-bf91-23f221a39b3b · outbound

This paper cites Moore, Gianni Antichi, and Marcin Wój- cik.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Moore, Gianni Antichi, and Marcin Wój- cik

Reference 37

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 39e26a03-c9a5-4718-ab56-e77258a2a7d5 · outbound

This paper cites Understanding the CRC32 Hash: A Comprehensive Guide.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Understanding the CRC32 Hash: A Comprehensive Guide

Reference 38

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 17ec53f9-01ad-465d-9c1d-04fcb657f665 · outbound

This paper cites an unresolved cited work.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Unresolved cited work

Reference 39

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raw_fallback, observed 2026-08-05T13:45:30.098838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation c27e5d5a-2c47-4c01-8fdf-4587c36f91a8 · outbound

This paper cites Intel Ethernet Network Adapter X710.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Intel Ethernet Network Adapter X710

Reference 40

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation bcb2d14d-9136-4523-92eb-954c6c9200a3 · outbound

This paper cites Tofino: P4-programmable Ethernet switch ASIC that delivers better performance at lower power.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Tofino: P4-programmable Ethernet switch ASIC that delivers better performance at lower power

Reference 41

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raw_fallback, observed 2026-08-05T13:45:29.852578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:18.167784Z digest=sha256:a7d7844c3b2c2828ce150b000a29285d2d165e64f90def2ad30b19be4c95197c

Observation 1de7dec6-d868-4873-8c86-b72d8d9fe2a7 · outbound

This paper cites Tofino2: Second-generation P4-programmable Ethernet Switch ASIC that Continues to De- liver Programmability without Compromise.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Tofino2: Second-generation P4-programmable Ethernet Switch ASIC that Continues to De- liver Programmability without Compromise

Reference 42

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raw_fallback, observed 2026-08-05T13:45:29.708640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:18.232948Z digest=sha256:6fdf1aa246a1f472e1b051ad4583dff4f9dea85193fa0b15eda71c60cb157a6b

Observation 5416891b-ae8c-4be9-a527-842057a29c46 · outbound

This paper cites Leo: Online ML-based Traffic Classification at Multi-Terabit Line Rate.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Leo: Online ML-based Traffic Classification at Multi-Terabit Line Rate

Reference 43

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raw_fallback, observed 2026-08-05T13:45:29.560049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:18.306268Z digest=sha256:b4cc3227d8cead1ac64a6fbc263447351e33e2ce15946307fcffb85d7aeb73c5

Observation bea22191-251b-48c0-82ba-cc43a86cf898 · outbound

This paper cites AC-DC: Adaptive Ensemble Classification for Network Traffic Identification.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate AC-DC: Adaptive Ensemble Classification for Network Traffic Identification

Reference 44

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local_arxiv, observed 2026-08-05T13:45:22.782341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:18.370037Z digest=sha256:0a67d88ec9d33079901236802780691ed32504886986bfa9eff9f4215c2c377a

Observation 1369be95-4706-4508-b38f-cff54a32e534 · outbound

This paper cites GPflowOpt: A Bayesian Optimization Library using TensorFlow.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate GPflowOpt: A Bayesian Optimization Library using TensorFlow

Reference 45

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local_arxiv, observed 2026-08-05T13:45:22.613928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:18.445106Z digest=sha256:05fb01613f01c1dc9f3d79d105f47ea32b4aa711f2b7ddf0fb108ef4b838f7f4

Observation f0d7eb92-8b22-4a0d-9a69-94e4f235a27b · outbound

This paper cites The IPU: A New, Strate- gic Resource for Cloud Service Providers.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate The IPU: A New, Strate- gic Resource for Cloud Service Providers

Reference 46

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raw_fallback, observed 2026-08-05T13:45:22.465472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:18.520638Z digest=sha256:beb63492bb8926edec5a24aed9a6d29770b6a5b18f98bb9c3ff9bff314a72b49

Observation 4de58cdc-3f2f-4c45-a6e4-99df39e0ee15 · outbound

This paper cites Characterization of tor traffic using time based features.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Characterization of tor traffic using time based features

Reference 47

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raw_fallback, observed 2026-08-05T13:45:29.414068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:18.615821Z digest=sha256:910ff34996c7ee43d39061cf67dc57e7b99d7dda2269d78073fda645f6929a05

Observation 96720761-d142-4e96-b7b6-dc9eaf710d97 · outbound

This paper cites HPCC: High Precision Congestion Control.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate HPCC: High Precision Congestion Control

Reference 48

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raw_fallback, observed 2026-08-05T13:45:29.298770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:18.715144Z digest=sha256:896d98c4ad5df15974151a89fd119c4b66ca978343d9170aa53d0599e3b83394

