Elias Frantar - RankMe

43 papers A* 15B 1Journal 24Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Compression Scaling Laws:Unifying Sparsity and Quantization. CoRR Elias Frantar, Utku Evci, Wonpyo Park, Neil Houlsby, Dan Alistarh
2025	B	`conf`	MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models. PPoPP Elias Frantar, Roberto L. Castro, Jiale Chen, Torsten Hoefler, Dan Alistarh
2025	J	`jnl`	TACO Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression. Trans. Mach. Learn. Res. Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh
2024	J	`jnl`	Accurate Neural Network Pruning Requires Rethinking Sparse Optimization. Trans. Mach. Learn. Res. Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh
2024	A*	`conf`	Error Feedback Can Accurately Compress Preconditioners. ICML Ionut-Vlad Modoranu, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, Dan Alistarh
2024	A*	`conf`	Extreme Compression of Large Language Models via Additive Quantization. ICML Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh
2024	J	`jnl`	Extreme Compression of Large Language Models via Additive Quantization. CoRR Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh
2024	—	`conf`	Jaxpruner: A Concise Library for Sparsity Research. CPAL Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault, Johan Samir Obando Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Woohyun Han, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart J. C. Bik, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann N. Dauphin, Gintare Karolina Dziugaite, Pablo Samuel Castro, Utku Evci
2024	—	`conf`	L-GreCo: Layerwise-adaptive Gradient Compression For Efficient Data-parallel Deep Learning. MLSys Ilia Markov, Kaveh Alim, Elias Frantar, Dan Alistarh
2024	J	`jnl`	MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models. CoRR Elias Frantar, Roberto L. Castro, Jiale Chen, Torsten Hoefler, Dan Alistarh
2024	—	`conf`	QMoE: Sub-1-Bit Compression of Trillion Parameter Models. MLSys Elias Frantar, Dan Alistarh
2024	A*	`conf`	QUIK: Towards End-to-end 4-Bit Inference on Generative Large Language Models. EMNLP Saleh Ashkboos, Ilia Markov, Elias Frantar, Tingxuan Zhong, Xincheng Wang, Jie Ren, Torsten Hoefler, Dan Alistarh
2024	A*	`conf`	SPADE: Sparsity-Guided Debugging for Deep Neural Networks. ICML Arshia Soltani Moakhar, Eugenia Iofinova, Elias Frantar, Dan Alistarh
2024	A*	`conf`	Scaling Laws for Sparsely-Connected Foundation Models. ICLR Elias Frantar, Carlos Riquelme Ruiz, Neil Houlsby, Dan Alistarh, Utku Evci
2024	A*	`conf`	SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. ICLR Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh
2023	J	`jnl`	Accurate Neural Network Pruning Requires Rethinking Sparse Optimization. CoRR Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh
2023	A*	`conf`	CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models. NeurIPS Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh
2023	J	`jnl`	JaxPruner: A concise library for sparsity research. CoRR Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault, Johan S. Obando-Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart J. C. Bik, Woohyun Han, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann N. Dauphin, Karolina Dziugaite, Pablo Samuel Castro, Utku Evci
2023	A*	`conf`	OPTQ: Accurate Quantization for Generative Pre-trained Transformers. ICLR Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh
2023	J	`jnl`	QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models. CoRR Tommaso Pegolotti, Elias Frantar, Dan Alistarh, Markus Püschel
2023	J	`jnl`	QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models. CoRR Elias Frantar, Dan Alistarh
2023	J	`jnl`	Scaling Laws for Sparsely-Connected Foundation Models. CoRR Elias Frantar, Carlos Riquelme, Neil Houlsby, Dan Alistarh, Utku Evci
2023	J	`jnl`	SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression. CoRR Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh
2023	J	`jnl`	Sparse Fine-tuning for Inference Acceleration of Large Language Models. CoRR Eldar Kurtic, Denis Kuznedelev, Elias Frantar, Michael Goin, Dan Alistarh
2023	J	`jnl`	SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot. CoRR Elias Frantar, Dan Alistarh
2023	A*	`conf`	SparseGPT: Massive Language Models Can be Accurately Pruned in One-Shot. ICML Elias Frantar, Dan Alistarh
2023	J	`jnl`	Towards End-to-end 4-Bit Inference on Generative Large Language Models. CoRR Saleh Ashkboos, Ilia Markov, Elias Frantar, Tingxuan Zhong, Xincheng Wang, Jie Ren, Torsten Hoefler, Dan Alistarh
2023	J	`jnl`	Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression. CoRR Denis Kuznedelev, Soroush Tabesh, Kimia Noorbakhsh, Elias Frantar, Sara Beery, Eldar Kurtic, Dan Alistarh
2023	J	`jnl`	ZipLM: Hardware-Aware Structured Pruning of Language Models. CoRR Eldar Kurtic, Elias Frantar, Dan Alistarh
2023	A*	`conf`	ZipLM: Inference-Aware Structured Pruning of Language Models. NeurIPS Eldar Kurtic, Elias Frantar, Dan Alistarh
2022	J	`jnl`	GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers. CoRR Elias Frantar, Saleh Ashkboos, Torsten Hoefler, Dan Alistarh
2022	J	`jnl`	L-GreCo: An Efficient and General Framework for Layerwise-Adaptive Gradient Compression. CoRR Mohammadreza Alimohammadi, Ilia Markov, Elias Frantar, Dan Alistarh
2022	A*	`conf`	Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning. NeurIPS Elias Frantar, Dan Alistarh
2022	J	`jnl`	Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning. CoRR Elias Frantar, Dan Alistarh
2022	A*	`conf`	SPDY: Accurate Pruning with Speedup Guarantees. ICML Elias Frantar, Dan Alistarh
2022	J	`jnl`	SPDY: Accurate Pruning with Speedup Guarantees. CoRR Elias Frantar, Dan Alistarh
2022	A*	`conf`	The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models. EMNLP Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh
2022	J	`jnl`	The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models. CoRR Eldar Kurtic, Daniel Campos, Tuan Nguyen, Elias Frantar, Mark Kurtz, Benjamin Fineran, Michael Goin, Dan Alistarh
2022	J	`jnl`	oViT: An Accurate Second-Order Pruning Framework for Vision Transformers. CoRR Denis Kuznedelev, Eldar Kurtic, Elias Frantar, Dan Alistarh
2021	J	`jnl`	Efficient Matrix-Free Approximations of Second-Order Information, with Applications to Pruning and Optimization. CoRR Elias Frantar, Eldar Kurtic, Dan Alistarh
2021	A*	`conf`	M-FAC: Efficient Matrix-Free Approximations of Second-Order Information. NeurIPS Elias Frantar, Eldar Kurtic, Dan Alistarh
2020	A*	`conf`	On the Sample Complexity of Adversarial Multi-Source PAC Learning. ICML Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph Lampert
2020	J	`jnl`	On the Sample Complexity of Adversarial Multi-Source PAC Learning. CoRR Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph H. Lampert