234 citations · 440 across the 31 of their papers we have counts for
20 papers · 1 filter
Greedy Modality Selection via Approximate Submodular Maximization
Runxiang Cheng, Gargi Balasubramaniam, Yifei He +2
Multimodal learning considers learning from multi-modality data, aiming to fuse heterogeneous sources of information. However, it is not always feasible to leverage all available m…
Conditional Contrastive Learning with Kernel
Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma +4
Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair cont…
Learning Weakly-Supervised Contrastive Representations
Yao-Hung Hubert Tsai, Tianqin Li, Weixin Liu +3
We argue that a form of the valuable information provided by the auxiliary information is its implied data clustering information. For instance, considering hashtags as auxiliary i…
Integrating Auxiliary Information in Self-supervised Learning
Yao-Hung Hubert Tsai, Tianqin Li, Weixin Liu +3
This paper presents to integrate the auxiliary information (e.g., additional attributes for data such as the hashtags for Instagram images) in the self-supervised learning process.…
Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning
Martin Q. Ma, Yao-Hung Hubert Tsai, Paul Pu Liang +4
Contrastive self-supervised learning (SSL) learns an embedding space that maps similar data pairs closer and dissimilar data pairs farther apart. Despite its success, one issue has…
A Note on Connecting Barlow Twins with Negative-Sample-Free Contrastive Learning
Yao-Hung Hubert Tsai, Shaojie Bai, Louis-Philippe Morency +1
In this report, we relate the algorithmic design of Barlow Twins' method to the Hilbert-Schmidt Independence Criterion (HSIC), thus establishing it as a contrastive learning approa…
Self-supervised Representation Learning with Relative Predictive Coding
Yao-Hung Hubert Tsai, Martin Q. Ma, Muqiao Yang +3
This paper introduces Relative Predictive Coding (RPC), a new contrastive representation learning objective that maintains a good balance among training stability, minibatch size s…
Unsupervised Domain Adaptation for Visual Navigation
Shangda Li, Devendra Singh Chaplot, Yao-Hung Hubert Tsai +3
Advances in visual navigation methods have led to intelligent embodied navigation agents capable of learning meaningful representations from raw RGB images and perform a wide varie…
Self-supervised Learning from a Multi-view Perspective
Yao-Hung Hubert Tsai, Yue Wu, Ruslan Salakhutdinov +1
As a subset of unsupervised representation learning, self-supervised representation learning adopts self-defined signals as supervision and uses the learned representation for down…
Neural Methods for Point-wise Dependency Estimation
Yao-Hung Hubert Tsai, Han Zhao, Makoto Yamada +2
Since its inception, the neural estimation of mutual information (MI) has demonstrated the empirical success of modeling expected dependency between high-dimensional random variabl…
Capsules with Inverted Dot-Product Attention Routing
Yao-Hung Hubert Tsai, Nitish Srivastava, Hanlin Goh +1
We introduce a new routing algorithm for capsule networks, in which a child capsule is routed to a parent based only on agreement between the parent's state and the child's vote. T…
Complex Transformer: A Framework for Modeling Complex-Valued Sequence
Muqiao Yang, Martin Q. Ma, Dongyu Li +2
While deep learning has received a surge of interest in a variety of fields in recent years, major deep learning models barely use complex numbers. However, speech, signal and audi…
Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel
Yao-Hung Hubert Tsai, Shaojie Bai, Makoto Yamada +2
Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequ…
Learning Neural Networks with Adaptive Regularization
Han Zhao, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov +1
Feed-forward neural networks can be understood as a combination of an intermediate representation and a linear hypothesis. While most previous works aim to diversify the representa…
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization
Paul Pu Liang, Zhun Liu, Yao-Hung Hubert Tsai +3
There has been an increased interest in multimodal language processing including multimodal dialog, question answering, sentiment analysis, and speech recognition. However, natural…
Learning Factorized Multimodal Representations
Yao-Hung Hubert Tsai, Paul Pu Liang, Amir Zadeh +2
Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information. Although the presence of multi…
Selecting the Best in GANs Family: a Post Selection Inference Framework
Yao-Hung Hubert Tsai, Makoto Yamada, Denny Wu +3
"Which Generative Adversarial Networks (GANs) generates the most plausible images?" has been a frequently asked question among researchers. To address this problem, we first propos…
Learning Markov Chain in Unordered Dataset
Yao-Hung Hubert Tsai, Han Zhao, Ruslan Salakhutdinov +1
The assumption that data samples are independently identically distributed is the backbone of many learning algorithms. Nevertheless, datasets often exhibit rich structure in pract…
Improving One-Shot Learning through Fusing Side Information
Yao-Hung Hubert Tsai, Ruslan Salakhutdinov
Deep Neural Networks (DNNs) often struggle with one-shot learning where we have only one or a few labeled training examples per category. In this paper, we argue that by using side…
Generative-Discriminative Variational Model for Visual Recognition
Chih-Kuan Yeh, Yao-Hung Hubert Tsai, Yu-Chiang Frank Wang
The paradigm shift from shallow classifiers with hand-crafted features to end-to-end trainable deep learning models has shown significant improvements on supervised learning tasks.…