Observation 1950a001-48ff-465a-94e7-d9fcf3fea4b5 · outbound

This paper cites SMAC3: A versatile Bayesian optimization package for hyperparameter optimiza- tion.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate SMAC3: A versatile Bayesian optimization package for hyperparameter optimiza- tion

Reference 49

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raw_fallback, observed 2026-08-05T13:45:29.190622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:18.823519Z digest=sha256:9ce0d89f784e30ea8f57756d9a877479d6d291d6914242249cff44905dddf581

Observation 664147f6-555a-4613-a885-553dac66eca5 · outbound

This paper cites ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysis.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysis

Reference 50

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local_arxiv, observed 2026-08-05T13:45:22.179777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:18.944491Z digest=sha256:0c70afeda7d8b4c3906baedc08eda68e57e74df6c7402b060281c8b70378cbdb

Observation c4d95599-d8f6-4373-8465-7270f7117b6e · outbound

This paper cites Neural Adaptive Video Streaming with Pensieve.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Neural Adaptive Video Streaming with Pensieve

Reference 51

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raw_fallback, observed 2026-08-05T13:45:29.073444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 05248c1a-a765-48b7-9dca-1a615088ca80 · outbound

This paper cites Homa: A Receiver-Driven Low-Latency Transport Protocol Using Network Priorities.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Homa: A Receiver-Driven Low-Latency Transport Protocol Using Network Priorities

Reference 52

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raw_fallback, observed 2026-08-05T13:45:28.934399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 7a5ba5b7-903b-4eaf-a2cb-ce5c39fae37a · outbound

This paper cites Algorithmic Performance- accuracy Trade-off in 3D Vision Applications using Hyper- mapper.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Algorithmic Performance- accuracy Trade-off in 3D Vision Applications using Hyper- mapper

Reference 53

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raw_fallback, observed 2026-08-05T13:45:28.823446Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 98907fd1-56a3-48ba-a7e3-6463c2cebfe1 · outbound

This paper cites ConnectX-6 Network Adapters.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate ConnectX-6 Network Adapters

Reference 54

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raw_fallback, observed 2026-08-05T13:45:28.719366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:19.313833Z digest=sha256:9dcecbfcb225def030637860e00f9c719a8e12f5d4b6e64a4b2a67cf4da101bf

Observation a9c1de6a-b9a1-4209-a2b0-74f5c7c96177 · outbound

This paper cites DOCA Documentation.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate DOCA Documentation

Reference 55

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raw_fallback, observed 2026-08-05T13:45:28.612437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:19.398282Z digest=sha256:92664040350317ad0fb83e3d34a10ee5a786192243b3359bb081843bacd877ef

Observation 40fc3a25-8286-4dc2-9451-ca28e57bdc90 · outbound

This paper cites Nvidia BlueField Data Processing Units.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Nvidia BlueField Data Processing Units

Reference 56

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raw_fallback, observed 2026-08-05T13:45:28.374338Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 4f7e175a-6c3c-4af9-8959-985f4f7352fd · outbound

This paper cites NVIDIA Spectrum-X: Ethernet Networking Platform for AI.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate NVIDIA Spectrum-X: Ethernet Networking Platform for AI

Reference 57

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raw_fallback, observed 2026-08-05T13:45:28.033527Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:19.554337Z digest=sha256:bb165d1d7da8a0ceedf942c39f5b8d7b72f1db2175e9e0d41a9fc3e8ebd45ca1

Observation 514f7972-2fd1-4799-b9a7-d8d46c908cf0 · outbound

This paper cites an unresolved cited work.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Unresolved cited work

Reference 58

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raw_fallback, observed 2026-08-05T13:45:27.893249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 9be831dc-268a-4808-9135-f8b5463468c0 · outbound

This paper cites Scikit-learn: Machine learning in Python.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Scikit-learn: Machine learning in Python

Reference 59

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raw_fallback, observed 2026-08-05T13:45:27.765428Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:19.670380Z digest=sha256:1165d1619c24f5698037a68dd4d4e49b2125e0a4e7b9a44126c9cf6d00a37a83

Observation d0a50a44-2eaa-44bf-a1cb-0703bf507742 · outbound

This paper cites an unresolved cited work.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Unresolved cited work

Reference 60

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raw_fallback, observed 2026-08-05T13:45:27.640731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d4e03497-10c5-426b-829a-d2d0d6973517 · outbound

This paper cites an unresolved cited work.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Unresolved cited work

Reference 61

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raw_fallback, observed 2026-08-05T13:45:27.485303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:19.830754Z digest=sha256:27192bcf1516ccfbae0b6967b4fbca8879449e180b945479a86a0a41a1b6011a

Observation 84e876dd-1ef8-4b05-9252-d3782d8dfc31 · outbound

This paper cites Elastic RSS: Co-Scheduling Packets and Cores Using Programmable NICs.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Elastic RSS: Co-Scheduling Packets and Cores Using Programmable NICs

