1 code implementation • 19 Feb 2024 • Thanh Le-Cong, Dat Nguyen, Bach Le, Toby Murray
In this paper, we investigate the naturalness of semantic-preserving transformations and their impacts on the evaluation of NPR.
no code implementations • 24 Jan 2024 • Dat Nguyen, Nesryne Mejri, Inder Pal Singh, Polina Kuleshova, Marcella Astrid, Anis Kacem, Enjie Ghorbel, Djamila Aouada
Second, an Enhanced Feature Pyramid Network (E-FPN) is proposed as a simple and effective mechanism for spreading discriminative low-level features into the final feature output, with the advantage of limiting redundancy.
no code implementations • 8 Jan 2024 • Dat Nguyen, Hieu M. Vu, Cong-Thanh Le, Bach Le, David Lo, ThanhVu Nguyen, Corina Pasareanu
To tackle the challenge of varying input structures in GNNs, GNNInfer first identifies a set of representative influential structures that contribute significantly towards the prediction of a GNN.
1 code implementation • 7 Jan 2024 • Chau Nguyen, Phuong Nguyen, Thanh Tran, Dat Nguyen, An Trieu, Tin Pham, Anh Dang, Le-Minh Nguyen
The Competition on Legal Information Extraction/Entailment (COLIEE) is held annually to encourage advancements in the automatic processing of legal texts.
no code implementations • 4 Nov 2022 • Hieu Nguyen Van, Dat Nguyen, Phuong Minh Nguyen, Minh Le Nguyen
We introduce efficient deep learning-based methods for legal document processing including Legal Document Retrieval and Legal Question Answering tasks in the Automated Legal Question Answering Competition (ALQAC 2022).
1 code implementation • 18 Apr 2022 • Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, Noah Goodman
Models can fail in unpredictable ways during deployment due to task ambiguity, when multiple behaviors are consistent with the provided training data.
no code implementations • 29 Sep 2021 • Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, Noah Goodman
An important barrier to the safe deployment of machine learning systems is the risk of \emph{task ambiguity}, where multiple behaviors are consistent with the provided examples.
1 code implementation • 7 Jun 2021 • Ines Chami, Albert Gu, Dat Nguyen, Christopher Ré
Given directions, PCA relies on: (1) a parameterization of subspaces spanned by these directions, (2) a method of projection onto subspaces that preserves information in these directions, and (3) an objective to optimize, namely the variance explained by projections.