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Found 4 papers, 2 papers with code
Distilling the Knowledge from Conditional Normalizing Flows
Normalizing flows are a powerful class of generative models demonstrating strong performance in several speech and vision problems.
Free-Lunch Saliency via Attention in Atari Agents
We show experimentally that a network with an FLS module exhibits performance similar to the baseline (i. e., it is "free", with no performance cost) and can be used as a drop-in replacement for reinforcement learning agents.
Learning State Representations in Complex Systems with Multimodal Data
Representation learning becomes especially important for complex systems with multimodal data sources such as cameras or sensors.
Label Denoising with Large Ensembles of Heterogeneous Neural Networks
Despite recent advances in computer vision based on various convolutional architectures, video understanding remains an important challenge.
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