Paper Citation Record · LEDGER

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity

As of 19 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 1 inbound Pith citation observation for arXiv:2502.01330.

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

pith.paper-citation-record.v1
2502.01330 v2

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:48:57.430004Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T00:54:03.409547Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

64 of 64 outbound references displayed

verified exact5
verified fuzzy23
unresolved34
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation f0179e06-780c-4662-bd33-9d577d9e95c3 · outbound

This paper cites E., Heckel, K.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity E., Heckel, K

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.442601Z

Source-reported events for the cited work

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Observation 8cb134ca-8b5a-4a4e-88fa-93f35b558d5f · outbound

This paper cites an unresolved cited work.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-09T15:48:58.432839Z

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Observation 513408af-0fa0-476d-a902-a2fd9e6ab457 · outbound

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

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T15:48:57.223635Z

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Observation 8da1b2bf-0e51-4d1e-b0c3-204bfe5c634d · outbound

This paper cites Provable Benefits of Overparameterization in Model Compression : From Double Descent to Pruning Neural Networks.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Provable Benefits of Overparameterization in Model Compression : From Double Descent to Pruning Neural Networks

Reference 4

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Observation 6985f25f-987d-4605-9836-3ff1c979fbd8 · outbound

This paper cites The lottery ticket hypothesis for pre-trained bert networks.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity The lottery ticket hypothesis for pre-trained bert networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.424086Z

Source-reported events for the cited work

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Observation bb649095-b182-4c49-a6de-36fb7571a1be · outbound

This paper cites Language Modeling using LMUs: 10x Better Data Efficiency or Improved Scaling Compared to Transformers.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Language Modeling using LMUs: 10x Better Data Efficiency or Improved Scaling Compared to Transformers

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-09T15:48:58.136411Z

Source-reported events for the cited work

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Observation ce21c664-c69c-4969-a974-697464d76680 · outbound

This paper cites N., Fan, A., Auli, M., and Grangier, D.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity N., Fan, A., Auli, M., and Grangier, D

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.414093Z

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Observation 4c306970-4765-45da-b665-dc27dd840662 · outbound

This paper cites Icassp 2023 deep noise suppression challenge.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Icassp 2023 deep noise suppression challenge

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.405001Z

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Observation ad2e75c0-b03a-4116-a295-a128e3cc0704 · outbound

This paper cites S., and Elsen, E.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity S., and Elsen, E

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.394696Z

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Observation 34acaf37-4074-4195-a81e-543ad0cb4414 · outbound

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

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Reference 10

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Source-reported events for the cited work

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Observation 0e50bf11-1429-4996-b07f-cda8c8207ec2 · outbound

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

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T15:48:57.254180Z

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Observation b19fa976-c65a-4390-8afd-0e3f0d948751 · outbound

This paper cites A Survey of Quantization Methods for Efficient Neural Network Inference.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity A Survey of Quantization Methods for Efficient Neural Network Inference

Reference 12

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Observation 779cad8a-12bd-4834-bdda-3b29d50f1877 · outbound

This paper cites It's raw! audio generation with state-space models.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity It's raw! audio generation with state-space models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.384388Z

Source-reported events for the cited work

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Observation 777f8b5a-06d3-4f9f-bd55-7910ceab491d · outbound

This paper cites Are wider nets better given the same number of parameters? October 2021.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Are wider nets better given the same number of parameters? October 2021

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.373921Z

Source-reported events for the cited work

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Observation 99490e59-a7cb-4c66-abc3-b773c8e6325a · outbound

This paper cites Aqt: Accurate quantized training.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Aqt: Accurate quantized training

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.361979Z

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Observation 04ccc0ed-a9eb-4397-853d-fbf832befd69 · outbound

This paper cites Foundations of time-frequency analysis.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Foundations of time-frequency analysis

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.351510Z

Source-reported events for the cited work

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Observation f2360e0c-9836-42de-bf0b-040b140368af · outbound

This paper cites Hippo: Recurrent memory with optimal polynomial projections.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Hippo: Recurrent memory with optimal polynomial projections

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.341199Z

Source-reported events for the cited work

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Observation e1d0b250-f4d3-4ed3-a7a1-4f435a690087 · outbound

This paper cites On the parameterization and initialization of diagonal state space models.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity On the parameterization and initialization of diagonal state space models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.330173Z

Source-reported events for the cited work

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Observation 6615aafc-7d4e-4646-b291-9734b14af9fe · outbound

This paper cites Efficiently modeling long sequences with structured state spaces.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Efficiently modeling long sequences with structured state spaces

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.319420Z

Source-reported events for the cited work

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Observation 250e01f8-c4ea-4cf2-acbd-366e84f12f2a · outbound

This paper cites Learning both weights and connections for efficient neural network.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Learning both weights and connections for efficient neural network

Reference 20

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Source-reported events for the cited work

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Observation 88e91248-374c-4765-a66d-f74ac9bbc193 · outbound

