Our publications & strategic partners research papers & articles
Our scientists and engineers focus on fundamental scientific breakthroughs to help guide the advancement of AI. Browse some of our publications spanning a wide range of core AI disciplines.
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The Riemannian Geometry of Deep Generative Models |
Dialog-based Interactive Image Retrieval H. Wu, X. Guo, Y. Cheng, S. J. Rennie, G. Tesauro and R. S. Feris Download paper |
| Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent X. Lian, C. Zhang, H. Zhang, C.-J. Hsieh, W. Zhang, and J. Liu Download paper |
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study S. Gupta, W. Zhang, and F. Wang Download paper |
| Increasing Trust in AI Services through Supplier's Declarations of Conformity M. Hind, S. Mehta, A. Mojsilović, R. Nair, K. N. Ramamurthy, A. Olteanu and K. R. Varshney Download paper |
An End-To-End Machine Learning Pipeline That Ensures Fairness Policies S. Shaikh, H. Vishwakarma, S. Mehta, K. R. Varshney, K. N. Ramamurthy and D. Wei Download paper |
| Mixed-precision training of deep neural networks using computational memory S. R. Nandakumar, M. Le Gallo, I. Boybat, B. Rajendran, A. Sebastian and E. Eleftheriou Download paper |
Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks Vardaan Pahuja, Anirban Laha, Shachar Mirkin, Vikas C. Raykar, Lili Kotlerman, Guy Lev Download paper |
| Elseviers \ BAIR | Cornell | AUT |
| JAIR | Nesta |
