Search Results for author: Hung Bui

Found 26 papers, 8 papers with code

Benchmarking with MIMIC-IV, an irregular, spare clinical time series dataset

no code implementations27 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.

Benchmarking Time Series

PhoGPT: Generative Pre-training for Vietnamese

1 code implementation6 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.

Instruction Following

On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources

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.

Domain Generalization Transfer Learning

On Label Shift in Domain Adaptation via Wasserstein Distance

no code implementations29 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.

Domain Adaptation

On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks

no code implementations29 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.

Knowledge Distillation Model Compression

Temporal Predictive Coding For Model-Based Planning In Latent Space

3 code implementations14 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.

Model-based Reinforcement Learning Representation Learning

Structured Dropout Variational Inference for Bayesian Neural Networks

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.

Bayesian Inference Computational Efficiency +2

On Robust Optimal Transport: Computational Complexity and Barycenter Computation

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.

On Transportation of Mini-batches: A Hierarchical Approach

2 code implementations11 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.

Domain Adaptation

Bayesian Metric Learning for Robust Training of Deep Models under Noisy Labels

no code implementations1 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.

General Classification Metric Learning +1

Non-Markovian Predictive Coding For Planning In Latent Space

no code implementations1 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.

Model-based Reinforcement Learning Representation Learning

Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior

no code implementations21 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.

Gaussian Processes Time Series +1

Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein

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.

Image Generation

Vec2Face: Unveil Human Faces from their Blackbox Features in Face Recognition

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.

Benchmarking Face Recognition +2

Distributional Sliced-Wasserstein and Applications to Generative Modeling

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.

Informativeness

On Unbalanced Optimal Transport: An Analysis of Sinkhorn Algorithm

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.

On Efficient Multilevel Clustering via Wasserstein Distances

1 code implementation19 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.

Clustering

Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control

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.

Decision Making Open-Ended Question Answering +1

On Deep Domain Adaptation: Some Theoretical Understandings

no code implementations15 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.

Domain Adaptation Transfer Learning

Robust Locally-Linear Controllable Embedding

no code implementations15 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.

Graphical Model Sketch

no code implementations9 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.

Boosted Markov Networks for Activity Recognition

no code implementations6 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.

Activity Recognition feature selection +1

Human Activity Learning and Segmentation using Partially Hidden Discriminative Models

no code implementations6 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.

Automorphism Groups of Graphical Models and Lifted Variational Inference

no code implementations26 Sep 2013 Hung Bui, Tuyen Huynh, Sebastian Riedel

This automorphism group provides a precise mathematical framework for lifted inference in the general exponential family.

Variational Inference

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