Imanol Schlag
Artificial Intelligence · 2018–2022
A researcher in the Science corpus: 4 papers; trusts 128 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 103 citations into it; #8,410 of 35,345 by trust here, #5,120 by citations
- Natural Language Processing — 55 citations into it; #1,871 of 5,688 by trust here, #1,343 by citations
- Model Architectures — 49 citations into it
- Computer Vision and Pattern Recognition — 35 citations into it; #6,564 of 21,575 by trust here, #14,151 by citations
- Optimization & Training — 15 citations into it
- Evaluation & Benchmarks — 14 citations into it
- Representation & Self-Supervised Learning — 11 citations into it
- Cognitive Neuroscience — 8 citations into it
Whom Imanol Schlag trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- J. Urgen Schmidhuber
- Geoffrey E. Hinton
- Alex Graves
- Yoshua Bengio
- Diederik P. Kingma
- Sepp Hochreiter
- Christoph von der Malsburg
- Jimmy Ba
- Alex Krizhevsky
- Paul J. Werbos
Who trusts Imanol Schlag overall
- Kezhen Chen
- Shuhao Cao
- Paul Smolensky
- Shuai Tang
- Klaus Greff
- Sjoerd van Steenkiste
- Maxwell Nye
- J. Urgen Schmidhuber
- Qiuyuan Huang
- Rupesh Kumar Srivastava
Papers from Imanol Schlag's perspective
Ordered by how strongly the researchers Imanol Schlag 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
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Solving Quantitative Reasoning Problems with Language Models (2022) — 1,845 citations
- Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving (2019) — 59 citations
- Learning to Reason with Third-Order Tensor Products (2018) — 70 citations
- Linear Transformers Are Secretly Fast Weight Programmers (2021) — 474 citations