Scott Sanner

Artificial Intelligence · 2009–2021

A researcher in the Science corpus: 16 papers; trusts 676 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 272 citations into it; #7,523 of 35,345 by trust here, #6,266 by citations
• Computer Vision and Pattern Recognition — 200 citations into it; #7,445 of 21,575 by trust here, #4,555 by citations
• Meta & Continual Learning — 143 citations into it; #365 of 1,041 by trust here, #184 by citations
• Representation & Self-Supervised Learning — 91 citations into it; #1,408 of 3,145 by trust here, #1,138 by citations
• Reinforcement Learning — 53 citations into it
• Information Systems — 31 citations into it; #1,868 of 8,771 by trust here, #1,413 by citations
• Optimization & Training — 31 citations into it
• Management Science and Operations Research — 22 citations into it

Whom Scott Sanner trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Craig Boutilier
4. Dimitri P. Bertsekas
5. Martin L. Puterman
6. Yoshua Bengio
7. Andrew Barto
8. Diederik P. Kingma
9. Yann LeCun
10. Andrew Y. Ng

Who trusts Scott Sanner overall

1. Philip S. Thomas
2. Chong Huang
3. George Dimitri Konidaris
4. Blai Bonet
5. Mario Bkassiny
6. Peter Kairouz
7. Luc De Raedt
8. Jiachun Liao
9. Martha White
10. Vito Bellini

Papers from Scott Sanner's perspective

Ordered by how strongly the researchers Scott Sanner 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

• Online Continual Learning in Image Classification: An Empirical Survey (2021) — 526 citations
• AutoRec: Autoencoders Meet Collaborative Filtering (2015) — 1,281 citations
• Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning (2021) — 237 citations
• A Survey of the Seventh International Planning Competition (2012) — 118 citations
• On the Effectiveness of Linear Models for One-Class Collaborative Filtering (2016) — 49 citations
• Practical solution techniques for first-order MDPs (2009) — 110 citations
• Social collaborative filtering for cold-start recommendations (2014) — 137 citations
• Batch-level Experience Replay with Review for Continual Learning (2020) — 18 citations
• Online Class-Incremental Continual Learning with Adversarial Shapley Value (2020) — 267 citations
• Adversarial Shapley Value Experience Replay for Task-Free Continual Learning (2020) — 6 citations
• Deep Reactive Policies for Planning in Stochastic Nonlinear Domains (2019) — 31 citations
• Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach (2018) — 32 citations
• Algorithms for Direct 0-1 Loss Optimization in Binary Classification (2013) — 125 citations
• Improving LDA topic models for microblogs via tweet pooling and automatic labeling (2013) — 511 citations
• Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda (2010) — 28 citations
• Gaussian Process Preference Elicitation (2010) — 96 citations