18 citations · 57 across the 10 of their papers we have counts for
8 papers · 1 filter
Understanding Masked Autoencoders via Hierarchical Latent Variable Models
Lingjing Kong, Martin Q. Ma, Guangyi Chen +4
Masked autoencoder (MAE), a simple and effective self-supervised learning framework based on the reconstruction of masked image regions, has recently achieved prominent success in…
Factorized Contrastive Learning: Going Beyond Multi-view Redundancy
Paul Pu Liang, Zihao Deng, Martin Ma +3
In a wide range of multimodal tasks, contrastive learning has become a particularly appealing approach since it can successfully learn representations from abundant unlabeled data…
Face-to-Face Contrastive Learning for Social Intelligence Question-Answering
Alex Wilf, Martin Q. Ma, Paul Pu Liang +2
Creating artificial social intelligence - algorithms that can understand the nuances of multi-person interactions - is an exciting and emerging challenge in processing facial expre…
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…
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 Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?
Martin Q. Ma, Yue Zhao, Xiaorong Zhang +1
Given an unsupervised outlier detection task, how should one select a detection algorithm as well as its hyperparameters (jointly called a model)? Unsupervised model selection is n…
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…
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…