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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:aba4bf3734b3d33ac4ec964cb398e08b1bdaeb174b332d816efa3e9d0676ee91

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:a4a949fea69d1ac00d411baff28374b3fde21877bc51210b2c289a639c80f101

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:d8b82f351a7d193a9a04ced037b78bf8c70757de21d7e1d7b7f3b028af4cddcb

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=sha256:1ec0ac21a857ecb6106b57c1397238c3818c0562955449476b8daf07854a09c0

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=sha256:0bc41e3899de0615b7eab56d4c345245762c03397a49b8d82a3723f9e7b3d048

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:e7a4e23d8b739056725aa711636da5aeb47236754051159e50b8abd454d8f7c0

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=sha256:5562bf89e34ab452d53f478e5c2c76cbf36e0cb7708d851d86df8f686f33186b

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:ee8d02940ea4f413e3b6459165e2346527d973343512afcd8d981854267a4e84

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=sha256:567b5690596c8cdcd98ac2c5691fc6063138aecbcc74fd1828da1117842fcda8

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=sha256:2d9a7e565065a392d590aa859dd67961aa74b41e9fbff1664d4d2b5012ebddde

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=sha256:07695140b3cb77c9faaf86385cd6280d426fc07de8e0c4854c970ca0a9beba78

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=sha256:4f67a59dab10609312f60ef4f5f3c7e456e32ff3661bb4c66a410cf218a7a79b

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:ba6ea41d081ac621e2be47d17f3bbe84e27a548bd693ba1620cb99d481814025

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:b26308832fe43bc539d0b08f4f12783310626226599aebc64c6e2be1c2d8757b

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:ad6e7022bee15b4184c99367b0900b7a166942edb53a5a89e0fbe059e013d755

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=sha256:8015484c5457a5c099b048906793b413780bdc6ab9634d5a20a891bfd61d8bca

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=sha256:7c3ee68143e878c32f72998c2a78ae6c441d2522721eade9d33b2a5dd2cf6711

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=sha256:1ba7408dd061df9b3a1a9ccfb4b9800e4b336330263b4f37073927be84ac0791

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=sha256:44480f850b86b0268c38df6b78909be51fffe4730b4a653b90a65d2cc9edc8c1

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:effe76bb35a1470c9cb76efce6c373d7757610a3248357a2ecd3a9dd67877ae3

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=sha256:77da6164a7af4731ba574069b05bf0abfb4004498f4a67f513553ab22bc54418

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:c1442aa30d1efa6087fd945449bd847291feec0748d0fed6014e51825cde7bab

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:eefd00a6d0b22e1f0b44207fac473fb9f8b08fdea9985ffa7c14d5022ffa3c21

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=sha256:42b61e9d0a75b4eeb5646dba84c32b90927a6848d9746dfa8dd2f035878a3781

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:e250bb4263f3ffd59c4a91dd038dc030655cc345f3d9dd3cab07260d569fdcb9

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:c6d9e6cd9a0e12482fa053b658c5b39cdd72b9d6abc936f43e6b713cacf57dc5

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=sha256:637f71bd1b6da58d26c9a17b5d17ed7c4865cb7d08dfde9a84e8077e453bde4c

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=sha256:87077f7985b8b093bc4d06622186caf427c4df8365590c5f00f361ccc06e1afe

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=sha256:03c3edb50b2f48f11d59b2f55b2b9bf485fca5b37248d48c52d19659303670e6

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:e60226a475bcea95e83d8c1dec103b8adeb2b14f4f5ead0e12590f42b7619ff8

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=sha256:51c9979fe7f291fb083189912cfe1a29073140b5f3546b82b23ac99125e3f6be

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=sha256:7cd84f77c4d1c6804d720747252b58317a3b0068416e1bbcc25cfdc89af56c00

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:f659ed3fdbc350dfe959b1e985b719501f6d7ac63e5d24464a2630bc46c1d814

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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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=2026-08-08T00:53:24.598131Z digest=sha256:35424b90aa024be53f05f03fa48405bbb7b6e2af0662a45f7faa3f04a3cc8c82

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=sha256:9cd1e2399865c2f93a7a7f5652d87c35b8a961cc9407697a0ca7b090ccda12a8

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:af99dec7acee473a8610024be3e9353dcd2f1c21cb905dcf68c92e0cf02daecc

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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metadata mismatch
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:ee7772d8862a613e1692234b73916301c17e6f829943d5cb720d3fa45ece21e2

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:b583d4c7b0be1d1c0889bfa1024284b87bebb9af3d41bc7e6f4b81413f3275b0

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:ffefdbb5a5b7e760091c039026005c5c3913d24a955d3fe9117617a45db7d5d5

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:b39c045f5e10ce219dde45c355e7e67c682be267305e5b36f48c457da571d263

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=sha256:9766166f8a34e70dda1290d6732af03522e02d8a034af1dec1f6119007b82ea1

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=sha256:0485684b8dbd2609bf9c5cc1c1e69c5a4f4d38240e0d89f0f4213ea729b174d8

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=sha256:22a03ab50091bf2f4bd8727eea14fa21bbf5d015935c5dbf1284c41c5e2aca19

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:f5ecda7b53cf6b1953633f245e47b7a6d1a95f6c00ac4b29e0158cce7eb3e593

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=sha256:70328c00491333d1af9ae09e13fedf52f0bc3521860a999e698a7caa8f043dd5

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=sha256:2aa34af521d8c501f6a192e1e0d991d2de4af5ba11afe3ebe808fe7d2eda0752

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=sha256:7c5955e9fda8e1dd86b8c99f2f333006de4eb08c4d17782e5457005c4cc790ed

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:d660bd7698eafe3983e1bfbc04bc31a32c8e8799961891788ccb12caa0b02bb0

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=sha256:14c06b19c2b17964d1490c6841d5bb3670efd0d843b9cabd4cc5dc29365e247d

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=sha256:14ef32df0ed0a3833da78decea1f1b5246e61b574b91491d8b6964c085ccd9b0

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:d056500e29ccd5d23fbd4af6b6568b4a19143f0c02aba67baca6089e4d6c834a

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=sha256:920d7603c6c663a5ab0013ade622b666acc42bbf2f4e60d6db165895a2437bb7

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:d02d2171df0b45a7110ebfbf3bd24f4f1f352012a8ebabcda6cf337b76704fe1

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=sha256:8125371dca41d54ec738eefd9407a5dc0b2dba94110715b20af57168e2e68f61

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=sha256:285f203c504f27ca82dcef0765549e92edd9dee043ecffd312198fbe325bd523

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=sha256:83be072b5a1158c78f0a42a5782ccb2889ba8e67983a3529df83ba7bea44e738

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=sha256:7f114648cc20a42b83c64c67505386cdbd0925efc4d12a82f21dd59c4d68f3f1

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=sha256:4c356c717416d3cb5d0d62d55921e657d28d85e6e0f645836879baf66af523e4

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=sha256:59b487b65f35f19e3e951c29f2eb626d0371dcfaacc2128a4199f0ce4a662f82

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=sha256:488a0cf1066a74dbf3d2dab7cf5416dcef23fe2486701db744aa92a0393ed812

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:f61c4c3b1591b73f56b942acca45d554c470bdb7d0a2627064455253cfeeede5

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=sha256:1acf7f8c15c6f5333144046708e86f22f5487c57a2559d8f301214d1e822495d

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=sha256:2b18ea5722d25e689e5a76cfe3f2a0fcc52eaf57c29cf9bbdf915068ec374de8

Pith citing papers

No inbound Pith citation observations are available.