Katja Hofmann
Artificial Intelligence · with Whiteson · 2011–2023
A researcher in the Science corpus: 18 papers; trusts 580 researchers and is trusted by 57 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 319 citations into it; #7,756 of 35,345 by trust here, #7,112 by citations
- Computer Vision and Pattern Recognition — 164 citations into it
- Reinforcement Learning — 162 citations into it; #644 of 2,242 by trust here, #568 by citations
- Management Science and Operations Research — 90 citations into it; #3,991 of 11,946 by trust here, #2,241 by citations
- Information Systems — 85 citations into it; #889 of 8,771 by trust here, #1,586 by citations
- Evaluation & Benchmarks — 56 citations into it; #1,679 of 3,147 by trust here, #2,545 by citations
- Representation & Self-Supervised Learning — 55 citations into it
- Meta & Continual Learning — 43 citations into it; #375 of 1,041 by trust here, #226 by citations
Whom Katja Hofmann trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Thorsten Joachims
- Richard S. Sutton
- J. Langford
- Diederik P. Kingma
- Yoshua Bengio
- Geoffrey E. Hinton
- Olivier Chapelle
- Lihong Li
- Alex Krizhevsky
- Yann LeCun
Who trusts Katja Hofmann overall
- Masrour Zoghi
- Shimon Whiteson
- Maarten de Rijke
- Chang Li
- Miroslav Dud'ik
- Ilya Markov
- Thorsten Joachims
- Chelsea Finn
- Aleksandrs Slivkins
- Róbert Istvan Busa-Fekete
Papers from Katja Hofmann's perspective
Ordered by how strongly the researchers Katja Hofmann 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- 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)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games (2016) — 45 citations
- CAML: Fast Context Adaptation via Meta-Learning (2018) — 38 citations
- Meta Reinforcement Learning with Latent Variable Gaussian Processes (2018) — 148 citations
- The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors (2021) — 73 citations
- Contextual Dueling Bandits (2015) — 162 citations
- Imitating Human Behaviour with Diffusion Models (2023) — 323 citations
- VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning (2019) — 331 citations
- Depth and nonlinearity induce implicit exploration for RL (2018) — 4 citations
- Disability-first Dataset Creation: Lessons from Constructing a Dataset for Teachable Object Recognition with Blind and Low Vision Data Collectors (2021) — 50 citations
- ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition (2021) — 55 citations
- Reusing historical interaction data for faster online learning to rank for IR (2013) — 122 citations
- Towards Conversational Recommender Systems (2016) — 475 citations
- Memory Efficient Meta-Learning with Large Images (2021) — 21 citations
- Fast Context Adaptation via Meta-Learning (2018) — 432 citations
- Online Evaluation for Information Retrieval (2016) — 115 citations
- The Malmo Platform for Artificial Intelligence Experimentation (2016) — 423 citations
- Fidelity, Soundness, and Efficiency of Interleaved Comparison Methods (2013) — 55 citations
- A probabilistic method for inferring preferences from clicks (2011) — 127 citations