| The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers R Csordás, K Irie, J Schmidhuber EMNLP 2021, 2021 | 125 | 2021 |
| 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 | 98 | 2021 |
| Randomized Positional Encodings Boost Length Generalization of Transformers A Ruoss, G Delétang, T Genewein, J Grau-Moya, R Csordás, M Bennani, ... arXiv preprint arXiv:2305.16843, 2023 | 68 | 2023 |
| 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 | 67 | 2021 |
| 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 | 65 | 2022 |
| Mindstorms in Natural Language-Based Societies of Mind M Zhuge, H Liu, F Faccio, DR Ashley, R Csordás, A Gopalakrishnan, ... arXiv preprint arXiv:2305.17066, 2023 | 59 | 2023 |
| 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 | 56 | 2021 |
| 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 | 45 | 2019 |
| 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 | 37 | 2021 |
| 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 | 33 | 2022 |
| 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 | 31 | 2019 |
| Approximating Two-Layer Feedforward Networks for Efficient Transformers R Csordás, K Irie, J Schmidhuber arXiv preprint arXiv:2310.10837, 2023 | 15 | 2023 |
| 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 | 12 | 2022 |
| SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention R Csordás, P Piękos, K Irie, J Schmidhuber arXiv preprint arXiv:2312.07987, 2023 | 6 | 2023 |
| Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations R Csordás, C Potts, CD Manning, A Geiger arXiv preprint arXiv:2408.10920, 2024 | 3 | 2024 |
| Automating Continual Learning K Irie, R Csordás, J Schmidhuber | 2 | 2023 |
| Improving Baselines in the Wild K Irie, I Schlag, R Csordás, J Schmidhuber NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and …, 2021 | 2 | 2021 |
| Self-organising Neural Discrete Representation Learning à la Kohonen K Irie, R Csordás, J Schmidhuber International Conference on Artificial Neural Networks, 343-362, 2024 | 1* | 2024 |
| MrT5: Dynamic Token Merging for Efficient Byte-level Language Models J Kallini, S Murty, CD Manning, C Potts, R Csordás arXiv preprint arXiv:2410.20771, 2024 | | 2024 |
| MoEUT: Mixture-of-Experts Universal Transformers R Csordás, K Irie, J Schmidhuber, C Potts, CD Manning arXiv preprint arXiv:2405.16039, 2024 | | 2024 |
Juergen SchmidhuberKing Abdullah University of Science and Technology / The Swiss AI Lab, IDSIA / University of LuganoVerified email at kaust.edu.sa
