Bhavya Kailkhura

Artificial Intelligence · 2016–2024

A researcher in the Science corpus: 9 papers; trusts 400 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 166 citations into it; #24,190 of 35,345 by trust here, #15,004 by citations
• Computer Vision and Pattern Recognition — 92 citations into it; #15,090 of 21,575 by trust here, #13,815 by citations
• Safety, Robustness & Interpretability — 92 citations into it; #1,587 of 2,251 by trust here, #1,945 by citations
• Generative Models — 32 citations into it
• Optimization & Training — 22 citations into it
• Signal Processing — 22 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Hardware and Architecture — 11 citations into it

Whom Bhavya Kailkhura trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Ian J. Goodfellow
4. Yann LeCun
5. Yoshua Bengio
6. Kaiming He
7. Diederik P. Kingma
8. Christian Szegedy
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Bhavya Kailkhura overall

1. Giulia Vilone
2. Amina Adadi
3. Shusen Liu
4. Alejandro Barredo Arrieta
5. Divyat Mahajan
6. Vanessa Buhrmester
7. Gyuhak Kim
8. Riccardo Guidotti
9. Raha Moraffah

Papers from Bhavya Kailkhura's perspective

Ordered by how strongly the researchers Bhavya Kailkhura 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking (2019) — 47 citations
• A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning: Principals, Recent Advances, and Applications (2020) — 339 citations
• TrustLLM: Trustworthiness in Large Language Models (2024) — 353 citations
• Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond (2020) — 389 citations
• Anomalous Instance Detection in Deep Learning: A Survey (2020) — 48 citations
• Generative Counterfactual Introspection for Explainable Deep Learning (2019) — 97 citations
• Universal Hard-label Black-Box Perturbations: Breaking Security-Through-Obscurity Defenses (2018) — 4 citations
• Universal Decision-Based Black-Box Perturbations: Breaking Security-Through-Obscurity Defenses (2018) — 11 citations
• TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning (2016) — 44 citations