Search Results for author: Hang Le

Found 11 papers, 7 papers with code

Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception

1 code implementation NeurIPS 2021 Joel Dapello, Jenelle Feather, Hang Le, Tiago Marques, David D. Cox, Josh H. McDermott, James J. DiCarlo, SueYeon Chung

Adversarial examples are often cited by neuroscientists and machine learning researchers as an example of how computational models diverge from biological sensory systems.

Adversarial Robustness

Dual-decoder Transformer for Joint Automatic Speech Recognition and Multilingual Speech Translation

1 code implementation COLING 2020 Hang Le, Juan Pino, Changhan Wang, Jiatao Gu, Didier Schwab, Laurent Besacier

We propose two variants of these architectures corresponding to two different levels of dependencies between the decoders, called the parallel and cross dual-decoder Transformers, respectively.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Emergence of Separable Manifolds in Deep Language Representations

1 code implementation ICML 2020 Jonathan Mamou, Hang Le, Miguel Del Rio, Cory Stephenson, Hanlin Tang, Yoon Kim, SueYeon Chung

In addition, we find that the emergence of linear separability in these manifolds is driven by a combined reduction of manifolds' radius, dimensionality and inter-manifold correlations.

The LIG system for the English-Czech Text Translation Task of IWSLT 2019

no code implementations EMNLP (IWSLT) 2019 Loïc Vial, Benjamin Lecouteux, Didier Schwab, Hang Le, Laurent Besacier

Therefore, we implemented a Transformer-based encoder-decoder neural system which is able to use the output of a pre-trained language model as input embeddings, and we compared its performance under three configurations: 1) without any pre-trained language model (constrained), 2) using a language model trained on the monolingual parts of the allowed English-Czech data (constrained), and 3) using a language model trained on a large quantity of external monolingual data (unconstrained).

Language Modelling Machine Translation +1

GraphDTA: prediction of drug–target binding affinity using graph convolutional networks

1 code implementation bioRxiv 2019 Thin Nguyen, Hang Le, Svetha Venkatesh

The results show that our proposed method can not only predict the affinity better than non-deep learning models, but also outperform competing deep learning approaches.

Drug Discovery Recommendation Systems

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