no code implementations • 27 Jan 2024 • Hung Bui, Harikrishna Warrier, Yogesh Gupta
Medical Information Mart for Intensive Care (MIMIC) dataset is a popular, public, and free EHR dataset in a raw format that has been used in numerous studies.
1 code implementation • 6 Nov 2023 • Dat Quoc Nguyen, Linh The Nguyen, Chi Tran, Dung Ngoc Nguyen, Dinh Phung, Hung Bui
The base model, PhoGPT-4B, with exactly 3. 7B parameters, is pre-trained from scratch on a Vietnamese corpus of 102B tokens, with an 8192 context length, employing a vocabulary of 20480 token types.
no code implementations • NeurIPS 2021 • Trung Phung, Trung Le, Long Vuong, Toan Tran, Anh Tran, Hung Bui, Dinh Phung
Domain adaptation (DA) benefits from the rigorous theoretical works that study its insightful characteristics and various aspects, e. g., learning domain-invariant representations and its trade-off.
no code implementations • 29 Oct 2021 • Trung Le, Dat Do, Tuan Nguyen, Huy Nguyen, Hung Bui, Nhat Ho, Dinh Phung
We study the label shift problem between the source and target domains in general domain adaptation (DA) settings.
no code implementations • 29 Oct 2021 • Dang Nguyen, Trang Nguyen, Khai Nguyen, Dinh Phung, Hung Bui, Nhat Ho
To address this issue, we propose a novel model fusion framework, named CLAFusion, to fuse neural networks with a different number of layers, which we refer to as heterogeneous neural networks, via cross-layer alignment.
3 code implementations • 14 Jun 2021 • Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
no code implementations • NeurIPS 2021 • Son Nguyen, Duong Nguyen, Khai Nguyen, Khoat Than, Hung Bui, Nhat Ho
Approximate inference in Bayesian deep networks exhibits a dilemma of how to yield high fidelity posterior approximations while maintaining computational efficiency and scalability.
no code implementations • NeurIPS 2021 • Khang Le, Huy Nguyen, Quang Nguyen, Tung Pham, Hung Bui, Nhat Ho
We consider robust variants of the standard optimal transport, named robust optimal transport, where marginal constraints are relaxed via Kullback-Leibler divergence.
2 code implementations • 11 Feb 2021 • Khai Nguyen, Dang Nguyen, Quoc Nguyen, Tung Pham, Hung Bui, Dinh Phung, Trung Le, Nhat Ho
To address these problems, we propose a novel mini-batch scheme for optimal transport, named Batch of Mini-batches Optimal Transport (BoMb-OT), that finds the optimal coupling between mini-batches and it can be seen as an approximation to a well-defined distance on the space of probability measures.
no code implementations • 1 Jan 2021 • Toan Tran, Hieu Vu, Gustavo Carneiro, Hung Bui
Label noise is a natural event of data collection and annotation and has been shown to have significant impact on the performance of deep learning models regarding accuracy reduction and sample complexity increase.
no code implementations • 1 Jan 2021 • Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
no code implementations • 21 Dec 2020 • Anh Tong, Toan Tran, Hung Bui, Jaesik Choi
Choosing a proper set of kernel functions is an important problem in learning Gaussian Process (GP) models since each kernel structure has different model complexity and data fitness.
2 code implementations • ICLR 2021 • Khai Nguyen, Son Nguyen, Nhat Ho, Tung Pham, Hung Bui
To improve the discrepancy and consequently the relational regularization, we propose a new relational discrepancy, named spherical sliced fused Gromov Wasserstein (SSFG), that can find an important area of projections characterized by a von Mises-Fisher distribution.
no code implementations • CVPR 2020 • Chi Nhan Duong, Thanh-Dat Truong, Kha Gia Quach, Hung Bui, Kaushik Roy, Khoa Luu
Unveiling face images of a subject given his/her high-level representations extracted from a blackbox Face Recognition engine is extremely challenging.
1 code implementation • ICLR 2021 • Khai Nguyen, Nhat Ho, Tung Pham, Hung Bui
Sliced-Wasserstein distance (SW) and its variant, Max Sliced-Wasserstein distance (Max-SW), have been used widely in the recent years due to their fast computation and scalability even when the probability measures lie in a very high dimensional space.
1 code implementation • ICML 2020 • Khiem Pham, Khang Le, Nhat Ho, Tung Pham, Hung Bui
We provide a computational complexity analysis for the Sinkhorn algorithm that solves the entropic regularized Unbalanced Optimal Transport (UOT) problem between two measures of possibly different masses with at most $n$ components.
1 code implementation • 19 Sep 2019 • Viet Huynh, Nhat Ho, Nhan Dam, XuanLong Nguyen, Mikhail Yurochkin, Hung Bui, and Dinh Phung
We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured corpus of data.
1 code implementation • ICLR 2020 • Nir Levine, Yin-Lam Chow, Rui Shu, Ang Li, Mohammad Ghavamzadeh, Hung Bui
A promising approach is to embed the high-dimensional observations into a lower-dimensional latent representation space, estimate the latent dynamics model, then utilize this model for control in the latent space.
no code implementations • 15 Nov 2018 • Trung Le, Khanh Nguyen, Nhat Ho, Hung Bui, Dinh Phung
The underlying idea of deep domain adaptation is to bridge the gap between source and target domains in a joint space so that a supervised classifier trained on labeled source data can be nicely transferred to the target domain.
no code implementations • NAACL 2018 • Quan Hung Tran, Tuan Lai, Gholamreza Haffari, Ingrid Zukerman, Trung Bui, Hung Bui
Contextual sequence mapping is one of the fundamental problems in Natural Language Processing (NLP).
no code implementations • 15 Oct 2017 • Ershad Banijamali, Rui Shu, Mohammad Ghavamzadeh, Hung Bui, Ali Ghodsi
We also propose a principled variational approximation of the embedding posterior that takes the future observation into account, and thus, makes the variational approximation more robust against the noise.
no code implementations • 21 Sep 2017 • Tong Yu, Branislav Kveton, Zheng Wen, Hung Bui, Ole J. Mengshoel
We study the problem of learning a latent variable model from a stream of data.
no code implementations • 9 Feb 2016 • Branislav Kveton, Hung Bui, Mohammad Ghavamzadeh, Georgios Theocharous, S. Muthukrishnan, Siqi Sun
Graphical models are a popular approach to modeling structured data but they are unsuitable for high-cardinality variables.
no code implementations • 6 Aug 2014 • Truyen Tran, Hung Bui, Svetha Venkatesh
We explore a framework called boosted Markov networks to combine the learning capacity of boosting and the rich modeling semantics of Markov networks and applying the framework for video-based activity recognition.
no code implementations • 6 Aug 2014 • Truyen Tran, Hung Bui, Svetha Venkatesh
Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance.
no code implementations • 26 Sep 2013 • Hung Bui, Tuyen Huynh, Sebastian Riedel
This automorphism group provides a precise mathematical framework for lifted inference in the general exponential family.