A zero-shot RL agent is an agent that can solve any RL task in a given environment, instantly with no additional planning or learning, after an initial reward-free learning phase.
Model comparison and ablation analyses show that these performances directly benefit from our original design choices, namely the use of (i) a contrastive objective, (ii) pretrained representations of speech and (iii) a common convolutional architecture simultaneously trained across several participants.
Machine learning has invaded various domains of computer science, including black-box optimization.
In most pragmatic settings, data augmentation and regularization are essential, and require hyperparameter search.
It compares favorably to a baseline that does not change those hyperparameters over the course of training, with an 8% relative WER improvement.
We design a simple optimization method to find the optimal latent parameters corresponding to the closest generation to any input inspirational image.
In this article, we show how a sparse NMF algorithm coined non-negative generalized morphological component analysis (nGMCA) can be extended to impose non-negativity in the direct domain along with sparsity in a transformed domain, with both analysis and synthesis formulations.