Reference 62

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raw_fallback, observed 2026-08-05T13:45:27.357372Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:19.907983Z digest=sha256:8774cd4748490fd7d433e1df2dc7ee06b7fffe8880c21ac98f8e4cae1e0134a8

Observation 82e2a517-9082-4590-b13b-dec04df60ee8 · outbound

This paper cites The case for an intermediate representation for programmable data planes.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate The case for an intermediate representation for programmable data planes

Reference 63

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raw_fallback, observed 2026-08-05T13:45:27.203207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d23be946-2366-4ad7-b7b6-68ad4c3e9666 · outbound

This paper cites Query planning for robust and scalable hybrid network telemetry systems.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Query planning for robust and scalable hybrid network telemetry systems

Reference 64

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raw_fallback, observed 2026-08-05T13:45:27.061473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation abf60c31-84e1-47f0-99ad-48c12873d829 · outbound

This paper cites Exploring Hyperparameter Usage and Tuning in Machine Learning Re- search.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Exploring Hyperparameter Usage and Tuning in Machine Learning Re- search

Reference 65

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raw_fallback, observed 2026-08-05T13:45:26.920523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:20.293409Z digest=sha256:012d2022e1b7586304e681d2522cf2fec57cb86f4161c2c4754e2cf2a90b07af

Observation c09589b8-7830-4fcf-8bd1-8ba7c4f4075d · outbound

This paper cites Re-architecting Traffic Analysis with Neural Network Interface Cards.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Re-architecting Traffic Analysis with Neural Network Interface Cards

Reference 66

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raw_fallback, observed 2026-08-05T13:45:26.749518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:20.382155Z digest=sha256:aa500dd0f6b0b6162084ded71bf8c93fb22ecbd8ba0250b0bbe7222600b8e440

Observation b942314a-cae7-4e09-96bf-890e8dfff44d · outbound

This paper cites Taurus: A Data Plane Architecture for Per-Packet ML.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Taurus: A Data Plane Architecture for Per-Packet ML

Reference 67

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raw_fallback, observed 2026-08-05T13:45:26.636939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:20.451488Z digest=sha256:27ad3373d1db459db3f3901eec61c550b432faebe81c6903361a1cd9d867d282

Observation da7010ac-5166-44a0-aaf8-2f15c0077484 · outbound

This paper cites Homunculus: Auto-Generating Ef- ficient Data-Plane ML Pipelines for Datacenter Networks.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Homunculus: Auto-Generating Ef- ficient Data-Plane ML Pipelines for Datacenter Networks

Reference 68

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raw_fallback, observed 2026-08-05T13:45:26.445323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:20.554337Z digest=sha256:56d9cf7600a0bdc220865ce7169d9a7a2f8da4db4778e4de97fd9f3c72682fb8

Observation 2d928422-0bfd-49d2-8592-eeb83889e34b · outbound

This paper cites Tensorflow.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Tensorflow

Reference 69

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raw_fallback, observed 2026-08-05T13:45:26.149665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:20.655981Z digest=sha256:5e83dfec88dc11322c20c01c28f652c95c38925ccbeabc67151e40744b3d5451

Observation d8c862b8-c7cd-498c-b190-9f82f0681430 · outbound

This paper cites Malware traffic classification using convo- lutional neural network for representation learning.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Malware traffic classification using convo- lutional neural network for representation learning

Reference 70

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raw_fallback, observed 2026-08-05T13:45:25.862689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:20.733084Z digest=sha256:0445060d175bffa6a509f72098ca64580f59f183a5b864fe6bd0d8297ef6d845

Observation 6f698f23-b943-4d39-9a10-c467554e7e38 · outbound

This paper cites xNIDS: Explaining Deep Learning-based Network Intrusion Detection Systems for Active Intrusion Responses.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate xNIDS: Explaining Deep Learning-based Network Intrusion Detection Systems for Active Intrusion Responses

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:25.765123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:20.823352Z digest=sha256:c63396dc2737e384cf5b069bf87375da5c1d85b78d6331c72abf27b6f707b699

Observation d30a3bb7-c8b2-458e-9d65-bf542ec79cdd · outbound

This paper cites Bayesian Optimization.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Bayesian Optimization

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:25.662325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:20.928027Z digest=sha256:2a86e4a5deca1663d30d85605510851945cccd3be6e697622b3ebbbd3df8c276

Observation f99a18e4-c995-4ec1-83df-63ffb336f878 · outbound

This paper cites TCP ex machina: Computer-generated Congestion Control.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate TCP ex machina: Computer-generated Congestion Control

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:25.469326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:21.021679Z digest=sha256:b12619cfc69a81d8236a7d28ca20b56f1534ba6b83ab46111f1980b87106911e

