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ORCIDPiotr Nawrot
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2020 – today
- 2024
[c4]Piotr Nawrot, Adrian Lancucki, Marcin Chochowski, David Tarjan, Edoardo M. Ponti:
Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference. ICML 2024- [i5]Piotr Nawrot, Adrian Lancucki, Marcin Chochowski, David Tarjan, Edoardo M. Ponti:
Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference. CoRR abs/2403.09636 (2024) - 2023
- [c3]Piotr Nawrot, Jan Chorowski, Adrian Lancucki, Edoardo Maria Ponti:
Efficient Transformers with Dynamic Token Pooling. ACL (1) 2023: 6403-6417 - [c2]Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner:
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models. NeurIPS 2023 - [i4]Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini
, Matt J. Kusner:
No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models. CoRR abs/2307.06440 (2023) - [i3]Piotr Nawrot:
nanoT5: A PyTorch Framework for Pre-training and Fine-tuning T5-style Models with Limited Resources. CoRR abs/2309.02373 (2023) - 2022
- [c1]Piotr Nawrot, Szymon Tworkowski, Michal Tyrolski, Lukasz Kaiser, Yuhuai Wu, Christian Szegedy, Henryk Michalewski:
Hierarchical Transformers Are More Efficient Language Models. NAACL-HLT (Findings) 2022: 1559-1571 - [i2]Piotr Nawrot, Jan Chorowski, Adrian Lancucki, Edoardo M. Ponti:
Efficient Transformers with Dynamic Token Pooling. CoRR abs/2211.09761 (2022) - 2021
- [i1]Piotr Nawrot, Szymon Tworkowski, Michal Tyrolski, Lukasz Kaiser, Yuhuai Wu, Christian Szegedy, Henryk Michalewski:
Hierarchical Transformers Are More Efficient Language Models. CoRR abs/2110.13711 (2021)
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