Alistarh, Dan

Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

Hoefler, T., Alistarh, D., Ben-Nun, T., Dryden, N., & Peste, A. (2021) · ETH Zurich

Advanced Neural Network ApplicationsMachine Learning and Data Classification

Cited by 423

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Frantar, E., Ashkboos, S., Hoefler, T., & Alistarh, D. (2022) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 197

SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Frantar, E., & Alistarh, D. (2023) · arXiv

Advanced Neural Network ApplicationsTopic Modeling

Cited by 131

FPGA-Accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-Off

Kara, K., Alistarh, D., Alonso, G., Mutlu, O., & Zhang, C. (2017) · 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)

Embedded Systems Design TechniquesNumerical Methods and Algorithms

Cited by 77

The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models

Kurtic, E., Campos, D., Nguyen, T., Frantar, E., Kurtz, M., Fineran, B., Goin, M., & Alistarh, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Advanced Neural Network ApplicationsTopic Modeling

Cited by 65

Taming unbalanced training workloads in deep learning with partial collective operations

Li, S., Ben-Nun, T., Girolamo, S. D., Alistarh, D., & Hoefler, T. (2020) · Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

Stochastic Gradient Optimization TechniquesParallel Computing and Optimization Techniques

Cited by 57

Distributed learning over unreliable networks

Yu, C., Tang, H., Renggli, C., Kassing, S., Singla, A., Alistarh, D., Zhang, C., & Liu, J. (2018) · arXiv

Stochastic Gradient Optimization TechniquesDistributed systems and fault tolerance

Cited by 45

How Well Do Sparse ImageNet Models Transfer?

Iofinova, E., Peste, A., Kurtz, M., & Alistarh, D. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 22