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2020 – today
- 2023
[c41]Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, Behnam Neyshabur:
REPAIR: REnormalizing Permuted Activations for Interpolation Repair. ICLR 2023- [c40]Harsh Mehta, Ankit Gupta, Ashok Cutkosky, Behnam Neyshabur:
Long Range Language Modeling via Gated State Spaces. ICLR 2023 - 2022
- [j3]Anders Johan Andreassen, Yasaman Bahri, Behnam Neyshabur, Rebecca Roelofs:
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning. Trans. Mach. Learn. Res. 2022 (2022) - [c39]Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi:
Exploring the Limits of Large Scale Pre-training. ICLR 2022 - [c38]Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur:
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks. ICLR 2022 - [c37]Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi:
Leveraging unlabeled data to predict out-of-distribution performance. ICLR 2022 - [c36]Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George Edward Dahl, Zachary Nado, Orhan Firat:
A Loss Curvature Perspective on Training Instabilities of Deep Learning Models. ICLR 2022 - [c35]Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, Orhan Firat:
Data Scaling Laws in NMT: The Effect of Noise and Architecture. ICML 2022: 1466-1482 - [c34]Ibrahim M. Alabdulmohsin, Behnam Neyshabur, Xiaohua Zhai:
Revisiting Neural Scaling Laws in Language and Vision. NeurIPS 2022 - [c33]Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay V. Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur:
Exploring Length Generalization in Large Language Models. NeurIPS 2022 - [c32]DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur:
Block-Recurrent Transformers. NeurIPS 2022 - [c31]Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V. Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra:
Solving Quantitative Reasoning Problems with Language Models. NeurIPS 2022 - [i46]Saurabh Garg, Sivaraman Balakrishnan, Zachary C. Lipton, Behnam Neyshabur, Hanie Sedghi:
Leveraging Unlabeled Data to Predict Out-of-Distribution Performance. CoRR abs/2201.04234 (2022) - [i45]Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Maxim Krikun, Colin Cherry, Behnam Neyshabur, Orhan Firat:
Data Scaling Laws in NMT: The Effect of Noise and Architecture. CoRR abs/2202.01994 (2022) - [i44]DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur:
Block-Recurrent Transformers. CoRR abs/2203.07852 (2022) - [i43]Lukas Timpl, Rahim Entezari, Hanie Sedghi, Behnam Neyshabur, Olga Saukh:
Understanding the effect of sparsity on neural networks robustness. CoRR abs/2206.10915 (2022) - [i42]Harsh Mehta, Ankit Gupta, Ashok Cutkosky
, Behnam Neyshabur:
Long Range Language Modeling via Gated State Spaces. CoRR abs/2206.13947 (2022) - [i41]Aitor Lewkowycz, Anders Andreassen, David Dohan, Ethan Dyer, Henryk Michalewski, Vinay V. Ramasesh, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur, Guy Gur-Ari, Vedant Misra:
Solving Quantitative Reasoning Problems with Language Models. CoRR abs/2206.14858 (2022) - [i40]Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay V. Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur:
Exploring Length Generalization in Large Language Models. CoRR abs/2207.04901 (2022) - [i39]Ibrahim Alabdulmohsin, Behnam Neyshabur, Xiaohua Zhai:
Revisiting Neural Scaling Laws in Language and Vision. CoRR abs/2209.06640 (2022) - [i38]Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, Behnam Neyshabur:
REPAIR: REnormalizing Permuted Activations for Interpolation Repair. CoRR abs/2211.08403 (2022) - [i37]Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron C. Courville, Behnam Neyshabur, Hanie Sedghi:
Teaching Algorithmic Reasoning via In-context Learning. CoRR abs/2211.09066 (2022) - [i36]Amr Khalifa, Michael C. Mozer, Hanie Sedghi, Behnam Neyshabur, Ibrahim Alabdulmohsin:
Layer-Stack Temperature Scaling. CoRR abs/2211.10193 (2022) - [i35]Tolga Ergen, Behnam Neyshabur, Harsh Mehta:
Convexifying Transformers: Improving optimization and understanding of transformer networks. CoRR abs/2211.11052 (2022) - 2021
- [c30]Pierre Foret, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur:
Sharpness-aware Minimization for Efficiently Improving Generalization. ICLR 2021 - [c29]Anna Golubeva, Guy Gur-Ari, Behnam Neyshabur:
Are wider nets better given the same number of parameters? ICLR 2021 - [c28]Harsh Mehta, Ashok Cutkosky, Behnam Neyshabur:
Extreme Memorization via Scale of Initialization. ICLR 2021 - [c27]Vaishnavh Nagarajan, Anders Andreassen, Behnam Neyshabur:
Understanding the failure modes of out-of-distribution generalization. ICLR 2021 - [c26]Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi:
