Trapit Bansal
Artificial Intelligence · 2015–2020
A researcher in the Science corpus: 7 papers; trusts 288 researchers and is trusted by 34 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 166 citations into it; #6,372 of 35,345 by trust here, #4,018 by citations
- Natural Language Processing — 80 citations into it; #3,181 of 5,688 by trust here, #4,112 by citations
- Computer Vision and Pattern Recognition — 66 citations into it
- Reinforcement Learning — 60 citations into it; #1,077 of 2,242 by trust here, #518 by citations
- Representation & Self-Supervised Learning — 37 citations into it
- Information Systems — 35 citations into it; #3,183 of 8,771 by trust here, #3,402 by citations
- Evaluation & Benchmarks — 27 citations into it
- Recommender Systems — 19 citations into it
Whom Trapit Bansal trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Ruslan Salakhutdinov
- Ilya Sutskever
- R. Caruana
- Alex Krizhevsky
- J. Urgen Schmidhuber
- Ronald J. Williams
- Jimmy Ba
Who trusts Trapit Bansal overall
- Wenpeng Yin
- Ting-Li Chen
- Shikhar Murty
- Aminu Da’u
- Christos Kaplanis
- Sergey Levine
- Panupong Pasupat
- Tatsunori B. Hashimoto
- Jonathan Bragg
- Xisen Jin
Papers from Trapit Bansal's perspective
Ordered by how strongly the researchers Trapit Bansal 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- 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)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments (2017) — 376 citations
- Emergent Complexity via Multi-Agent Competition (2017) — 435 citations
- 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
- Ask the GRU: Multi-task Learning for Deep Text Recommendations (2016) — 327 citations
- Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles (2015) — 89 citations
- Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets (2018) — 67 citations