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Found 5 papers, 2 papers with code
Hierarchical Policy Learning is Sensitive to Goal Space Design
Hierarchy in reinforcement learning agents allows for control at multiple time scales yielding improved sample efficiency, the ability to deal with long time horizons and transferability of sub-policies to tasks outside the training distribution.
Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning
The current approach, which we call "direct optimization", requires deep changes within each framework to improve the training performance for each hardware backend (CPUs, GPUs, FPGAs, ASICs) and requires $\mathcal{O}(fp)$ effort; where $f$ is the number of frameworks and $p$ is the number of platforms.
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
Here we present the Flexpoint data format, aiming at a complete replacement of 32-bit floating point format training and inference, designed to support modern deep network topologies without modifications.
Discovering Hidden Factors of Variation in DeepNetworks
Deep learning has enjoyed a great deal of success because of its ability to learnuseful features for tasks such as classification.
Discovering Hidden Factors of Variation in Deep Networks
Deep learning has enjoyed a great deal of success because of its ability to learn useful features for tasks such as classification.
