Rishikesh Jha

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 123 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #14,301 of 35,345 by trust here, #19,018 by citations
• Natural Language Processing — 56 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Large Language Models — 10 citations into it
• Reinforcement Learning — 7 citations into it
• Information Systems — 5 citations into it

Whom Rishikesh Jha trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Chelsea Finn
5. Jimmy Ba
6. Oriol Vinyals
7. Ilya Sutskever
8. Alex Krizhevsky
9. J. Urgen Schmidhuber
10. Yann LeCun

Who trusts Rishikesh Jha overall

1. Wenpeng Yin
2. Shikhar Murty
3. Panupong Pasupat
4. Jonathan Bragg
5. Tatsunori B. Hashimoto
6. Rabeeh Karimi Mahabadi
7. Xisen Jin
8. Trapit Bansal
9. Yanda Chen
10. Bill Yuchen Lin

Papers from Rishikesh Jha's perspective

Ordered by how strongly the researchers Rishikesh Jha trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks (2020) — 100 citations
• Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks (2019) — 127 citations