This paper cites C., and Wu, J.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity C., and Wu, J

Reference 21

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Observation 20d1d562-414c-4b8f-a86a-0232cca3a72f · outbound

This paper cites Mixture of A Million Experts.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Mixture of A Million Experts

Reference 22

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Observation bf29fbed-8602-4ea7-94f1-58013e8ba16b · outbound

This paper cites Mixture of A Million Experts.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Mixture of A Million Experts

Reference 23

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Observation 6891cc60-24d0-478b-9d3b-f6e3abcaefb5 · outbound

This paper cites Quantized Neural Networks : Training Neural Networks with Low Precision Weights and Activations.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Quantized Neural Networks : Training Neural Networks with Low Precision Weights and Activations

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.302373Z

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Observation 0495ef66-ac52-4368-8bcb-20f2bbfd8e87 · outbound

This paper cites Approximate Top-$k$ for Increased Parallelism.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Approximate Top-$k$ for Increased Parallelism

Reference 25

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Observation d5fd1e81-de28-4b72-ad28-8549d339aaa0 · outbound

This paper cites M., Pandit, T., Merkel, C., Kubendran, R., Aimone, J.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity M., Pandit, T., Merkel, C., Kubendran, R., Aimone, J

Reference 26

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Observation cb830f64-f68c-4883-afb0-0b7fa8dcabf6 · outbound

This paper cites JaxPruner: A concise library for sparsity research.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity JaxPruner: A concise library for sparsity research

Reference 27

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Observation 5ae69329-8856-4ca2-9149-a467e4040d1e · outbound

This paper cites On the quantization of recurrent neural networks.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity On the quantization of recurrent neural networks

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-09T15:48:58.007442Z

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Observation 31c80437-367a-46fd-be60-5a9630d4103d · outbound

This paper cites Cerebras architecture deep dive: First look inside the hardware/software co-design for deep learning.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Cerebras architecture deep dive: First look inside the hardware/software co-design for deep learning

Reference 29

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Source-reported events for the cited work

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Observation 7218a094-2520-4ad9-96dc-131212458579 · outbound

This paper cites Ten Lessons We Have Learned in the New "Sparseland": A Short Handbook for Sparse Neural Network Researchers.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Ten Lessons We Have Learned in the New "Sparseland": A Short Handbook for Sparse Neural Network Researchers

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-09T15:48:57.498863Z

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Observation 027a51a2-d6e2-4756-9555-f0563787fc17 · outbound

This paper cites N., Singh, S., and Behbahani, F.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity N., Singh, S., and Behbahani, F

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.291877Z

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Observation f152769f-c3cc-41eb-8628-94f2f04d3955 · outbound

This paper cites SpiNNaker 2: A 10 Million Core Processor System for Brain Simulation and Machine Learning.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity SpiNNaker 2: A 10 Million Core Processor System for Brain Simulation and Machine Learning

Reference 32

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Observation 10b17624-8b43-485a-8e0e-314aa5fd274d · outbound

This paper cites A Diagonal Structured State Space Model on Loihi 2 for Efficient Streaming Sequence Processing.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity A Diagonal Structured State Space Model on Loihi 2 for Efficient Streaming Sequence Processing

Reference 33

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Observation e4b6abd6-f8e4-4113-a173-3f889f1a7855 · outbound

This paper cites I., Alizadeh-Vahid, K., Mehta, S., del Mundo, C.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity I., Alizadeh-Vahid, K., Mehta, S., del Mundo, C

Reference 34

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Observation 77891f43-44dd-47b7-8dbf-b41a509b973b · outbound

This paper cites Accelerating Sparse Deep Neural Networks.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Accelerating Sparse Deep Neural Networks

Reference 35

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Observation dbe9961e-01c9-4835-b41a-04c5a2484c8f · outbound

This paper cites C., Mocanu, E., Stone, P., Nguyen, P.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity C., Mocanu, E., Stone, P., Nguyen, P

Reference 36

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Observation 5a33b656-354b-423d-9165-9809089ffcb2 · outbound

This paper cites S., Akopyan, F., Andreopoulos, A., Appuswamy, R., Arthur, J.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity S., Akopyan, F., Andreopoulos, A., Appuswamy, R., Arthur, J

Reference 37

Resolution: verified exact; doi, observed 2026-08-09T15:48:58.275659Z

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Observation 918acf82-4e64-4c48-a3ae-86d587d43531 · outbound

This paper cites Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Weight Sparsity Complements Activity Sparsity in Neuromorphic Language Models

Reference 38

Resolution: metadata mismatch; local_arxiv, observed 2026-08-09T15:48:57.867395Z

Source-reported events for the cited work

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Observation 4dc50cf4-9827-46dc-9396-255494c2fe2e · outbound

This paper cites W., Wornow, M., Birch - Sykes, C., Massaroli, S., Patel, A., Rabideau, C.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity W., Wornow, M., Birch - Sykes, C., Massaroli, S., Patel, A., Rabideau, C

