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Paper Citation Record · LEDGER

LaPrune: Controllable Differentiable Sparsity at Million Scale

As of 8 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2608.04057.

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

pith.paper-citation-record.v1
2608.04057 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T00:53:24.732436Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

63 of 63 outbound references displayed

  • verified exact3
  • verified fuzzy14
  • unresolved37
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch9

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 79660480-8b3e-462b-95d4-b46588a08754 · outbound

This paper cites Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education.

LaPrune: Controllable Differentiable Sparsity at Million Scale Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

Reference 1

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source=arxiv_source observed=2026-08-08T00:53:24.440333Z digest=sha256:57dcd49c3f63af492957aa3c04dcfcbcdc883d80ade5aa5ab060f20f4c5e7234

Observation 185aa214-9040-4152-a315-106e0c36ec8a · outbound

This paper cites Classification Problem Solving.

LaPrune: Controllable Differentiable Sparsity at Million Scale Classification Problem Solving

Reference 2

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source=arxiv_source observed=2026-08-08T00:53:24.445653Z digest=sha256:3076ef3153aa38b0550cc2eab0066fc6ae72537a5002abda9d28cc394e7d41fb

Observation 3dadf713-bdc9-43e0-919f-fe58f17e53b2 · outbound

This paper cites , title =.

LaPrune: Controllable Differentiable Sparsity at Million Scale , title =

Reference 3

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source=arxiv_source observed=2026-08-08T00:53:24.450092Z digest=sha256:2a5f3079d704739de69e87443d9f20f9a61e46450c39f8a60f6cbfe80fc59452

Observation 2451015c-1fdf-4ff3-93cb-6b9d99e3e2d0 · outbound

This paper cites New Ways to Make Microcircuits Smaller---Duplicate Entry.

LaPrune: Controllable Differentiable Sparsity at Million Scale New Ways to Make Microcircuits Smaller---Duplicate Entry

Reference 4

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source=arxiv_source observed=2026-08-08T00:53:24.455066Z digest=sha256:a405afee9332370414c5676cc0e3b45bbb7ac1a5f36e559b05bcbcbbd787e810

Observation 3bb87984-a814-411a-bea8-66bb61427f21 · outbound

This paper cites Clancey and Glenn Rennels , abstract =.

LaPrune: Controllable Differentiable Sparsity at Million Scale Clancey and Glenn Rennels , abstract =

Reference 5

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source=arxiv_source observed=2026-08-08T00:53:24.459062Z digest=sha256:1757738e4ed81c23efb22736bd18c077810a3e9500cbdd72eeb049caecfd999c

Observation a0508f4e-6ed4-4f32-bfc1-97c24d9ad25c · outbound

This paper cites and Rennels, Glenn R.

LaPrune: Controllable Differentiable Sparsity at Million Scale and Rennels, Glenn R

Reference 6

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source=arxiv_source observed=2026-08-08T00:53:24.463961Z digest=sha256:fc2c42df3c5f2bda36f4db94eea081dceb3ab791eb4cf7f32c16f9fdbfad32eb

Observation 121abb12-0609-4aa1-9926-f0259c425cd8 · outbound

This paper cites Poligon: A System for Parallel Problem Solving.

LaPrune: Controllable Differentiable Sparsity at Million Scale Poligon: A System for Parallel Problem Solving

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T00:53:24.468316Z digest=sha256:37bb87bf4635ebba0b9f073811669f7fcfb4afee080eff6d050af4141285a89b

Observation 3e372cbb-3273-4838-9fcd-9ecb93fb63e5 · outbound

This paper cites Transfer of Rule-Based Expertise through a Tutorial Dialogue.

LaPrune: Controllable Differentiable Sparsity at Million Scale Transfer of Rule-Based Expertise through a Tutorial Dialogue

Reference 8

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source=arxiv_source observed=2026-08-08T00:53:24.472737Z digest=sha256:8f357f408a46b6651c13b4d2555c0ecaa13ff5dea40d65a2bf4c0abf03a6f0f9

Observation 5b418186-7d73-44c9-81e8-26b9c38967d1 · outbound

This paper cites The Engineering of Qualitative Models.

