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Found 6 papers, 2 papers with code
Monarch: Expressive Structured Matrices for Efficient and Accurate Training
To address these issues, we propose a class of matrices (Monarch) that is hardware-efficient (they are parameterized as products of two block-diagonal matrices for better hardware utilization) and expressive (they can represent many commonly used transforms).
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
Machine learning models are known to perpetuate and even amplify the biases present in the data.
Spoken Moments: Learning Joint Audio-Visual Representations from Video Descriptions
With this in mind, the descriptions people generate for videos of different dynamic events can greatly improve our understanding of the key information of interest in each video.
Routing with Self-Attention for Multimodal Capsule Networks
We present a new multimodal capsule network that allows us to leverage the strength of capsules in the context of a multimodal learning framework on large amounts of video data.
Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation
As such, it calls for different pre-training methods for specific scenarios, and the pre-trained models are likely to be limited by their universality and representation quality.
Anticipating Driving Behavior through Deep Learning-Based Policy Prediction
In this endeavor, we developed a comprehensive system that processes integrated visual features derived from video frames captured by a regular camera, along with depth details obtained from a point cloud scanner.
