Csordás, Róbert

The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers

Csordás, R., Irie, K., & Schmidhuber, J. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 106

Mindstorms in Natural Language-Based Societies of Mind

Zhuge, M., Liu, H., Faccio, F., Ashley, D. R., Csordás, R., Gopalakrishnan, A., Hamdi, A., Hammoud, H. A. A. K., Herrmann, V., Irie, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsMulti-Agent Systems and Negotiation

Cited by 24

Mindstorms in Natural Language-Based Societies of Mind

Zhuge, M., Liu, H., Faccio, F., Ashley, D. R., Csordás, R., Gopalakrishnan, A., Hamdi, A., Hammoud, H. A. A. K., Herrmann, V., Irie, K., et al. (2025) · Computational Visual Media

Multimodal Machine Learning ApplicationsMulti-Agent Systems and Negotiation

Cited by 24

Going Beyond Linear Transformers with Recurrent Fast Weight Programmers

Irie, K., Schlag, I., Csordás, R., & Schmidhuber, J. (2021) · arXiv

Topic ModelingNeural Networks and Applications

Cited by 14

The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention

Irie, K., Csordás, R., & Schmidhuber, J. (2022) · arXiv

Explainable Artificial Intelligence (XAI)Domain Adaptation and Few-Shot Learning

Cited by 6

Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions

Irie, K., Csordás, R., & Schmidhuber, J. (2023) · arXiv

semigroups and automata theoryNeural Networks and Applications

Cited by 3

Randomized Positional Encodings Boost Length Generalization of Transformers

Ruoss, A., Delétang, G., Genewein, T., Grau-Moya, J., Csordás, R., Bennani, M., Legg, S., & Veness, J. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1