LaPrune: Controllable Differentiable Sparsity at Million Scale The Engineering of Qualitative Models

Reference 9

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source=arxiv_source observed=2026-08-08T00:53:24.477140Z digest=sha256:5fe5112954172a032658fcba3c98041e4f04b9748371fd7ec8a8d134e765fc97

Observation da2f8d8a-e0ff-4430-812d-e9d18a2a3528 · outbound

This paper cites 2023 , eprint=.

LaPrune: Controllable Differentiable Sparsity at Million Scale 2023 , eprint=

Reference 10

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source=arxiv_source observed=2026-08-08T00:53:24.481201Z digest=sha256:9ba62f2edeabd1ecb28fcff68f23a5b2f409ac5fe6f2d1735970dad4f4b89e11

Observation 7de9bc34-54fa-44d6-8626-bba1c8d45773 · outbound

This paper cites Pluto: The 'Other' Red Planet.

LaPrune: Controllable Differentiable Sparsity at Million Scale Pluto: The 'Other' Red Planet

Reference 11

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source=arxiv_source observed=2026-08-08T00:53:24.485424Z digest=sha256:36cb39e40c8c2c6889fe59170ad973e4ccf11705b5fe557427ffb462aa91ce25

Observation cbac3f53-2bbe-4ea3-9c92-9be83f8e6b79 · outbound

This paper cites Stochastic Optimization of Sorting Networks via Continuous Relaxations.

LaPrune: Controllable Differentiable Sparsity at Million Scale Stochastic Optimization of Sorting Networks via Continuous Relaxations

Reference 12

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source=arxiv_source observed=2026-08-08T00:53:24.489951Z digest=sha256:cbdb807407fbc811f3598c00e722cfaaec0d4a8932a3dd0fa94d64a59972a45a

Observation 1de9cdfc-e3b0-45dd-8916-ac9eb68facb0 · outbound

This paper cites Fast Differentiable Sorting and Ranking.

LaPrune: Controllable Differentiable Sparsity at Million Scale Fast Differentiable Sorting and Ranking

Reference 13

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source=arxiv_source observed=2026-08-08T00:53:24.495060Z digest=sha256:fd0b18878b18b9737fe0bf5204b42a9d0e3933e343c768627c033808df04aa9f

Observation fc34212b-e20f-4cdd-b58a-f5e5039d699d · outbound

This paper cites SoftSort: A Continuous Relaxation for the argsort Operator.

LaPrune: Controllable Differentiable Sparsity at Million Scale SoftSort: A Continuous Relaxation for the argsort Operator

Reference 14

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local_arxiv, observed 2026-08-08T00:53:25.404223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.500046Z digest=sha256:be71f98b471c9c6ce1fdf87d2eff286e0fc89f29d70048a12b3de618eca525a7

Observation 2392d4c9-0f4c-4f0a-b589-f917d942fcef · outbound

This paper cites Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision.

LaPrune: Controllable Differentiable Sparsity at Million Scale Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision

Reference 15

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local_arxiv, observed 2026-08-08T00:53:25.386091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.505328Z digest=sha256:3b8aa9d0a306dd1f2ed39015f53e2444043916b3055de588223c8fa8a8e609cc

Observation d72d6974-d7fa-4d12-abe4-318a079a780d · outbound

This paper cites Differentiable Top-k Operator with Optimal Transport.

LaPrune: Controllable Differentiable Sparsity at Million Scale Differentiable Top-k Operator with Optimal Transport

Reference 16

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local_arxiv, observed 2026-08-08T00:53:25.367573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.510010Z digest=sha256:fb14a3132eed5d55291173bc5df0a18143438bad595e0875feac3ec528866434

Observation a8355d73-cd37-4d31-a9cc-6ae1dea67b56 · outbound

This paper cites and Puigcerver, Joan and Djolonga, Josip and Peyr.

