LaPrune: Controllable Differentiable Sparsity at Million Scale | Paper Citation Record

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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This paper cites Classification Problem Solving.

LaPrune: Controllable Differentiable Sparsity at Million Scale Classification Problem Solving

Reference 2

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This paper cites , title =.

LaPrune: Controllable Differentiable Sparsity at Million Scale , title =

Reference 3

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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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This paper cites Clancey and Glenn Rennels , abstract =.

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

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This paper cites and Rennels, Glenn R.

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

Reference 6

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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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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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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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This paper cites 2023 , eprint=.

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

Reference 10

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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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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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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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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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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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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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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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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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LaPrune: Controllable Differentiable Sparsity at Million Scale Unresolved cited work

Reference 19

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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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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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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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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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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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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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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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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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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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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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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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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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LaPrune: Controllable Differentiable Sparsity at Million Scale , title =

Reference 32

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LaPrune: Controllable Differentiable Sparsity at Million Scale , title =

Reference 33

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LaPrune: Controllable Differentiable Sparsity at Million Scale Unresolved cited work

Reference 34

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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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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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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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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

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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

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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

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

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

Reference 41

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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

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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

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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

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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

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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

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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

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LaPrune: Controllable Differentiable Sparsity at Million Scale , title =

Reference 48

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

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

Reference 49

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

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

Reference 50

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This paper cites Categorical Reparameterization with Gumbel-Softmax.

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

Reference 51

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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

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LaPrune: Controllable Differentiable Sparsity at Million Scale , title =

Reference 53

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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

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

Reference 55

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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

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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

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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 2026-08-09T06:31:02.800959+00:00.

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.

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 2026-08-09T06:31:02.800959+00:00.

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 2026-08-09T06:31:02.800959+00:00.

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.

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 2026-08-09T06:31:02.800959+00:00.

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Outbound references

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