Zobrazeno 1 - 10 of 398 pro vyhledávání: '"Martin Q"'
1
Report
Understanding Masked Autoencoders via Hierarchical Latent Variable Models
Autor: Kong, Lingjing, Ma, Martin Q., Chen, Guangyi, Xing, Eric P., Chi, Yuejie, Morency, Louis-Philippe, Zhang, Kun
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 a variety of vision tasks. Despite the emergence of intriguing empiric
Externí odkaz: http://arxiv.org/abs/2306.04898
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2
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Face-to-Face Contrastive Learning for Social Intelligence Question-Answering
Autor: Wilf, Alex, Ma, Martin Q., Liang, Paul Pu, Zadeh, Amir, Morency, Louis-Philippe
Creating artificial social intelligence - algorithms that can understand the nuances of multi-person interactions - is an exciting and emerging challenge in processing facial expressions and gestures from multimodal videos. Recent multimodal methods
Externí odkaz: http://arxiv.org/abs/2208.01036
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3
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Conditional Contrastive Learning with Kernel
Autor: Tsai, Yao-Hung Hubert, Li, Tianqin, Ma, Martin Q., Zhao, Han, Zhang, Kun, Morency, Louis-Philippe, Salakhutdinov, Ruslan
Conditional contrastive learning frameworks consider the conditional sampling procedure that constructs positive or negative data pairs conditioned on specific variables. Fair contrastive learning constructs negative pairs, for example, from the same
Externí odkaz: http://arxiv.org/abs/2202.05458
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5
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Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning
Autor: Ma, Martin Q., Tsai, Yao-Hung Hubert, Liang, Paul Pu, Zhao, Han, Zhang, Kun, Salakhutdinov, Ruslan, Morency, Louis-Philippe
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 been overlooked: the fairness aspect of representations learned using
Externí odkaz: http://arxiv.org/abs/2106.02866
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6
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A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?
Autor: Ma, Martin Q., Zhao, Yue, Zhang, Xiaorong, Akoglu, Leman
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 notoriously difficult, in the absence of hold-out validation data with
Externí odkaz: http://arxiv.org/abs/2104.01422
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7
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Self-supervised Representation Learning with Relative Predictive Coding
Autor: Tsai, Yao-Hung Hubert, Ma, Martin Q., Yang, Muqiao, Zhao, Han, Morency, Louis-Philippe, Salakhutdinov, Ruslan
This paper introduces Relative Predictive Coding (RPC), a new contrastive representation learning objective that maintains a good balance among training stability, minibatch size sensitivity, and downstream task performance. The key to the success of
Externí odkaz: http://arxiv.org/abs/2103.11275
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8
Report
Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis
Autor: Tsai, Yao-Hung Hubert, Ma, Martin Q., Yang, Muqiao, Salakhutdinov, Ruslan, Morency, Louis-Philippe
The human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language. Recent multimodal learning with strong performances on human-centric tasks such as sentim
Externí odkaz: http://arxiv.org/abs/2004.14198
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9
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