Elias Frantar
15 papers · 2020–2024 · 4 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (4) π Cross-Pollinator (8) π Interdisciplinary Bridge π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (16)
πΊοΈ
Taxonomy Completionist
(16)
π€
Dynamic Duo
(15)
π
Triple Crown
π₯
Unstoppable
(5)
π
Century Club
(15)
β‘
Prolific Year
(6)
Conferences
ICML (6)
NIPS (4)
ICLR (3)
EMNLP (2)
Top co-authors
Keywords
model compression
(5)
second-order optimization
(2)
neural network compression
(2)
inference optimization
(2)
model quantization
(2)
model pruning
(2)
weight quantization
(2)
weight pruning
(2)
large language model
(2)
neural network pruning
(2)
knowledge distillation
(2)
dynamic programming
(1)
multi-source learning
(1)
neural network optimization
(1)
network pruning
(1)
adversarial learning
(1)
pac learning
(1)
second-order information
(1)
sample complexity
(1)
language model
(1)
Papers
SPADE: Sparsity-Guided Debugging for Deep Neural Networks
ICML 2024
QUIK: Towards End-to-end 4-Bit Inference on Generative Large Language Models
EMNLP 2024
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
ICLR 2024
Scaling Laws for Sparsely-Connected Foundation Models
ICLR 2024
Extreme Compression of Large Language Models via Additive Quantization
ICML 2024
Error Feedback Can Accurately Compress Preconditioners
ICML 2024
SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot
ICML 2023
CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models
NIPS 2023
OPTQ: Accurate Quantization for Generative Pre-trained Transformers
ICLR 2023
ZipLM: Inference-Aware Structured Pruning of Language Models
NIPS 2023
SPDY: Accurate Pruning with Speedup Guarantees
ICML 2022
Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning
NIPS 2022
The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models
EMNLP 2022
M-FAC: Efficient Matrix-Free Approximations of Second-Order Information
NIPS 2021
On the Sample Complexity of Adversarial Multi-Source PAC Learning
ICML 2020