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers. ICLR 2021 - [c25]Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur:
When Do Curricula Work? ICLR 2021 - [c24]Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur:
Deep Learning Through the Lens of Example Difficulty. NeurIPS 2021: 10876-10889 - [i34]Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur:
Deep Learning Through the Lens of Example Difficulty. CoRR abs/2106.09647 (2021) - [i33]Anders Andreassen, Yasaman Bahri, Behnam Neyshabur, Rebecca Roelofs:
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning. CoRR abs/2106.15831 (2021) - [i32]Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi:
Exploring the Limits of Large Scale Pre-training. CoRR abs/2110.02095 (2021) - [i31]Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George E. Dahl, Zachary Nado, Orhan Firat:
A Loss Curvature Perspective on Training Instability in Deep Learning. CoRR abs/2110.04369 (2021) - [i30]Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur:
The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks. CoRR abs/2110.06296 (2021) - 2020
- [c23]Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi:
The intriguing role of module criticality in the generalization of deep networks. ICLR 2020 - [c22]Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio:
Fantastic Generalization Measures and Where to Find Them. ICLR 2020 - [c21]Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur:
Observational Overfitting in Reinforcement Learning. ICLR 2020 - [c20]Yiding Jiang, Parth Natekar, Manik Sharma, Sumukh K. Aithal, Dhruva Kashyap, Natarajan Subramanyam, Carlos Lassance, Daniel M. Roy, Gintare Karolina Dziugaite, Suriya Gunasekar, Isabelle Guyon, Pierre Foret, Scott Yak, Hossein Mobahi, Behnam Neyshabur, Samy Bengio:
Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning. NeurIPS (Competition and Demos) 2020: 170-190 - [c19]Behnam Neyshabur:
Towards Learning Convolutions from Scratch. NeurIPS 2020 - [c18]Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang:
What is being transferred in transfer learning? NeurIPS 2020 - [i29]Behnam Neyshabur:
Towards Learning Convolutions from Scratch. CoRR abs/2007.13657 (2020) - [i28]Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang:
What is being transferred in transfer learning? CoRR abs/2008.11687 (2020) - [i27]Harsh Mehta, Ashok Cutkosky, Behnam Neyshabur:
Extreme Memorization via Scale of Initialization. CoRR abs/2008.13363 (2020) - [i26]Pierre Foret, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur:
Sharpness-Aware Minimization for Efficiently Improving Generalization. CoRR abs/2010.01412 (2020) - [i25]Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi:
The Deep Bootstrap: Good Online Learners are Good Offline Generalizers. CoRR abs/2010.08127 (2020) - [i24]Anna Golubeva, Behnam Neyshabur, Guy Gur-Ari:
Are wider nets better given the same number of parameters? CoRR abs/2010.14495 (2020) - [i23]Vaishnavh Nagarajan, Anders Andreassen, Behnam Neyshabur:
Understanding the Failure Modes of Out-of-Distribution Generalization. CoRR abs/2010.15775 (2020) - [i22]Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur:
When Do Curricula Work? CoRR abs/2012.03107 (2020) - [i21]Yiding Jiang, Pierre Foret, Scott Yak, Daniel M. Roy, Hossein Mobahi, Gintare Karolina Dziugaite, Samy Bengio, Suriya Gunasekar, Isabelle Guyon, Behnam Neyshabur:
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning. CoRR abs/2012.07976 (2020)
2010 – 2019
- 2019
- [c17]Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro:
The role of over-parametrization in generalization of neural networks. ICLR (Poster) 2019 - [i20]Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi:
The intriguing role of module criticality in the generalization of deep networks. CoRR abs/1912.00528 (2019) - [i19]Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio:
Fantastic Generalization Measures and Where to Find Them. CoRR abs/1912.02178 (2019) - [i18]Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur:
Observational Overfitting in Reinforcement Learning. CoRR abs/1912.02975 (2019) - 2018
- [j2]Somaye Hashemifar, Behnam Neyshabur, Aly A. Khan, Jinbo Xu:
Predicting protein-protein interactions through sequence-based deep learning. Bioinform. 34(17): i802-i810 (2018) - [c16]Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro:
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. ICLR (Poster) 2018 - [c15]Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang:
Stronger Generalization Bounds for Deep Nets via a Compression Approach. ICML 2018: 254-263 - [c14]Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro:
