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Found 7 papers, 4 papers with code
ArctyrEX : Accelerated Encrypted Execution of General-Purpose Applications
Fully Homomorphic Encryption (FHE) is a cryptographic method that guarantees the privacy and security of user data during computation.
Understanding the Effect of the Long Tail on Neural Network Compression
E. g., it has been shown that mismatches between the full and compressed models can be biased towards under-represented classes.
Efficient Sparsely Activated Transformers
Transformer-based neural networks have achieved state-of-the-art task performance in a number of machine learning domains including natural language processing and computer vision.
Going Beyond Classification Accuracy Metrics in Model Compression
With the rise in edge-computing devices, there has been an increasing demand to deploy energy and resource-efficient models.
A Programmable Approach to Neural Network Compression
Deep neural networks (DNNs) frequently contain far more weights, represented at a higher precision, than are required for the specific task which they are trained to perform.
Accelerating Reinforcement Learning through GPU Atari Emulation
We introduce CuLE (CUDA Learning Environment), a CUDA port of the Atari Learning Environment (ALE) which is used for the development of deep reinforcement algorithms.
AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks
Training deep neural networks with Stochastic Gradient Descent, or its variants, requires careful choice of both learning rate and batch size.
