Stephen J. Roberts

Artificial Intelligence · 2000–2021

A researcher in the Science corpus: 32 papers; trusts 626 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 317 citations into it; #4,329 of 35,345 by trust here, #5,252 by citations
Computer Vision and Pattern Recognition — 107 citations into it; #17,790 of 21,575 by trust here, #19,310 by citations
Probabilistic & Bayesian Methods — 100 citations into it; #1,366 of 1,525 by trust here, #1,487 by citations
Reinforcement Learning — 100 citations into it
Management Science and Operations Research — 81 citations into it; #5,472 of 11,946 by trust here, #4,921 by citations
Optimization & Training — 58 citations into it
Representation & Self-Supervised Learning — 55 citations into it; #1,920 of 3,145 by trust here, #2,805 by citations
Computational Theory and Mathematics — 50 citations into it; #4,907 of 12,182 by trust here, #4,696 by citations

Whom Stephen J. Roberts trusts overall

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

Who trusts Stephen J. Roberts overall

Papers from Stephen J. Roberts's perspective

Ordered by how strongly the researchers Stephen J. Roberts 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)
Long Short-Term Memory
Gradient-based learning applied to document recognition
Attention is All you Need (Neural Information Processing Systems)
ImageNet: A large-scale hierarchical image database
Learning Multiple Layers of Features from Tiny Images
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

Bayesian optimization for sensor set selection (2010) — 178 citations
Sequential Bayesian Prediction in the Presence of Changepoints and Faults (2010) — 79 citations
Independent Component Analysis: Principles and Practice (2001) — 188 citations
Variational Inference for Gaussian Process Modulated Poisson Processes (2014) — 127 citations
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning (2014) — 25 citations
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes (2015) — 22 citations
Ready Policy One: World Building Through Active Learning (2020) — 54 citations
Same State, Different Task: Continual Reinforcement Learning without Interference (2021) — 62 citations
Hierarchical Indian buffet neural networks for Bayesian continual learning (2019) — 26 citations
Hidden Markov Independent Component Analysis (2000) — 34 citations
Variational Bayes for generalized autoregressive models (2002) — 172 citations
Towards Real-Time Information Processing of Sensor Network Data Using Computationally Efficient Multi-output Gaussian Processes (2008) — 189 citations
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature (2014) — 121 citations
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction (2019) — 51 citations
Thresholded ConvNet ensembles: neural networks for technical forecasting (2018) — 14 citations
Effective Diversity in Population-Based Reinforcement Learning (2020) — 191 citations
Indian Buffet Neural Networks for Continual Learning (2019) — 6 citations
Enhancing Time-Series Momentum Strategies Using Deep Neural Networks (2019) — 103 citations
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018) — 285 citations
MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting (2018) — 15 citations