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Found 7 papers, 2 papers with code
Masked prediction tasks: a parameter identifiability view
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.
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation
Noise-contrastive estimation (NCE) is a statistically consistent method for learning unnormalized probabilistic models.
Contrastive learning of strong-mixing continuous-time stochastic processes
Contrastive learning is a family of self-supervised methods where a model is trained to solve a classification task constructed from unlabeled data.
Generalized Boosting
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.
Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction
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.
Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos
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.
Learning to Decompose and Disentangle Representations for Video Prediction
Our goal is to predict future video frames given a sequence of input frames.
