Search Results for author: Tung Nguyen

Found 19 papers, 9 papers with code

Image Colorization Using a Deep Convolutional Neural Network

no code implementations27 Apr 2016 Tung Nguyen, Kazuki Mori, Ruck Thawonmas

In this paper, we present a novel approach that uses deep learning techniques for colorizing grayscale images.

Colorization General Classification +4

DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks

1 code implementation27 Jul 2019 Simon Wiedemann, Heiner Kirchoffer, Stefan Matlage, Paul Haase, Arturo Marban, Talmaj Marinc, David Neumann, Tung Nguyen, Ahmed Osman, Detlev Marpe, Heiko Schwarz, Thomas Wiegand, Wojciech Samek

The field of video compression has developed some of the most sophisticated and efficient compression algorithms known in the literature, enabling very high compressibility for little loss of information.

Neural Network Compression Quantization +1

Predictive Coding for Locally-Linear Control

1 code implementation ICML 2020 Rui Shu, Tung Nguyen, Yin-Lam Chow, Tuan Pham, Khoat Than, Mohammad Ghavamzadeh, Stefano Ermon, Hung H. Bui

High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks.

Decision Making

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

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

Tensor Completion with Provable Consistency and Fairness Guarantees for Recommender Systems

no code implementations4 Apr 2022 Tung Nguyen, Jeffrey Uhlmann

We introduce a new consistency-based approach for defining and solving nonnegative/positive matrix and tensor completion problems.

Fairness Recommendation Systems

A Simple and Scalable Tensor Completion Algorithm via Latent Invariant Constraint for Recommendation System

no code implementations27 Jun 2022 Tung Nguyen, Sang T. Truong, Jeffrey Uhlmann

In this paper we provide a latent-variable formulation and solution to the recommender system (RS) problem in terms of a fundamental property that any reasonable solution should be expected to satisfy.

Recommendation Systems Tensor Decomposition

Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling

1 code implementation9 Jul 2022 Tung Nguyen, Aditya Grover

We propose Transformer Neural Processes (TNPs), a new member of the NP family that casts uncertainty-aware meta learning as a sequence modeling problem.

Bayesian Optimization Decision Making +3

Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning

1 code implementation11 Oct 2022 Tung Nguyen, Qinqing Zheng, Aditya Grover

We study CWBC in the context of RvS (Emmons et al., 2021) and Decision Transformers (Chen et al., 2021), and show that CWBC significantly boosts their performance on various benchmarks.

Offline RL reinforcement-learning +1

Machine Learning Approach to Polymerization Reaction Engineering: Determining Monomers Reactivity Ratios

no code implementations3 Jan 2023 Tung Nguyen, Mona Bavarian

Here, we demonstrate how machine learning enables the prediction of comonomers reactivity ratios based on the molecular structure of monomers.

Graph Attention Multi-Task Learning

ClimaX: A foundation model for weather and climate

1 code implementation24 Jan 2023 Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K. Gupta, Aditya Grover

We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings.

Self-Supervised Learning Weather Forecasting

Utilization of domain knowledge to improve POMDP belief estimation

no code implementations17 Feb 2023 Tung Nguyen, Johane Takeuchi

The partially observable Markov decision process (POMDP) framework is a common approach for decision making under uncertainty.

Decision Making Decision Making Under Uncertainty

ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling

1 code implementation NeurIPS 2023 Tung Nguyen, Jason Jewik, Hritik Bansal, Prakhar Sharma, Aditya Grover

Modeling weather and climate is an essential endeavor to understand the near- and long-term impacts of climate change, as well as inform technology and policymaking for adaptation and mitigation efforts.

Benchmarking Weather Forecasting

An Admissible Shift-Consistent Method for Recommender Systems

no code implementations17 Jul 2023 Tung Nguyen, Jeffrey Uhlmann

In this paper, we propose a new constraint, called shift-consistency, for solving matrix/tensor completion problems in the context of recommender systems.

Fairness Imputation +1

Imposing Consistency Properties on Blackbox Systems with Applications to SVD-Based Recommender Systems

no code implementations17 Jul 2023 Tung Nguyen, Jeffrey Uhlmann

In this paper we discuss pre- and post-processing methods to induce desired consistency and/or invariance properties in blackbox systems, e. g., AI-based.

Fairness Matrix Completion +1

ExPT: Synthetic Pretraining for Few-Shot Experimental Design

1 code implementation NeurIPS 2023 Tung Nguyen, Sudhanshu Agrawal, Aditya Grover

In this work, we address the more challenging yet realistic setting of few-shot experimental design, where only a few labeled data points of input designs and their corresponding values are available.

Experimental Design In-Context Learning

A Study on Social Robot Behavior in Group Conversation

no code implementations19 Dec 2023 Tung Nguyen, Eric Nichols, Randy Gomez

Recently, research in human-robot interaction began to consider a robot's influence at the group level.

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