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ORCIDJean Kaddour
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2020 – today
- 2024
[i17]Aryo Pradipta Gema, Joshua Ong Jun Leang, Giwon Hong, Alessio Devoto, Alberto Carlo Maria Mancino, Rohit Saxena, Xuanli He, Yu Zhao
, Xiaotang Du, Mohammad Reza Ghasemi Madani, Claire Barale, Robert McHardy, Joshua Harris, Jean Kaddour, Emile van Krieken, Pasquale Minervini:
Are We Done with MMLU? CoRR abs/2406.04127 (2024)- [i16]Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, Simon Brunner, Chen Gong, Thong Hoang, Armel Randy Zebaze, Xiaoheng Hong, Wen-Ding Li, Jean Kaddour, Ming Xu, Zhihan Zhang, Prateek Yadav, Naman Jain, Alex Gu, Zhoujun Cheng, Jiawei Liu, Qian Liu, Zijian Wang, David Lo, Binyuan Hui, Niklas Muennighoff, Daniel Fried, Xiaoning Du, Harm de Vries, Leandro von Werra:
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions. CoRR abs/2406.15877 (2024) - [i15]Georgy Tyukin, Gbètondji J.-S. Dovonon, Jean Kaddour, Pasquale Minervini:
Attention Is All You Need But You Don't Need All Of It For Inference of Large Language Models. CoRR abs/2407.15516 (2024) - 2023
- [c7]Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae:
DAG Learning on the Permutahedron. ICLR 2023 - [c6]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 - [c5]Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Joan Lasenby, Qi Liu:
Evaluating Self-Supervised Learning for Molecular Graph Embeddings. NeurIPS 2023 - [i14]Valentina Zantedeschi, Luca Franceschi, Jean Kaddour, Matt J. Kusner, Vlad Niculae:
DAG Learning on the Permutahedron. CoRR abs/2301.11898 (2023) - [i13]Aengus Lynch, Gbètondji J.-S. Dovonon, Jean Kaddour, Ricardo Silva:
Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases. CoRR abs/2303.05470 (2023) - [i12]Jean Kaddour:
The MiniPile Challenge for Data-Efficient Language Models. CoRR abs/2304.08442 (2023) - [i11]Yuwei Yin, Jean Kaddour, Xiang Zhang, Yixin Nie, Zhenguang Liu, Lingpeng Kong, Qi Liu:
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models. CoRR abs/2304.08821 (2023) - [i10]Sunny Sanyal, Jean Kaddour, Abhishek Kumar, Sujay Sanghavi:
Understanding the Effectiveness of Early Weight Averaging for Training Large Language Models. CoRR abs/2306.03241 (2023) - [i9]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) - [i8]Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, Robert McHardy:
Challenges and Applications of Large Language Models. CoRR abs/2307.10169 (2023) - [i7]Jean Kaddour, Qi Liu:
Text Data Augmentation in Low-Resource Settings via Fine-Tuning of Large Language Models. CoRR abs/2310.01119 (2023) - 2022
- [c4]Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner:
When Do Flat Minima Optimizers Work? NeurIPS 2022 - [i6]Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner:
Questions for Flat-Minima Optimization of Modern Neural Networks. CoRR abs/2202.00661 (2022) - [i5]Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Matt J. Kusner, Joan Lasenby, Qi Liu:
Evaluating Self-Supervised Learning for Molecular Graph Embeddings. CoRR abs/2206.08005 (2022) - [i4]Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva:
Causal Machine Learning: A Survey and Open Problems. CoRR abs/2206.15475 (2022) - [i3]Jean Kaddour:
Stop Wasting My Time! Saving Days of ImageNet and BERT Training with Latest Weight Averaging. CoRR abs/2209.14981 (2022) - 2021
- [c3]Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva:
Causal Effect Inference for Structured Treatments. NeurIPS 2021: 24841-24854 - [i2]Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva:
Graph Intervention Networks for Causal Effect Estimation. CoRR abs/2106.01939 (2021) - 2020
- [c2]Jean Kaddour, Steindór Sæmundsson, Marc Peter Deisenroth:
Probabilistic Active Meta-Learning. NeurIPS 2020 - [i1]Jean Kaddour, Steindór Sæmundsson, Marc Peter Deisenroth:
Probabilistic Active Meta-Learning. CoRR abs/2007.08949 (2020)
2010 – 2019
- 2018
- [c1]Christian Krupitzer, Martin Pfannemüller, Jean Kaddour, Christian Becker:
SATISFy: Towards a Self-Learning Analyzer for Time Series Forecasting in Self-Improving Systems. FAS*W@SASO/ICAC 2018: 182-189
Coauthor Index
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last updated on 2024-10-07 21:17 CEST by the dblp team
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