Sanjay Chawla

Artificial Intelligence · 2009–2022

A researcher in the Science corpus: 11 papers; trusts 210 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 192 citations into it; #3,972 of 35,345 by trust here, #6,720 by citations
• Computer Vision and Pattern Recognition — 69 citations into it; #20,136 of 21,575 by trust here, #18,782 by citations
• Computer Networks and Communications — 58 citations into it; #505 of 9,886 by trust here, #1,118 by citations
• Signal Processing — 43 citations into it; #2,122 of 7,572 by trust here, #1,669 by citations
• Information Systems — 28 citations into it; #846 of 8,771 by trust here, #4,012 by citations
• Representation & Self-Supervised Learning — 28 citations into it
• Safety, Robustness & Interpretability — 19 citations into it; #803 of 2,251 by trust here, #1,945 by citations
• Optimization & Training — 13 citations into it; #1,250 of 2,601 by trust here, #2,313 by citations

Whom Sanjay Chawla trusts overall

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

1. Jaka Demsar
2. Huaiyu Zhu
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Andrew Y. Ng
6. Trevor J. Hastie
7. Alex Krizhevsky
8. Amir Globerson
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Sanjay Chawla overall

1. Arturo Geigel
2. Reza Shokri
3. Bo Li
4. Kathrin Grosse
5. Shike Mei
6. Xiaojin Zhu
7. Nguyen Phan Dang Khoa
8. Battista Biggio
9. Scott Alfeld
10. Yevgeniy Vorobeychik

Papers from Sanjay Chawla's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• k-means-: A Unified Approach to Clustering and Outlier Detection (2013) — 282 citations
• Class Confidence Weighted kNN Algorithms for Imbalanced Data Sets (2011) — 162 citations
• Deep Learning for Anomaly Detection: A Survey (2019) — 1,857 citations
• Mining adversarial patterns via regularized loss minimization (2010) — 58 citations
• A Game Theoretical Model for Adversarial Learning (2009) — 73 citations
• Interpretable scientific discovery with symbolic regression: a review (2022) — 338 citations
• Anomaly Detection using One-Class Neural Networks (2018) — 422 citations
• RoadTracer: Automatic Extraction of Road Networks from Aerial Images (2018) — 371 citations
• Efficient Thompson Sampling for Online Matrix-Factorization Recommendation (2015) — 196 citations
• Sparse Feature Attacks in Adversarial Learning (2014) — 60 citations
• Discovering spatio-temporal causal interactions in traffic data streams (2011) — 401 citations