Behnam Neyshabur

According to our database, Behnam Neyshabur authored at least 58 papers between 2013 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models.

Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel

CoRR, 2023

Long Range Language Modeling via Gated State Spaces.

Harsh Mehta, Ankit Gupta, Ashok Cutkosky, Behnam Neyshabur

Proceedings of the Eleventh International Conference on Learning Representations, 2023

REPAIR: REnormalizing Permuted Activations for Interpolation Repair.

Keller Jordan, Hanie Sedghi, Olga Saukh, Rahim Entezari, Behnam Neyshabur

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning.

Anders Johan Andreassen, Yasaman Bahri, Behnam Neyshabur, Rebecca Roelofs

Trans. Mach. Learn. Res., 2022

Convexifying Transformers: Improving optimization and understanding of transformer networks.

Tolga Ergen, Behnam Neyshabur, Harsh Mehta

CoRR, 2022

Layer-Stack Temperature Scaling.

Amr Khalifa, Michael C. Mozer, Hanie Sedghi, Behnam Neyshabur, Ibrahim Alabdulmohsin

CoRR, 2022

Teaching Algorithmic Reasoning via In-context Learning.

Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron C. Courville, Behnam Neyshabur, Hanie Sedghi

CoRR, 2022

Understanding the effect of sparsity on neural networks robustness.

Lukas Timpl, Rahim Entezari, Hanie Sedghi, Behnam Neyshabur, Olga Saukh

CoRR, 2022

Data Scaling Laws in NMT: The Effect of Noise and Architecture.

Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Maxim Krikun, Colin Cherry, Behnam Neyshabur, Orhan Firat

CoRR, 2022

Solving Quantitative Reasoning Problems with Language Models.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Block-Recurrent Transformers.

DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, Behnam Neyshabur

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Exploring Length Generalization in Large Language Models.

Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay V. Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, Behnam Neyshabur

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Revisiting Neural Scaling Laws in Language and Vision.

Ibrahim M. Alabdulmohsin, Behnam Neyshabur, Xiaohua Zhai

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Data Scaling Laws in NMT: The Effect of Noise and Architecture.

Yamini Bansal, Behrooz Ghorbani, Ankush Garg, Biao Zhang, Colin Cherry, Behnam Neyshabur, Orhan Firat

Proceedings of the International Conference on Machine Learning, 2022

A Loss Curvature Perspective on Training Instabilities of Deep Learning Models.

Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George Edward Dahl, Zachary Nado, Orhan Firat

Proceedings of the Tenth International Conference on Learning Representations, 2022

Leveraging unlabeled data to predict out-of-distribution performance.

Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, Hanie Sedghi

Proceedings of the Tenth International Conference on Learning Representations, 2022

The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks.

Rahim Entezari, Hanie Sedghi, Olga Saukh, Behnam Neyshabur

Proceedings of the Tenth International Conference on Learning Representations, 2022

Exploring the Limits of Large Scale Pre-training.

Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

A Loss Curvature Perspective on Training Instability in Deep Learning.

Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George E. Dahl, Zachary Nado, Orhan Firat

CoRR, 2021

Deep Learning Through the Lens of Example Difficulty.

Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

When Do Curricula Work?

Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur

Proceedings of the 9th International Conference on Learning Representations, 2021

The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers.

Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi

Proceedings of the 9th International Conference on Learning Representations, 2021

Understanding the failure modes of out-of-distribution generalization.

Vaishnavh Nagarajan, Anders Andreassen, Behnam Neyshabur

Proceedings of the 9th International Conference on Learning Representations, 2021

Extreme Memorization via Scale of Initialization.

Harsh Mehta, Ashok Cutkosky, Behnam Neyshabur

Proceedings of the 9th International Conference on Learning Representations, 2021

Are wider nets better given the same number of parameters?

Anna Golubeva, Guy Gur-Ari, Behnam Neyshabur

Proceedings of the 9th International Conference on Learning Representations, 2021

Sharpness-aware Minimization for Efficiently Improving Generalization.

Pierre Foret, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

NeurIPS 2020 Competition: Predicting Generalization in Deep Learning.