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.266675Z

Source-reported events for the cited work

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Observation fbe6d083-ff24-43b6-baf8-ac4bc7041555 · outbound

This paper cites Sigma Delta Quantized Networks.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Sigma Delta Quantized Networks

Reference 40

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Observation c680c522-0ef4-4ab7-a356-ead444df8ffd · outbound

This paper cites P., Rubin, D.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity P., Rubin, D

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-09T15:48:57.355624Z

Source-reported events for the cited work

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Observation e52fc593-ff8c-40ff-b0bd-5c2790fbef20 · outbound

This paper cites an unresolved cited work.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-09T15:48:58.257661Z

Source-reported events for the cited work

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Observation e52d351f-8c9b-4758-95ed-52781becc974 · outbound

This paper cites Towards artificial general intelligence with hybrid tianjic chip architecture.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Towards artificial general intelligence with hybrid tianjic chip architecture

Reference 43

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Observation 830b7d24-cbba-4e59-8e96-14a696c0c9b1 · outbound

This paper cites and Abreu, S.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity and Abreu, S

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.249073Z

Source-reported events for the cited work

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Observation d462d34e-9636-45f1-b498-f99533df0250 · outbound

This paper cites W., Nguyen, E., Ponnusamy, P., Deiseroth, B., Kersting, K., Suzuki, T., Hie, B., Ermon, S., R \' e , C., Zhang, C., and Massaroli, S.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity W., Nguyen, E., Ponnusamy, P., Deiseroth, B., Kersting, K., Suzuki, T., Hie, B., Ermon, S., R \' e , C., Zhang, C., and Massaroli, S

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.239549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9bc82661-dec8-4cca-99e1-201ba9c2f3e0 · outbound

This paper cites The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-09T15:48:57.372116Z

Source-reported events for the cited work

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Observation cb1b400f-b178-4fe0-8c95-efaba27db585 · outbound

This paper cites K., Dubey, H., Gopal, V., Cutler, R., Braun, S., Gamper, H., Aichner, R., and Srinivasan, S.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity K., Dubey, H., Gopal, V., Cutler, R., Braun, S., Gamper, H., Aichner, R., and Srinivasan, S

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.228558Z

Source-reported events for the cited work

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Observation da9fe687-06e8-4c3e-ab4b-de0b4991d263 · outbound

This paper cites Interspeech 2021 Deep Noise Suppression Challenge.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Interspeech 2021 Deep Noise Suppression Challenge

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-09T15:48:57.378594Z

Source-reported events for the cited work

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Observation 273b1061-f817-453b-bdec-47c13e36c41d · outbound

This paper cites Comparing Rewinding and Fine-tuning in Neural Network Pruning.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Comparing Rewinding and Fine-tuning in Neural Network Pruning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-09T15:48:57.381876Z

Source-reported events for the cited work

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Observation 9ef26d84-3ad1-4123-a781-15eebaae1484 · outbound

This paper cites V., Hinton, G.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity V., Hinton, G

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.218408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4e30e604-ce3f-497b-97f5-cf6d3e9464a8 · outbound

This paper cites B., Timcheck, J., Frady, P., Campos-Macias, L., and Davies, M.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity B., Timcheck, J., Frady, P., Campos-Macias, L., and Davies, M

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.207850Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity B., Timcheck, J., Frady, P., Campos-Macias, L., and Davies, M

Reference 52

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Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Unresolved cited work

Reference 53

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Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 54

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This paper cites Long range arena : A benchmark for efficient transformers.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Long range arena : A benchmark for efficient transformers

Reference 55

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This paper cites B., Ben Dayan Rubin, D., Kupryjanow, A., Orchard, G., Pindor, L., Shea, T., and Davies, M.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity B., Ben Dayan Rubin, D., Kupryjanow, A., Orchard, G., Pindor, L., Shea, T., and Davies, M

Reference 56

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Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity N., Kaiser, L., and Polosukhin, I

Reference 57

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This paper cites Legendre memory units: Continuous-time representation in recurrent neural networks.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Legendre memory units: Continuous-time representation in recurrent neural networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.169009Z

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Observation 14317d74-d675-430a-a459-77aec8894e6d · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-09T15:48:57.412494Z

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Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 60

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Observation ef13388c-19c0-495b-99c5-78fbdded0a90 · outbound

This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 61

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Observation 80a9ba36-3f3a-45ba-8ce0-60825729395b · outbound

This paper cites S., Keckler, S.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity S., Keckler, S

Reference 62

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Observation 2f34fcf1-2e4b-4e23-b20d-f0b3a8f928a1 · outbound

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Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-09T15:48:57.426567Z

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Observation d8a5a404-488f-4e3c-853f-eee863a04536 · outbound

This paper cites and Gupta, S.

Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity and Gupta, S

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T15:48:58.158108Z

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Pith citing papers

Observation 987dba4a-0b49-4dae-8ae1-8366e10e068b · inbound

Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation cites this paper.

Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity

Reference 26

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.