Yao-Hung Hubert Tsai

research

∙ 12/09/2022

Object Goal Navigation with End-to-End Self-Supervision

A household robot should be able to navigate to target locations without...

0 So Yeon Min, et al. ∙

research

∙ 10/22/2022

Greedy Modality Selection via Approximate Submodular Maximization

Multimodal learning considers learning from multi-modality data, aiming ...

0 Runxiang Cheng, et al. ∙

research

∙ 09/27/2022

Towards Multimodal Multitask Scene Understanding Models for Indoor Mobile Agents

The perception system in personalized mobile agents requires developing ...

3 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 09/25/2022

Paraphrasing Is All You Need for Novel Object Captioning

Novel object captioning (NOC) aims to describe images containing objects...

3 Cheng-Fu Yang, et al. ∙

research

∙ 07/22/2022

Scale dependant layer for self-supervised nuclei encoding

Recent developments in self-supervised learning give us the possibility ...

0 Peter Naylor, et al. ∙

research

∙ 02/14/2022

Learning Weakly-Supervised Contrastive Representations

We argue that a form of the valuable information provided by the auxilia...

16 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 02/11/2022

Conditional Contrastive Learning with Kernel

Conditional contrastive learning frameworks consider the conditional sam...

10 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 06/14/2021

HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units

Self-supervised approaches for speech representation learning are challe...

0 Wei-Ning Hsu, et al. ∙

research

∙ 06/05/2021

Integrating Auxiliary Information in Self-supervised Learning

This paper presents to integrate the auxiliary information (e.g., additi...

2 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 06/05/2021

Conditional Contrastive Learning: Removing Undesirable Information in Self-Supervised Representations

Self-supervised learning is a form of unsupervised learning that leverag...

44 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 04/28/2021

A Note on Connecting Barlow Twins with Negative-Sample-Free Contrastive Learning

In this report, we relate the algorithmic design of Barlow Twins' method...

8 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 03/21/2021

Self-supervised Representation Learning with Relative Predictive Coding

This paper introduces Relative Predictive Coding (RPC), a new contrastiv...

32 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 10/27/2020

Unsupervised Domain Adaptation for Visual Navigation

Advances in visual navigation methods have led to intelligent embodied n...

5 Shangda Li, et al. ∙

research

∙ 06/10/2020

Demystifying Self-Supervised Learning: An Information-Theoretical Framework

Self-supervised representation learning adopts self-defined signals as s...

22 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 06/09/2020

Neural Methods for Point-wise Dependency Estimation

Since its inception, the neural estimation of mutual information (MI) ha...

13 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 05/25/2020

Feature Robust Optimal Transport for High-dimensional Data

Optimal transport is a machine learning technique with applications incl...

13 Mathis Petrovich, et al. ∙

research

∙ 04/29/2020

Interpretable Multimodal Routing for Human Multimodal Language

The human language has heterogeneous sources of information, including t...

0 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 02/12/2020

Capsules with Inverted Dot-Product Attention Routing

We introduce a new routing algorithm for capsule networks, in which a ch...

5 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 10/22/2019

Complex Transformer: A Framework for Modeling Complex-Valued Sequence

While deep learning has received a surge of interest in a variety of fie...

10 Muqiao Yang, et al. ∙

research

∙ 09/05/2019

LSMI-Sinkhorn: Semi-supervised Squared-Loss Mutual Information Estimation with Optimal Transport

Estimating mutual information is an important machine learning and stati...

6 Yanbin Liu, et al. ∙

research

∙ 08/30/2019

Transformer Dissection: An Unified Understanding for Transformer's Attention via the Lens of Kernel

Transformer is a powerful architecture that achieves superior performanc...

32 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 07/14/2019

Learning Neural Networks with Adaptive Regularization

Feed-forward neural networks can be understood as a combination of an in...

3 Han Zhao, et al. ∙

research

∙ 07/01/2019

Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization

There has been an increased interest in multimodal language processing i...

17 Paul Pu Liang, et al. ∙

research

∙ 06/01/2019

Multimodal Transformer for Unaligned Multimodal Language Sequences

Human language is often multimodal, which comprehends a mixture of natur...

0 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 05/14/2019

Strong and Simple Baselines for Multimodal Utterance Embeddings

Human language is a rich multimodal signal consisting of spoken words, f...

0 Paul Pu Liang, et al. ∙

research

∙ 03/25/2019

Video Relationship Reasoning using Gated Spatio-Temporal Energy Graph

Visual relationship reasoning is a crucial yet challenging task for unde...

0 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 06/16/2018

Learning Factorized Multimodal Representations

Learning representations of multimodal data is a fundamentally complex r...

0 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 02/17/2018

Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator

Measuring divergence between two distributions is essential in machine l...

0 Makoto Yamada, et al. ∙

research

∙ 02/15/2018

Selecting the Best in GANs Family: a Post Selection Inference Framework

"Which Generative Adversarial Networks (GANs) generates the most plausib...

0 Yao-Hung Hubert Tsai, et al. ∙

research

∙ 02/15/2018

"Dependency Bottleneck" in Auto-encoding Architectures: an Empirical Study

Recent works investigated the generalization properties in deep neural n...

0 Denny Wu, et al. ∙

research

∙ 03/17/2017

Learning Robust Visual-Semantic Embeddings

Many of the existing methods for learning joint embedding of images and ...

0 Yao-Hung Hubert Tsai, et al. ∙

Yao-Hung Hubert Tsai

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