Dauphin, Yann

mixup: Beyond Empirical Risk Minimization

Zhang, H., Cisse, M., Dauphin, Y. N., & Lopez-Paz, D. (2017) · arXiv

Machine Learning and Data ClassificationDomain Adaptation and Few-Shot Learning

Cited by 5,831

Convolutional Sequence to Sequence Learning

Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 2,850

Hierarchical Neural Story Generation

Fan, A., Lewis, M., & Dauphin, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingArtificial Intelligence in Games

Cited by 1,266

Hierarchical Neural Story Generation

Fan, A., Lewis, M., & Dauphin, Y. (2018) · arXiv

Topic ModelingArtificial Intelligence in Games

Cited by 1,242

Identifying and attacking the saddle point problem in high-dimensional non-convex optimization

Dauphin, Y., Pascanu, R., Gulcehre, C., Cho, K., Ganguli, S., & Bengio, Y. (2014) · arXiv

Stochastic Gradient Optimization TechniquesAdvanced Optimization Algorithms Research

Cited by 827

Combining modality specific deep neural networks for emotion recognition in video

Kahou, S. E., Pal, C., Bouthillier, X., Froumenty, P., Gülçehre, Ç., Memisevic, R., Vincent, P., Courville, A., Bengio, Y., Ferrari, R. C., et al. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction

Emotion and Mood RecognitionFace recognition and analysis

Cited by 391

Deal or No Deal? End-to-End Learning of Negotiation Dialogues

Lewis, M., Yarats, D., Dauphin, Y., Parikh, D., & Batra, D. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Speech and dialogue systemsMulti-Agent Systems and Negotiation

Cited by 347

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

Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment

Beygelzimer, A., Dauphin, Y. N., Liang, P., & Vaughan, J. W. (2023) · arXiv

Academic Writing and PublishingPsychology Research and Bibliometrics

Cited by 2