Search Results for author:
Found 8 papers, 4 papers with code
Phenaki: Variable Length Video Generation From Open Domain Textual Description
To the best of our knowledge, this is the first time a paper studies generating videos from time variable prompts.
Ranked #4 on
Video Prediction
on BAIR Robot Pushing
Adaptive Optimization with Examplewise Gradients
We propose a new, more general approach to the design of stochastic gradient-based optimization methods for machine learning.
HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models
We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model.
Gaussian Mean Field Regularizes by Limiting Learned Information
Variational inference with a factorized Gaussian posterior estimate is a widely used approach for learning parameters and hidden variables.
Modular Networks: Learning to Decompose Neural Computation
Scaling model capacity has been vital in the success of deep learning.
Noisy Information Bottlenecks for Generalization
We propose Noisy Information Bottlenecks (NIB) to limit mutual information between learned parameters and the data through noise.
Stochastic Variational Optimization
These approaches are of particular interest because they are parallelizable.
Transfer Learning for Speech Recognition on a Budget
End-to-end training of automated speech recognition (ASR) systems requires massive data and compute resources.
