Intel AI Research is pushing the limits of artificial intelligence and computing at every level, from atomic physics to data-center orchestration. We make big bets and take a systems view of AI: our research spans foundational work in machine learning algorithms and computer architecture to applied research in computer vision, autonomous driving, and distributed learning systems.
Deep Learning Optimization for Edge Devices: Analysis of Training Quantization Parameters
Reinforcement Learning Coach is an an open source research framework for training and evaluating reinforcement learning (RL) agents that uses the processing power of multi-core CPUs to enable efficient training of RL agents.
Network compression can reduce the memory footprint of a neural network, increase its inference speed and save energy. Distiller provides a PyTorch environment for prototyping and analyzing compression algorithms, such as sparsity-inducing methods and low-precision arithmetic.
Natural Language Processing (NLP) Architect is an open-source Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding. It is intended to be a platform for future research and collaboration.