Rakshit S. Trivedi
Artificial Intelligence · 2016–2019
A researcher in the Science corpus: 5 papers; trusts 40 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 47 citations into it; #25,055 of 35,345 by trust here, #17,110 by citations
- Reinforcement Learning — 26 citations into it
- Graph Learning — 15 citations into it
- Statistical and Nonlinear Physics — 15 citations into it
- Computer Vision and Pattern Recognition — 12 citations into it
- Representation & Self-Supervised Learning — 12 citations into it
- Economics and Econometrics — 11 citations into it
- Applied Mathematics — 7 citations into it; #7,395 of 7,620 by trust here, #2,098 by citations
Whom Rakshit S. Trivedi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Maximilian Nickel
- Antoine Bordes
- Geoffrey E. Hinton
- Esko Valkeila
- Yoshua Bengio
- Andrew Y. Ng
- Nan Du
- Richard Socher
- Ilya Sutskever
- Diederik P. Kingma
Who trusts Rakshit S. Trivedi overall
- Seyed Mehran Kazemi
- Woojeong Jin
- Rishab Goel
- Yanbang Wang
- Joakim Skarding
- Srijan Kumar
- Emanuele Rossi
- Shib Sankar Dasgupta
- Meng Qu
- Jure Leskovec
Papers from Rakshit S. Trivedi's perspective
Ordered by how strongly the researchers Rakshit S. Trivedi 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
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs (2017) — 583 citations
- Deep Mean Field Games for Learning Optimal Behavior Policy of Large Populations (2017) — 37 citations
- DyRep: Learning Representations over Dynamic Graphs (2019) — 678 citations
- Learning Deep Mean Field Games for Modeling Large Population Behavior (2017) — 56 citations
- Recurrent Marked Temporal Point Processes: Embedding Event History to Vector (2016) — 831 citations