Palash Goyal
Artificial Intelligence · 2017–2023
A researcher in the Science corpus: 6 papers; trusts 181 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 111 citations into it; #6,212 of 35,345 by trust here, #8,365 by citations
- Statistical and Nonlinear Physics — 64 citations into it; #951 of 10,127 by trust here, #1,430 by citations
- Graph Learning — 53 citations into it; #175 of 693 by trust here, #318 by citations
- Representation & Self-Supervised Learning — 48 citations into it; #956 of 3,145 by trust here, #1,658 by citations
- Computer Vision and Pattern Recognition — 44 citations into it
- Natural Language Processing — 37 citations into it
- Molecular Biology — 20 citations into it
- Computer Networks and Communications — 14 citations into it; #1,372 of 9,886 by trust here, #2,300 by citations
Whom Palash Goyal trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Jure Leskovec
- Diederik P. Kingma
- Mark Newman
- Ilya Sutskever
- Daixin Wang
- Alex Krizhevsky
- Yann LeCun
- Aditya Grover
Who trusts Palash Goyal overall
- Mark Heimann
- David M. Beskow
- Woojeong Jin
- Seyed Mehran Kazemi
- William L. Hamilton
- Yassine Ouali
- Aniket Bajpai
- Joakim Skarding
- Sujit Rokka Chhetri
- Benedek Rozemberczki
Papers from Palash Goyal's perspective
Ordered by how strongly the researchers Palash Goyal trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Graph Embedding Techniques, Applications, and Performance: A Survey (2017) — 1,820 citations
- “I’m fully who I am”: Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation (2023) — 99 citations
- Deep Learning for Natural Language Processing (2019) — 555 citations
- DynGEM: Deep Embedding Method for Dynamic Graphs (2018) — 423 citations
- Embedding Networks with Edge Attributes (2018) — 13 citations
- dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning (2018) — 454 citations