LaPrune: Controllable Differentiable Sparsity at Million Scale and Puigcerver, Joan and Djolonga, Josip and Peyr

Reference 17

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raw_fallback, observed 2026-08-08T00:53:25.665696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.515518Z digest=sha256:14a080af3d9f46d9e4daef2deb466233845eb5f03f523040546505f5d1faefa2

Observation 2f4e3202-630b-40d1-bca2-8590121eccde · outbound

This paper cites Proceedings of the 42nd International Conference on Machine Learning , pages =.

LaPrune: Controllable Differentiable Sparsity at Million Scale Proceedings of the 42nd International Conference on Machine Learning , pages =

Reference 18

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.521133Z digest=sha256:c7d3d70d006922fa82296e08a2434f10e5b75674e1db2c2cf13e09664e65c29f

Observation 2d4afc36-661b-4c14-b42c-126359c3599d · outbound

This paper cites an unresolved cited work.

LaPrune: Controllable Differentiable Sparsity at Million Scale Unresolved cited work

Reference 19

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source=arxiv_source observed=2026-08-08T00:53:24.526119Z digest=sha256:1c602e03be709ea0b63ec192b134735244b25ac4b9a25d187daa58fdac248e4c

Observation 4f7c393b-3904-432d-8094-097a0916e35d · outbound

This paper cites 2025 , eprint=.

LaPrune: Controllable Differentiable Sparsity at Million Scale 2025 , eprint=

Reference 20

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.530575Z digest=sha256:746e4f3d8587c77972dedfa8ae24bf2d8377a644e08e92ea54f02ac242b155c9

Observation 5928049c-2f59-4a2f-9079-75ee789d1919 · outbound

This paper cites Movement Pruning: Adaptive Sparsity by Fine-Tuning.

LaPrune: Controllable Differentiable Sparsity at Million Scale Movement Pruning: Adaptive Sparsity by Fine-Tuning

Reference 21

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source=arxiv_source observed=2026-08-08T00:53:24.535174Z digest=sha256:f600dd316141d1070250ee37a80b9504fcf38f41211079ebe7f94bf4727e4dbc

Observation 17578e94-75da-4fc8-85aa-924e9cf99351 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

LaPrune: Controllable Differentiable Sparsity at Million Scale LoRA: Low-Rank Adaptation of Large Language Models

Reference 22

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source=arxiv_source observed=2026-08-08T00:53:24.540449Z digest=sha256:0430655dcaa522394b870e6d9665df8c7adeee4c5e088172e72930a5f067e32a

Observation 7936c35d-6f0f-4ea2-b5f9-055f9ae7af2b · outbound

This paper cites Learning both Weights and Connections for Efficient Neural Networks.

LaPrune: Controllable Differentiable Sparsity at Million Scale Learning both Weights and Connections for Efficient Neural Networks

Reference 23

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source=arxiv_source observed=2026-08-08T00:53:24.545019Z digest=sha256:5f17480d9d6b3785aa3ba97d6fdc69b19222ffb3e184acbd98596060a73119a2

Observation 24e9e44e-1c5c-40d9-8d53-536dfba63e50 · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

LaPrune: Controllable Differentiable Sparsity at Million Scale The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 24

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source=arxiv_source observed=2026-08-08T00:53:24.550426Z digest=sha256:248c8077de7a300eea54a61cf3b318e120c68135bb81d19f7190423b5b10f1c3

Observation 326e8022-8676-4cdc-be60-62ccfe260f8b · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

LaPrune: Controllable Differentiable Sparsity at Million Scale Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 25

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source=arxiv_source observed=2026-08-08T00:53:24.556630Z digest=sha256:79a93eed1cfbc7866c8ed8dfa75d6b9e80a205621afe37b684128f08b1739831

Observation 2907df80-93fd-4e78-89c4-1a06cadcdf00 · outbound

This paper cites Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity.

LaPrune: Controllable Differentiable Sparsity at Million Scale Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Reference 26

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source=arxiv_source observed=2026-08-08T00:53:24.561819Z digest=sha256:a9ecd42d16165f8712f7255c73bfdf7f18147ba1b0bae107e7d76426deeff393

Observation 5cf19484-a609-4667-beae-3740c334d917 · outbound

This paper cites DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification.

