Search Results for author: Bingbin Liu

Found 7 papers, 2 papers with code

Masked prediction tasks: a parameter identifiability view

no code implementations18 Feb 2022 Bingbin Liu, Daniel Hsu, Pradeep Ravikumar, Andrej Risteski

This lens is undoubtedly very interesting, but suffers from the problem that there isn't a "canonical" set of downstream tasks to focus on -- in practice, this problem is usually resolved by competing on the benchmark dataset du jour.

Self-Supervised Learning

Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation

no code implementations ICLR 2022 Bingbin Liu, Elan Rosenfeld, Pradeep Ravikumar, Andrej Risteski

Noise-contrastive estimation (NCE) is a statistically consistent method for learning unnormalized probabilistic models.

Contrastive learning of strong-mixing continuous-time stochastic processes

no code implementations3 Mar 2021 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.

Contrastive Learning Time Series

Generalized Boosting

no code implementations NeurIPS 2020 Arun Suggala, Bingbin Liu, Pradeep Ravikumar

Using thorough empirical evaluation, we show that our learning algorithms have superior performance over traditional additive boosting algorithms, as well as existing greedy learning techniques for DNNs.

Additive models Classification +2

Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction

1 code implementation20 Feb 2020 Bingbin Liu, Ehsan Adeli, Zhangjie Cao, Kuan-Hui Lee, Abhijeet Shenoi, Adrien Gaidon, Juan Carlos Niebles

In addition, we introduce a new dataset designed specifically for autonomous-driving scenarios in areas with dense pedestrian populations: the Stanford-TRI Intent Prediction (STIP) dataset.

Autonomous Driving

Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos

no code implementations ECCV 2018 Bingbin Liu, Serena Yeung, Edward Chou, De-An Huang, Li Fei-Fei, Juan Carlos Niebles

A major challenge in computer vision is scaling activity understanding to the long tail of complex activities without requiring collecting large quantities of data for new actions.

Video Retrieval

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