Implicit Regularization in Matrix Factorization. ITA 2018: 1-10 - [i17]Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang:
Stronger generalization bounds for deep nets via a compression approach. CoRR abs/1802.05296 (2018) - [i16]Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro:
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks. CoRR abs/1805.12076 (2018) - 2017
- [c13]Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire:
Corralling a Band of Bandit Algorithms. COLT 2017: 12-38 - [c12]Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nati Srebro:
Exploring Generalization in Deep Learning. NIPS 2017: 5947-5956 - [c11]Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro:
Implicit Regularization in Matrix Factorization. NIPS 2017: 6151-6159 - [i15]Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro:
Geometry of Optimization and Implicit Regularization in Deep Learning. CoRR abs/1705.03071 (2017) - [i14]Behnam Neyshabur, Srinadh Bhojanapalli, Ayan Chakrabarti:
Stabilizing GAN Training with Multiple Random Projections. CoRR abs/1705.07831 (2017) - [i13]Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro:
Implicit Regularization in Matrix Factorization. CoRR abs/1705.09280 (2017) - [i12]Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro:
Exploring Generalization in Deep Learning. CoRR abs/1706.08947 (2017) - [i11]Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro:
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. CoRR abs/1707.09564 (2017) - [i10]Behnam Neyshabur:
Implicit Regularization in Deep Learning. CoRR abs/1709.01953 (2017) - 2016
- [c10]Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro:
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. NIPS 2016: 3477-3485 - [c9]Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro:
Global Optimality of Local Search for Low Rank Matrix Recovery. NIPS 2016: 3873-3881 - [c8]Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro:
Data-Dependent Path Normalization in Neural Networks. ICLR (Poster) 2016 - [i9]Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nathan Srebro:
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations. CoRR abs/1605.07154 (2016) - [i8]Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro:
Global Optimality of Local Search for Low Rank Matrix Recovery. CoRR abs/1605.07221 (2016) - [i7]Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire:
Corralling a Band of Bandit Algorithms. CoRR abs/1612.06246 (2016) - 2015
- [c7]Somaye Hashemifar, Behnam Neyshabur, Jinbo Xu:
Joint inference of tissue-specific networks with a scale free topology. BIBM 2015: 290-294 - [c6]Behnam Neyshabur, Ryota Tomioka, Nathan Srebro:
Norm-Based Capacity Control in Neural Networks. COLT 2015: 1376-1401 - [c5]Behnam Neyshabur, Nathan Srebro:
On Symmetric and Asymmetric LSHs for Inner Product Search. ICML 2015: 1926-1934 - [c4]Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro:
Path-SGD: Path-Normalized Optimization in Deep Neural Networks. NIPS 2015: 2422-2430 - [c3]Behnam Neyshabur, Ryota Tomioka, Nathan Srebro:
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning. ICLR (Workshop) 2015 - [i6]Behnam Neyshabur, Ryota Tomioka, Nathan Srebro:
Norm-Based Capacity Control in Neural Networks. CoRR abs/1503.00036 (2015) - [i5]Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro:
Path-SGD: Path-Normalized Optimization in Deep Neural Networks. CoRR abs/1506.02617 (2015) - 2014
- [c2]Behnam Neyshabur, Yury Makarychev, Nathan Srebro:
Clustering, Hamming Embedding, Generalized LSH and the Max Norm. ALT 2014: 306-320 - [i4]Behnam Neyshabur, Yury Makarychev, Nathan Srebro:
Clustering, Hamming Embedding, Generalized LSH and the Max Norm. CoRR abs/1405.3167 (2014) - [i3]Behnam Neyshabur, Nathan Srebro:
On Symmetric and Asymmetric LSHs for Inner Product Search. CoRR abs/1410.5518 (2014) - 2013
- [j1]Behnam Neyshabur, Ahmadreza Khadem, Somaye Hashemifar, Seyed Shahriar Arab:
NETAL: a new graph-based method for global alignment of protein-protein interaction networks. Bioinform. 29(13): 1654-1662 (2013) - [c1]Behnam Neyshabur, Nati Srebro, Ruslan Salakhutdinov, Yury Makarychev, Payman Yadollahpour:
The Power of Asymmetry in Binary Hashing. NIPS 2013: 2823-2831 - [i2]Behnam Neyshabur, Rina Panigrahy:
Sparse Matrix Factorization. CoRR abs/1311.3315 (2013) - [i1]Behnam Neyshabur, Payman Yadollahpour, Yury Makarychev, Ruslan Salakhutdinov, Nathan Srebro:
The Power of Asymmetry in Binary Hashing. CoRR abs/1311.7662 (2013)
Coauthor Index
Nathan Srebro
aka: Nati Srebro
aka: Nati Srebro
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last updated on 2023-10-02 00:40 CEST by the dblp team
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