Michael C. Mozer
Artificial Intelligence · 1988–2021
A researcher in the Science corpus: 35 papers; trusts 1,131 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 278 citations into it; #13,704 of 35,345 by trust here, #11,624 by citations
- Computer Vision and Pattern Recognition — 237 citations into it; #4,878 of 21,575 by trust here, #6,281 by citations
- Model Architectures — 123 citations into it; #1,328 of 2,278 by trust here, #1,665 by citations
- Representation & Self-Supervised Learning — 100 citations into it; #638 of 3,145 by trust here, #1,395 by citations
- Reinforcement Learning — 49 citations into it
- Natural Language Processing — 48 citations into it; #2,570 of 5,688 by trust here, #4,449 by citations
- Evaluation & Benchmarks — 41 citations into it
- Optimization & Training — 40 citations into it
Whom Michael C. Mozer trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- J. Urgen Schmidhuber
- Diederik P. Kingma
- David E. Rumelhart
- Yoshua Bengio
- Alex Krizhevsky
- Yann LeCun
- Sepp Hochreiter
- Kaiming He
- Ronald J. Williams
Who trusts Michael C. Mozer overall
- J. Urgen Schmidhuber
- Mark B. Ring
- Yoshua Bengio
- Sepp Hochreiter
- Sergey Levine
- Klaus Greff
- Quoc V. Le
- Armand Joulin
- Mohit Bansal
- Trevor Darrell
Papers from Michael C. Mozer's perspective
Ordered by how strongly the researchers Michael C. Mozer trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Learning Deep Disentangled Embeddings with the F-Statistic Loss (2018) — 238 citations
- Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding (2018) — 94 citations
- Coordination Among Neural Modules Through a Shared Global Workspace (2021) — 117 citations
- Learning to generate images with perceptual similarity metrics (2015) — 206 citations
- Localist Attractor Networks (2001) — 34 citations
- Wandering Within a World: Online Contextualized Few-Shot Learning (2020) — 33 citations
- Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning (2021) — 77 citations
- Sequential Mastery of Multiple Visual Tasks: Networks Naturally Learn to Learn and Forget to Forget (2019) — 24 citations
- Understanding invariance via feedforward inversion of discriminatively trained classifiers (2021) — 11 citations
- State-Denoised Recurrent Neural Networks (2018) — 8 citations
- Convolutional Bipartite Attractor Networks (2019) — 8 citations
- Bayesian community-wide culture-independent microbial source tracking (2011) — 1,590 citations
- Neural Production Systems (2021) — 94 citations
- Mitigating bias in calibration error estimation (2020) — 131 citations
- Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems (2020) — 75 citations
- Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning (2018) — 90 citations
- Optimizing Instructional Policies (2013) — 36 citations
- When does fading enhance perceptual category learning? (2013) — 59 citations
- Neural net architectures for temporal sequence processing (2007) — 91 citations
- Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic (2003) — 305 citations