Dissecting the Winning Solution of the HiggsML Challenge. - Gábor Melis (HEPML@NIPS, 2014)

Semantic Parsing with Semi-Supervised Sequential Autoencoders. - Tomás Kociský; Gábor Melis; Edward Grefenstette; Chris Dyer; Wang Ling; Phil Blunsom; Karl Moritz Hermann (EMNLP, 2016)

On the State of the Art of Evaluation in Neural Language Models. - Gábor Melis; Chris Dyer; Phil Blunsom (ICLR (Poster), 2018)

Memory Architectures in Recurrent Neural Network Language Models. - Dani Yogatama; Yishu Miao; Gábor Melis; Wang Ling; Adhiguna Kuncoro; Chris Dyer; Phil Blunsom (ICLR (Poster), 2018)

Pushing the bounds of dropout. - Gábor Melis; Charles Blundell; Tomás Kociský; Karl Moritz Hermann; Chris Dyer; Phil Blunsom (CoRR, 2018)

Encoding Spatial Relations from Natural Language. - Tiago Ramalho; Tomás Kociský; Frederic Besse; S. M. Ali Eslami; Gábor Melis; Fabio Viola; Phil Blunsom; Karl Moritz Hermann (CoRR, 2018)

The NarrativeQA Reading Comprehension Challenge. - Tomás Kociský; Jonathan Schwarz; Phil Blunsom; Chris Dyer; Karl Moritz Hermann; Gábor Melis; Edward Grefenstette (Trans. Assoc. Comput. Linguistics, 2018)

Variational Smoothing in Recurrent Neural Network Language Models. - Lingpeng Kong; Gábor Melis; Wang Ling; Lei Yu 008; Dani Yogatama (ICLR (Poster), 2019)

Unsupervised Recurrent Neural Network Grammars. - Yoon Kim; Alexander M. Rush; Lei Yu 008; Adhiguna Kuncoro; Chris Dyer; Gábor Melis (NAACL-HLT (1), 2019)

A Critical Analysis of Biased Parsers in Unsupervised Parsing. - Chris Dyer; Gábor Melis; Phil Blunsom (CoRR, 2019)

Mogrifier LSTM. - Gábor Melis; Tomás Kociský; Phil Blunsom (ICLR, 2020)

Capturing document context inside sentence-level neural machine translation models with self-training. - Elman Mansimov; Gábor Melis; Lei Yu (CoRR, 2020)

Mutual Information Constraints for Monte-Carlo Objectives. - Gábor Melis; András György 001; Phil Blunsom (CoRR, 2020)

Two-Tailed Averaging: Anytime Adaptive Once-in-a-while Optimal Iterate Averaging for Stochastic Optimization. - Gábor Melis (CoRR, 2022)

Circling Back to Recurrent Models of Language. - Gábor Melis (CoRR, 2022)

Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling. - Gábor Melis; András György 001; Phil Blunsom (J. Mach. Learn. Res., 2022)

Adaptive Hashing - Faster Hash Functions with Fewer Collisions. - Gábor Melis (ELS, 2024)