LaPrune: Controllable Differentiable Sparsity at Million Scale DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

Reference 27

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source=arxiv_source observed=2026-08-08T00:53:24.566993Z digest=sha256:0499dcc81db9f89c8fade44240b395214e7aa363fdd9de00babcbcb387b08bb4

Observation 2b6321df-cf0c-4b0d-ba13-c34d248406a7 · outbound

This paper cites k-Sparse Autoencoders.

LaPrune: Controllable Differentiable Sparsity at Million Scale k-Sparse Autoencoders

Reference 28

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source=arxiv_source observed=2026-08-08T00:53:24.572115Z digest=sha256:b00da7e684fe3e9497ae5c3c51109631a097c1a2610c5d89f585e38644cdf239

Observation 3461493d-fe1d-40e6-b5f4-8f8e327d7d9a · outbound

This paper cites Scaling and Evaluating Sparse Autoencoders , booktitle =.

LaPrune: Controllable Differentiable Sparsity at Million Scale Scaling and Evaluating Sparse Autoencoders , booktitle =

Reference 29

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verified fuzzy
raw_fallback, observed 2026-08-08T00:53:25.613911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.576889Z digest=sha256:bc5516b7cfef5e6d84897f6ebaa0090a64b007c0d301853dd85c9d245b2de1bd

Observation f8d416b0-f933-4f6d-9f88-44d86c98b818 · outbound

This paper cites Learning with Differentiable Perturbed Optimizers.

LaPrune: Controllable Differentiable Sparsity at Million Scale Learning with Differentiable Perturbed Optimizers

Reference 30

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metadata mismatch
local_arxiv, observed 2026-08-08T00:53:25.238895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.581423Z digest=sha256:b35d378f742f19a715acae94c1aae0c94c077e4093573530a4ccad18b9cc968f

Observation f17b255c-9720-405d-be45-c27ae272190c · outbound

This paper cites Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances.

LaPrune: Controllable Differentiable Sparsity at Million Scale Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances

Reference 31

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source=arxiv_source observed=2026-08-08T00:53:24.585509Z digest=sha256:0c98e34d2008d9e3be95fe16d5ec56a12c9687aac129eb80791a9f22e13a1f90

Observation a15c8b8e-0e9e-4739-8d14-ead42005a5dd · outbound

This paper cites , title =.

LaPrune: Controllable Differentiable Sparsity at Million Scale , title =

Reference 32

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source=arxiv_source observed=2026-08-08T00:53:24.589588Z digest=sha256:3a5496d9a90fb5f2b27d80873f7b4d319a6205d1a804325a9c21714a665f3ef0

Observation 6dbaba0a-46ca-47fc-8201-4f1e665906b3 · outbound

This paper cites , title =.

LaPrune: Controllable Differentiable Sparsity at Million Scale , title =

Reference 33

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verified fuzzy
raw_fallback, observed 2026-08-08T00:53:25.592054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.593775Z digest=sha256:889e66003476c604d1f183432fc3c99dab2a3cc4d682bd5ddd346a4afc85bf92

Observation aaa6e3ce-8e3b-465d-8ef9-a36bb7750f99 · outbound

This paper cites an unresolved cited work.

LaPrune: Controllable Differentiable Sparsity at Million Scale Unresolved cited work

Reference 34

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.598131Z digest=sha256:2938e8268074f8a74d0c7ad5fc42da5e6fc281d7302bab6de10e50759ef7c5e1

Observation 4d4cdea8-36e4-4e53-9d60-6ca3cdc7a56b · outbound

This paper cites The Limitations of Deep Learning in Adversarial Settings.

LaPrune: Controllable Differentiable Sparsity at Million Scale The Limitations of Deep Learning in Adversarial Settings

Reference 35

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source=arxiv_source observed=2026-08-08T00:53:24.602051Z digest=sha256:99078e0b15f8aa4f76c9befcf0d9c548e3450f3e0e342ca124b2e1c99b431036

Observation 86dea56a-c7cd-460a-87af-7f0ae52fcfe1 · outbound

This paper cites SparseFool: a few pixels make a big difference.

