James Bailey

Artificial Intelligence · International Conference on Machine Learning · 2009–2021

A researcher in the Science corpus: 24 papers; trusts 818 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 442 citations into it; #4,421 of 35,345 by trust here, #2,552 by citations
Computer Vision and Pattern Recognition — 276 citations into it; #15,744 of 21,575 by trust here, #5,711 by citations
Safety, Robustness & Interpretability — 210 citations into it; #1,554 of 2,251 by trust here, #831 by citations
Model Architectures — 80 citations into it
Optimization & Training — 80 citations into it
Signal Processing — 78 citations into it
Evaluation & Benchmarks — 41 citations into it
Representation & Self-Supervised Learning — 39 citations into it

Whom James Bailey trusts overall

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

Who trusts James Bailey overall

Papers from James Bailey's perspective

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

Their papers

Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality (2018) — 849 citations
Dimensionality-Driven Learning with Noisy Labels (2018) — 475 citations
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance (2010) — 2,394 citations
Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections (2016) — 146 citations
Iterative Learning with Open-set Noisy Labels (2018) — 357 citations
On the Convergence and Robustness of Adversarial Training (2021) — 386 citations
The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality (2017) — 62 citations
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data (2018) — 11 citations
Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems (2019) — 547 citations
Normalized Loss Functions for Deep Learning with Noisy Labels (2020) — 563 citations
Black-box Adversarial Attacks on Video Recognition Models (2019) — 171 citations
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets (2020) — 364 citations
GAVIN (2021) — 26 citations
Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression (2021) — 333 citations
Improving Adversarial Robustness Requires Revisiting Misclassified Examples (2020) — 849 citations
MurTree: Optimal Decision Trees via Dynamic Programming and Search (2020) — 89 citations
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks (2020) — 617 citations
Clean-Label Backdoor Attacks on Video Recognition Models (2020) — 324 citations
MurTree: Optimal Classification Trees via Dynamic Programming and Search (2020) — 19 citations
Symmetric Cross Entropy for Robust Learning With Noisy Labels (2019) — 1,167 citations