Krishna Pillutla
Artificial Intelligence · 2017–2021
A researcher in the Science corpus: 4 papers; trusts 54 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 101 citations into it; #16,290 of 35,345 by trust here, #11,921 by citations
- Natural Language Processing — 34 citations into it
- Evaluation & Benchmarks — 32 citations into it
- Federated & Distributed Learning — 30 citations into it; #153 of 319 by trust here, #258 by citations
- Optimization & Training — 26 citations into it
- Computer Vision and Pattern Recognition — 21 citations into it
- Computational Mechanics — 16 citations into it; #9,556 of 10,001 by trust here, #8,947 by citations
- Statistics and Probability — 13 citations into it
Whom Krishna Pillutla trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Sepp Hochreiter
- Yoshua Bengio
- Francis R. Bach
- Gábor Lugosi
- Alec Go
- Alex Krizhevsky
- Yann LeCun
- Diederik P. Kingma
- J. Urgen Schmidhuber
Who trusts Krishna Pillutla overall
- Lie He
- Sai Praneeth Karimireddy
- Yuyang Deng
- Virat Shejwalkar
- Chen Chen
- Mohammed Adnan
- Hongyi Wang
- Martin Jaggi
- Aaron Lou
- Peter Kairouz
Papers from Krishna Pillutla's perspective
Ordered by how strongly the researchers Krishna Pillutla trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers (2021) — 548 citations
- A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares) (2017) — 41 citations
- Robust Aggregation for Federated Learning (2019) — 1,001 citations
- A Superquantile Approach to Federated Learning with Heterogeneous Devices (2021) — 28 citations