LaPrune: Controllable Differentiable Sparsity at Million Scale SparseFool: a few pixels make a big difference

Reference 36

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metadata mismatch
local_arxiv, observed 2026-08-08T00:53:25.191697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.606846Z digest=sha256:5530476d60faf5061b6d6e8d225e9d6508a20b3e77636e64959c8e33c605f1a4

Observation 3da726dd-8c53-4fa7-82cf-cc80b524f879 · outbound

This paper cites GreedyFool: Distortion-Aware Sparse Adversarial Attack.

LaPrune: Controllable Differentiable Sparsity at Million Scale GreedyFool: Distortion-Aware Sparse Adversarial Attack

Reference 37

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local_arxiv, observed 2026-08-08T00:53:25.171243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.611189Z digest=sha256:7b47329561e3003b8e26af8175a29fcbfbdd73d88e055e5241c900376128d52f

Observation de56c6dc-37b5-49b8-87e9-1e6ab91ecc05 · outbound

This paper cites Natural Evolution Strategies , journal =.

LaPrune: Controllable Differentiable Sparsity at Million Scale Natural Evolution Strategies , journal =

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:53:25.564788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.615510Z digest=sha256:bf81892376406f0379e67dd325df19166d7cfb5fd2ce142f8520e4815e5a5fcf

Observation e67f5529-e342-4bab-92bb-2a1b2db419f1 · outbound

This paper cites Black-box Adversarial Attacks with Limited Queries and Information.

LaPrune: Controllable Differentiable Sparsity at Million Scale Black-box Adversarial Attacks with Limited Queries and Information

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-08T00:53:24.620261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T00:53:24.620261Z digest=sha256:1444b4c195169a7028c7e62dd2520d64acd87feecc410a6558c06e95b7e97f77

Observation 55f72832-8324-4c64-a77d-e97b93a6b134 · outbound

This paper cites Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks.

LaPrune: Controllable Differentiable Sparsity at Million Scale Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Reference 40

Resolution
metadata mismatch
local_arxiv, observed 2026-08-08T00:53:25.136394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.624871Z digest=sha256:abecb7f3615b0956aa46eb67f0ae5579477f8c6911ccdbc84374fe0fc43b9c8f

Observation e3daab18-6875-47a0-ab6c-1ab170f5fb9e · outbound

This paper cites arXiv preprint arXiv:2212.07495 , year =.

LaPrune: Controllable Differentiable Sparsity at Million Scale arXiv preprint arXiv:2212.07495 , year =

Reference 41

Resolution
verified exact
raw_fallback, observed 2026-08-08T00:53:25.116059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.630111Z digest=sha256:b1b80f1370183f829bea7f1996cd923ca259f4feed1264b3dec20465e3f885e0

Observation a33b919d-807f-4653-b3f6-af1f0eb20a53 · outbound

This paper cites Structured Adversarial Attack: Towards General Implementation and Better Interpretability.

LaPrune: Controllable Differentiable Sparsity at Million Scale Structured Adversarial Attack: Towards General Implementation and Better Interpretability

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-08T00:53:25.036218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.634261Z digest=sha256:cd9f10e8fc67f6d0eec0881a424b8c1f2f7384c9eac72d5123ee135ba5bd6784

Observation 9cb226fb-7b5f-4f81-b452-eecce1b3398c · outbound

This paper cites ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders.

LaPrune: Controllable Differentiable Sparsity at Million Scale ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-08T00:53:24.638438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T00:53:24.638438Z digest=sha256:878fb13996e6f60e609a110356e697821c7f2dfdd8e8cfe8b79e54f0b377fbc2

Observation c11309c2-d0df-4701-bd42-cdef2fb2dd42 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , year =.

