LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization | Paper Citation Record

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This paper cites IEEE Standard for SystemVerilog–Unified Hardware Design, Specification, and Verification Language.IEEE Std 1800-2023 (Revision of IEEE Std 1800-2017) (2024), 1–1354.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization IEEE Standard for SystemVerilog–Unified Hardware Design, Specification, and Verification Language.IEEE Std 1800-2023 (Revision of IEEE Std 1800-2017) (2024), 1–1354

Reference 1

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Observation 453da45d-2503-4eaf-b3c8-e97da523eaf4 · outbound

This paper cites Ballard, Joshua Bambrick, Sebastian W.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Ballard, Joshua Bambrick, Sebastian W

Reference 2

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This paper cites O’Donnell, Daniel Berenberg, Ian Fisk, Niccolò Zanichelli, Bo Zhang, Arkadiusz Nowaczynski, Bei Wang, Marta M.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization O’Donnell, Daniel Berenberg, Ian Fisk, Niccolò Zanichelli, Bo Zhang, Arkadiusz Nowaczynski, Bei Wang, Marta M

Reference 3

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This paper cites Croci, Bo Li, Pash- mina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Croci, Bo Li, Pash- mina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:43.254778Z

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Observation 5b738e3a-b255-4760-bbac-bb9b7e493e86 · outbound

This paper cites Kinch, R.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Kinch, R

Reference 5

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This paper cites an unresolved cited work.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:59:43.239846Z

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This paper cites an unresolved cited work.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:59:43.232609Z

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This paper cites an unresolved cited work.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 8

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 9

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 10

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 11

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 12

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 13

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 14

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 15

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 16

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 17

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 18

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 19

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 20

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Observation b7213f0f-7f88-478d-ad3e-952ef3b273f0 · outbound

This paper cites Fallon, Vikram V.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Fallon, Vikram V

Reference 21

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 22

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 23

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 24

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 25

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This paper cites Oh, Yeonhong Park, Yoonho Song, Jung-Hun Park, Sanghee Lee, Kyoung Park, Jae W.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Oh, Yeonhong Park, Yoonho Song, Jung-Hun Park, Sanghee Lee, Kyoung Park, Jae W

Reference 26

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Reference 27

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This paper cites BiLLM: Pushing the Limit of Post-Training Quantization for LLMs.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 28

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Reference 29

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 30

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 31

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 32

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 33

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 34

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This paper cites Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization

Reference 35

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Reference 36

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Reference 37

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving

Reference 38

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Reference 39

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization AffineQuant: Affine Transformation Quantization for Large Language Models

Reference 40

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Reference 41

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Large Language Models: A Survey

Reference 42

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Reference 43

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization A White Paper on Neural Network Quantization

Reference 44

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Reference 45

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Reference 46

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Reference 47

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Reference 48

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization An Introduction to Convolutional Neural Networks

Reference 49

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Corey, and H

Reference 50

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This paper cites Exploring Post-Training Quantization of Protein Language Models.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Exploring Post-Training Quantization of Protein Language Models

Reference 51

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Self-attention Does Not Need $O(n^2)$ Memory

Reference 52

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This paper cites Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W.

LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W

Reference 53

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Observation 9d31ee20-efb4-4844-bc32-445378d575f7 · outbound

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Unresolved cited work

Reference 54

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization LLaMA: Open and Efficient Foundation Language Models

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization SOFA: A Compute-Memory Optimized Sparsity Accelerator via Cross-Stage Coordinated Tiling

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Reference 70

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization https://doi.org/10.1145/3649329.3657326

Reference 74

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

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LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization In2024 ACM/IEEE 51st Annual International Symposium on Computer Architecture (ISCA)

Reference 2024

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Pith citing papers