Neyshabur, Behnam

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Sharpness-Aware Minimization for Efficiently Improving Generalization

Foret, P., Kleiner, A., Mobahi, H., & Neyshabur, B. (2020) · arXiv

Stochastic Gradient Optimization TechniquesMachine Learning and Data Classification

Cited by 521

Solving Quantitative Reasoning Problems with Language Models

Lewkowycz, A., Andreassen, A., Dohan, D., Dyer, E., Michalewski, H., Ramasesh, V., Slone, A., Anil, C., Schlag, I., Gutman-Solo, T., et al. (2022) · arXiv

Mathematics Education and PedagogyTopic Modeling

Cited by 358

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Implicit Regularization in Matrix Factorization

Gunasekar, S., Woodworth, B., Bhojanapalli, S., Neyshabur, B., & Srebro, N. (2018) · 2018 Information Theory and Applications Workshop (ITA)

Sparse and Compressive Sensing TechniquesStochastic Gradient Optimization Techniques

Cited by 170

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

Entezari, R., Sedghi, H., Saukh, O., & Neyshabur, B. (2021) · arXiv

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 156

Gemma 2: Improving Open Language Models at a Practical Size

Gemma Team, Riviere, M., Pathak, S., Sessa, P. G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ramé, A., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 149

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Understanding the Failure Modes of Out-of-Distribution Generalization

Nagarajan, V., Andreassen, A., & Neyshabur, B. (2020) · arXiv

Domain Adaptation and Few-Shot LearningStochastic Gradient Optimization Techniques

Cited by 55

Observational Overfitting in Reinforcement Learning

Song, X., Jiang, Y., Tu, S., Du, Y., & Neyshabur, B. (2019) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 48

Exploring Length Generalization in Large Language Models

Anil, C., Wu, Y., Andreassen, A., Lewkowycz, A., Misra, V., Ramasesh, V., Slone, A., Gur-Ari, G., Dyer, E., & Neyshabur, B. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 44

Block-Recurrent Transformers

Hutchins, D., Schlag, I., Wu, Y., Dyer, E., & Neyshabur, B. (2022) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 43

Revisiting neural scaling laws in language and vision

Alabdulmohsin, I., Neyshabur, B., & Zhai, X. (2022) · Advances in Neural Information Processing Systems 35

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 41

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

Singh, A., Co-Reyes, J. D., Agarwal, R., Anand, A., Patil, P., Garcia, X., Liu, P. J., Harrison, J., Lee, J., Xu, K., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 10