LaPrune: Controllable Differentiable Sparsity at Million Scale IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , year =

Reference 44

Resolution
metadata mismatch
raw_fallback, observed 2026-08-08T00:53:24.997787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.643197Z digest=sha256:d49832747303b172e7d36c02ab53951d5257bdd1fc532bb490c48a6131fa3450

Observation 8ee413ac-1d2d-4fd9-91eb-15ce9cb30ebe · outbound

This paper cites Sparse and Imperceivable Adversarial Attacks.

LaPrune: Controllable Differentiable Sparsity at Million Scale Sparse and Imperceivable Adversarial Attacks

Reference 45

Resolution
metadata mismatch
local_arxiv, observed 2026-08-08T00:53:24.903087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.647334Z digest=sha256:48ec1a30370efd834986f1041f27466b499078cc23b2f77591205541f759e430

Observation 2202254c-7bab-4f0b-b7c1-db46db775e6c · outbound

This paper cites -zero: Gradient-based Optimization of _0 -norm Adversarial Examples , booktitle =.

LaPrune: Controllable Differentiable Sparsity at Million Scale -zero: Gradient-based Optimization of _0 -norm Adversarial Examples , booktitle =

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:53:25.551822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.651937Z digest=sha256:03e199a97edbe78932bb3769dc8e2849ecdb08fa7fd6a28de8c1dc709238660b

Observation 958377a3-0d51-40b0-9364-06e8553024bb · outbound

This paper cites European Conference on Computer Vision (ECCV) , year =.

LaPrune: Controllable Differentiable Sparsity at Million Scale European Conference on Computer Vision (ECCV) , year =

Reference 47

Resolution
verified exact
doi, observed 2026-08-08T00:53:24.770592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.656062Z digest=sha256:dbad778a26d329e44741c2ba75b1e51f112b99b8d7818ba7e8f4a5275c71b185

Observation f7266874-c7ea-45bb-a1db-006e11173a8c · outbound

This paper cites , title =.

LaPrune: Controllable Differentiable Sparsity at Million Scale , title =

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:53:25.538714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.660602Z digest=sha256:355f4590dadc0a038850848bb6cd0664d7579b48ccf520536183fcfd4f54eff8

Observation 4ce281f1-57b4-48b9-a7a6-ee477f461481 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation , journal =.

LaPrune: Controllable Differentiable Sparsity at Million Scale Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation , journal =

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:53:25.525603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.665056Z digest=sha256:156fe8ffdae4b820eeff2ba80dcf2873d8889d77ae9f474d1cfcebd1b240d0fd

Observation bae3d3f4-895e-43ac-8cea-717c4c217f92 · outbound

This paper cites Neural Discrete Representation Learning.

LaPrune: Controllable Differentiable Sparsity at Million Scale Neural Discrete Representation Learning

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-08T00:53:24.670307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T00:53:24.670307Z digest=sha256:a34744c24ae92f78d191877a91e0e2ffc376fc6a7b5cb355472237bf11cba6d4

Observation 79bf830a-eb3d-4cfe-9559-0029263dca90 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

LaPrune: Controllable Differentiable Sparsity at Million Scale Categorical Reparameterization with Gumbel-Softmax

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-08T00:53:24.675390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T00:53:24.675390Z digest=sha256:2571082563cf5f1804e8a745cdde1889a78cb395b67508c7eb37ead8f8b0c3d9

Observation bbca1cbb-a9c4-4b97-9b2c-7d6278992435 · outbound

This paper cites The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

LaPrune: Controllable Differentiable Sparsity at Million Scale The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-08T00:53:24.680663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T00:53:24.680663Z digest=sha256:f329026afb448714dbff141cfc698e9f03ad30ab0638ecea1671450405e569e2

Observation 8d75fe2e-d1e4-48a3-aa39-6847990a3c06 · outbound

This paper cites , title =.

LaPrune: Controllable Differentiable Sparsity at Million Scale , title =

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-08T00:53:24.684858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T00:53:24.684858Z digest=sha256:b1568ff80811d5b9758c50b4b654461b6b694186f364d294f58fe484ed6b8d14

Observation aa3a13da-88e2-4078-99aa-135a80a4d9d2 · outbound

This paper cites Evolution Strategies as a Scalable Alternative to Reinforcement Learning.

