Yogatama, Dani

Grandmaster level in StarCraft II using multi-agent reinforcement learning

Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019) · Nature

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 3,455

Emergent Abilities of Large Language Models

Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,264

Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems

Ling, W., Yogatama, D., Dyer, C., & Blunsom, P. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 325

Reducing Sentiment Bias in Language Models via Counterfactual Evaluation

Huang, P.-S., Zhang, H., Jiang, R., Stanforth, R., Welbl, J., Rae, J., Maini, V., Yogatama, D., & Kohli, P. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020

Sentiment Analysis and Opinion MiningEthics and Social Impacts of AI

Cited by 124

Episodic Memory in Lifelong Language Learning

d'Autume, C. D. M., Ruder, S., Kong, L., & Yogatama, D. (2019) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 95

Mind the Gap: Assessing Temporal Generalization in Neural Language Models

Lazaridou, A., Kuncoro, A., Gribovskaya, E., Agrawal, D., Liska, A., Terzi, T., Gimenez, M., d'Autume, C. D. M., Kocisky, T., Ruder, S., et al. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 90

Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers

Tay, Y., Dehghani, M., Rao, J., Fedus, W., Abnar, S., Chung, H. W., Narang, S., Yogatama, D., Vaswani, A., & Metzler, D. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 63

Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?

Tay, Y., Dehghani, M., Abnar, S., Chung, H., Fedus, W., Rao, J., Narang, S., Tran, V., Yogatama, D., & Metzler, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Topic ModelingNatural Language Processing Techniques

Cited by 34

Relational memory-augmented language models

Liu, Q., Yogatama, D., & Blunsom, P. (2022) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 23

Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling?

Tay, Y., Dehghani, M., Abnar, S., Chung, H. W., Fedus, W., Rao, J., Narang, S., Tran, V. Q., Yogatama, D., & Metzler, D. (2022) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 11

Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models

Padlewski, P., Bain, M., Henderson, M., Zhu, Z., Relan, N., Pham, H., Ong, D., Aleksiev, K., Ormazabal, A., Phua, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 1