10 citations · 10 across the 4 of their papers we have counts for
5 papers
Masked prediction tasks: a parameter identifiability view
Bingbin Liu, Daniel Hsu, Pradeep Ravikumar +1
The vast majority of work in self-supervised learning, both theoretical and empirical (though mostly the latter), have largely focused on recovering good features for downstream ta…
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation
Bingbin Liu, Elan Rosenfeld, Pradeep Ravikumar +1
Noise-contrastive estimation (NCE) is a statistically consistent method for learning unnormalized probabilistic models. It has been empirically observed that the choice of the nois…
Contrastive learning of strong-mixing continuous-time stochastic processes
Bingbin Liu, Pradeep Ravikumar, Andrej Risteski
Contrastive learning is a family of self-supervised methods where a model is trained to solve a classification task constructed from unlabeled data. It has recently emerged as one…
Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction
Bingbin Liu, Ehsan Adeli, Zhangjie Cao +4
Reasoning over visual data is a desirable capability for robotics and vision-based applications. Such reasoning enables forecasting of the next events or actions in videos. In rece…
Learning to Decompose and Disentangle Representations for Video Prediction
Jun-Ting Hsieh, Bingbin Liu, De-An Huang +2
Our goal is to predict future video frames given a sequence of input frames. Despite large amounts of video data, this remains a challenging task because of the high-dimensionality…