LaPrune: Controllable Differentiable Sparsity at Million Scale Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-08T00:53:24.689106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T00:53:24.689106Z digest=sha256:f0b0c5fc6929fac47a48eb0eb4bbce16553ce6843d4a4b5c9cfad2fd9edb02e5

Observation 8d04e957-16fd-4a5a-96d4-fcf0131ddc0d · outbound

This paper cites Adam: A Method for Stochastic Optimization.

LaPrune: Controllable Differentiable Sparsity at Million Scale Adam: A Method for Stochastic Optimization

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-08T00:53:24.693858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T00:53:24.693858Z digest=sha256:2218bac8cedb521c411f1094862fe99bdf66ed5e93ac4206fe8a84a634240d90

Observation bea4b686-c535-4bbc-9905-93d577cd9dd1 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence , year=.

LaPrune: Controllable Differentiable Sparsity at Million Scale IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:53:25.504166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.698820Z digest=sha256:371c15354f05e7fb6138ebd55de87a77817af52159bc0097da486491f5de544a

Observation 510921f9-8f5e-4405-8a4d-1e769d851599 · outbound

This paper cites Reparameterizable Subset Sampling via Continuous Relaxations.

LaPrune: Controllable Differentiable Sparsity at Million Scale Reparameterizable Subset Sampling via Continuous Relaxations

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-08T00:53:24.703140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T00:53:24.703140Z digest=sha256:b8a2d07aecefeef46b9c81b0f667393db0fa0c1596488ae185f3d6e4660fd742

Observation 203cb793-b688-44dd-96ec-d515e7ca05d2 · outbound

This paper cites International Conference on Machine Learning , pages=.

LaPrune: Controllable Differentiable Sparsity at Million Scale International Conference on Machine Learning , pages=

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:53:25.490897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.707839Z digest=sha256:e8e81096dcff9f4c9c851faeccf4ad4de9d2b234555fe870bb2688e3cbbbf73a

Observation b4359e7e-3185-4eb0-b2a1-b145607e6ef4 · outbound

This paper cites International conference on machine learning , pages=.

LaPrune: Controllable Differentiable Sparsity at Million Scale International conference on machine learning , pages=

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-08T00:53:24.713932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T00:53:24.713932Z digest=sha256:d629ea65a1666c711f269cf003f91bcc4bb4c6821ae592a24347bc363e9cd31a

Observation 8cb3d756-3d4a-4764-9dfb-2c5e06fcc212 · outbound

This paper cites Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=.

LaPrune: Controllable Differentiable Sparsity at Million Scale Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , pages=

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:53:25.469595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.719174Z digest=sha256:b820146ef11cf1950fbdbe50ce9d50474a23b29d20b0087a72d7526306091dd8

Observation a856cfcb-bbcb-4421-ac09-563bd2a43477 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

LaPrune: Controllable Differentiable Sparsity at Million Scale Advances in Neural Information Processing Systems , volume=

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:53:25.456525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.723963Z digest=sha256:a03830c6327914368e2895b7d92eae34dd9a5f3585ce71fb6fbb8ad1627f7594

Observation 5549e875-12c1-45ef-adf9-733c6506def8 · outbound

This paper cites BatchTopK Sparse Autoencoders.

LaPrune: Controllable Differentiable Sparsity at Million Scale BatchTopK Sparse Autoencoders

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-08T00:53:24.728224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T00:53:24.728224Z digest=sha256:e5517ca3b3878d6b501ab60ce3745de8651fe0ebf9e824b038e3fa880fae1807

Observation 29db1847-438d-485d-8e41-ae677543261a · outbound

This paper cites 2018 , eprint=.

LaPrune: Controllable Differentiable Sparsity at Million Scale 2018 , eprint=

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T00:53:25.443264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-08T00:53:24.732436Z digest=sha256:92e2c1ce59d6b469c0682614f5f0a58bd24cadffe023e95ecee173741a2139ef

Pith citing papers

No inbound Pith citation observations are available.