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Róbert Csordás
Tytuł
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The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers
R Csordás, K Irie, J Schmidhuber
EMNLP 2021, 2021
542021
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers
K Irie, I Schlag, R Csordás, J Schmidhuber
Conference on Neural Information Processing Systems (NeurIPS), 2021, 2021
382021
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks
R Csordás, S van Steenkiste, J Schmidhuber
International Conference on Learning Representations (ICLR), 2021
372021
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control
R Csordás, J Schmidhuber
International Conference on Learning Representations (ICLR), 2019
302019
Method and apparatus for generating a displacement map of an input dataset pair
R Csordás, Á Kis-Benedek, B Szalkai
US Patent 10,380,753, 2019
192019
A Modern Self-Referential Weight Matrix That Learns to Modify Itself
K Irie, I Schlag, R Csordás, J Schmidhuber
Deep RL Workshop NeurIPS 2021, 2021
152021
The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization
R Csordás, K Irie, J Schmidhuber
International Conference on Learning Representations (ICLR), 2021
112021
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention
K Irie*, R Csordás*, J Schmidhuber
International Conference on Machine Learning 2022 (ICML), 2022
72022
A generalist neural algorithmic learner
B Ibarz, V Kurin, G Papamakarios, K Nikiforou, M Bennani, R Csordás, ...
Learning on Graphs Conference, 2: 1-2: 23, 2022
32022
Improving Baselines in the Wild
K Irie, I Schlag, R Csordás, J Schmidhuber
NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and …, 2021
12021
CTL++: Evaluating Generalization on Never-Seen Compositional Patterns of Known Functions, and Compatibility of Neural Representations
R Csordás, K Irie, J Schmidhuber
EMNLP 2022, 2022
2022
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