Observation d07ce629-9a4b-455a-8d82-0e27e9d7e768 · outbound

This paper cites A GPU-accelerated network traf- fic monitoring and analysis system.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate A GPU-accelerated network traf- fic monitoring and analysis system

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:25.292798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:21.123041Z digest=sha256:5e2a7f57f0937f074a81571a9e223b9811b79cc096310eac22fc99452eb4bbf5

Observation 55254f05-b1a4-4fbf-a4e4-030a5e043c9c · outbound

This paper cites Mousika: Enable General In-Network Intel- ligence in Programmable Switches by Knowledge Distillation.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Mousika: Enable General In-Network Intel- ligence in Programmable Switches by Knowledge Distillation

Reference 75

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:25.049125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:21.232594Z digest=sha256:3b5ba436c79731609e1c72028847f27ca4e17ec74067cb5e3c8e934460cfb8d2

Observation 789c6f2a-7269-483b-9e89-681ee183363e · outbound

This paper cites Rosetta: Enabling Robust TLS Encrypted Traffic Classification in Diverse Net- work Environments with TCP-Aware Traffic Augmentation.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Rosetta: Enabling Robust TLS Encrypted Traffic Classification in Diverse Net- work Environments with TCP-Aware Traffic Augmentation

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:24.744171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:21.320640Z digest=sha256:e7514630a27c6304960f2d8cb48323a8b43c2a31f027c61a3427151618fdc259

Observation 431cbf71-b0af-4edb-8c67-11accd4349bc · outbound

This paper cites Alveo SN1000 SmartNICs.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Alveo SN1000 SmartNICs

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:24.413963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:21.414824Z digest=sha256:d0e03fcbb6f6fb46602385236f315e2a0d337951165684e13e3f86a04d90a250

Observation 968cf07c-4fea-4950-baa3-d4bec2164def · outbound

This paper cites Alveo U250 Data Center Accelerator Card.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Alveo U250 Data Center Accelerator Card

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:24.221561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:21.502939Z digest=sha256:25fe5c1703861ac8fcdd6203eaae509b4596458cd8fe6ce2ad4c596b2c5037c4

Observation 04093fc1-d0b6-4150-9abc-9d690cf2a8af · outbound

This paper cites Do Switches Dream of Machine Learning? Toward In-Network Classification.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Do Switches Dream of Machine Learning? Toward In-Network Classification

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:24.042193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:21.570990Z digest=sha256:7ae1fb28a2a65dfc79bb4d5fca089f5c0c2b53ce4ffaa15fe854a91c1d4a932a

Observation bbf77c45-b561-4618-93c8-14b7e6a12eb3 · outbound

This paper cites X2 Programmable Ethernet Switch.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate X2 Programmable Ethernet Switch

Reference 80

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:23.923411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:21.648145Z digest=sha256:c2c792eca18dd5b0b4e1ac5d90fa03800d045a991b2c770a0e52cbacb4a30228

Observation 133521e3-3ee9-4ec1-b80e-8cdcac38cd80 · outbound

This paper cites Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:23.742085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:21.725718Z digest=sha256:1912f3fd7c2a5785b73bd199f2621465cb1beeeac7c42bf2612cdda4ef8561d6

Observation ef6795ee-84c0-43aa-9674-d033837aded4 · outbound

This paper cites Pantheon: The Training Ground for Internet Congestion-Control Research.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Pantheon: The Training Ground for Internet Congestion-Control Research

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:23.581985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:21.799138Z digest=sha256:ae4da59e8c52caae45add851db5eeb5c680142be54eeb93c995e12ae1570d7fb

Observation f76b5e96-186a-48e9-a369-040175af36e5 · outbound

This paper cites Brain-on-switch: towards advanced intelligent network data plane via NN-driven traffic analysis at line-speed.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Brain-on-switch: towards advanced intelligent network data plane via NN-driven traffic analysis at line-speed

Reference 83

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:23.418855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:21.870854Z digest=sha256:1b5a8867cecc0ac6334a74ed17442ef8e44fd9ddbe211f95ee791a352291f3cb

Observation 156998ce-7612-4db1-a24b-6955e5f1ac8f · outbound

This paper cites Planter: Rapid prototyping of in-network machine learning inference.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate Planter: Rapid prototyping of in-network machine learning inference

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:23.282629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:21.956057Z digest=sha256:c04c889efdef118576830e444b5a7a757267b7edc9ce952bcb5d5d7ab5d2537b

Observation da5e728f-7137-4b8a-a110-d60b50863a3f · outbound

This paper cites An Efficient Design of Intelligent Network Data Plane.

SpliDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate An Efficient Design of Intelligent Network Data Plane

Reference 85

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T13:45:23.119357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-05T13:45:22.027286Z digest=sha256:a7d2732c1a780f754dfa463b0f84b015edaf80d154f010f8336f8255a9e868cf

Pith citing papers

No inbound Pith citation observations are available.