Yiding Jiang, Pierre Foret, Scott Yak, Daniel M. Roy, Hossein Mobahi, Gintare Karolina Dziugaite, Samy Bengio, Suriya Gunasekar, Isabelle Guyon, Behnam Neyshabur

CoRR, 2020

The Deep Bootstrap: Good Online Learners are Good Offline Generalizers.

Preetum Nakkiran, Behnam Neyshabur, Hanie Sedghi

CoRR, 2020

What is being transferred in transfer learning?

Behnam Neyshabur, Hanie Sedghi, Chiyuan Zhang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Towards Learning Convolutions from Scratch.

Behnam Neyshabur

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning.

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

Proceedings of the NeurIPS 2020 Competition and Demonstration Track, 2020

Observational Overfitting in Reinforcement Learning.

Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur

Proceedings of the 8th International Conference on Learning Representations, 2020

Fantastic Generalization Measures and Where to Find Them.

Yiding Jiang, Behnam Neyshabur, Hossein Mobahi, Dilip Krishnan, Samy Bengio

Proceedings of the 8th International Conference on Learning Representations, 2020

The intriguing role of module criticality in the generalization of deep networks.

Niladri S. Chatterji, Behnam Neyshabur, Hanie Sedghi

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

The role of over-parametrization in generalization of neural networks.

Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks.

Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro

CoRR, 2018

Predicting protein-protein interactions through sequence-based deep learning.

Somaye Hashemifar, Behnam Neyshabur, Aly A. Khan, Jinbo Xu

Bioinform., 2018

Stronger Generalization Bounds for Deep Nets via a Compression Approach.

Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang

Proceedings of the 35th International Conference on Machine Learning, 2018

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks.

Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Implicit Regularization in Deep Learning.

Behnam Neyshabur

CoRR, 2017

Geometry of Optimization and Implicit Regularization in Deep Learning.

Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro

CoRR, 2017

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks.

Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro

CoRR, 2017

Stabilizing GAN Training with Multiple Random Projections.

Behnam Neyshabur, Srinadh Bhojanapalli, Ayan Chakrabarti

CoRR, 2017

Exploring Generalization in Deep Learning.

Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Implicit Regularization in Matrix Factorization.

Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Corralling a Band of Bandit Algorithms.

Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Data-Dependent Path Normalization in Neural Networks.

Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro

Proceedings of the 4th International Conference on Learning Representations, 2016

Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations.

Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Global Optimality of Local Search for Low Rank Matrix Recovery.

Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning.

Behnam Neyshabur, Ryota Tomioka, Nathan Srebro

Proceedings of the 3rd International Conference on Learning Representations, 2015

Path-SGD: Path-Normalized Optimization in Deep Neural Networks.

Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

On Symmetric and Asymmetric LSHs for Inner Product Search.

Behnam Neyshabur, Nathan Srebro

Proceedings of the 32nd International Conference on Machine Learning, 2015

Norm-Based Capacity Control in Neural Networks.

Behnam Neyshabur, Ryota Tomioka, Nathan Srebro

Proceedings of The 28th Conference on Learning Theory, 2015

Joint inference of tissue-specific networks with a scale free topology.

Somaye Hashemifar, Behnam Neyshabur, Jinbo Xu

Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine, 2015

2014

Clustering, Hamming Embedding, Generalized LSH and the Max Norm.

Behnam Neyshabur, Yury Makarychev, Nathan Srebro

Proceedings of the Algorithmic Learning Theory - 25th International Conference, 2014

2013

Sparse Matrix Factorization.

Behnam Neyshabur, Rina Panigrahy

CoRR, 2013

NETAL: a new graph-based method for global alignment of protein-protein interaction networks.

Behnam Neyshabur, Ahmadreza Khadem, Somaye Hashemifar, Seyed Shahriar Arab

Bioinform., 2013

The Power of Asymmetry in Binary Hashing.

Behnam Neyshabur, Nati Srebro, Ruslan Salakhutdinov, Yury Makarychev, Payman Yadollahpour

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013