Now showing items 1-6 of 6
Pruning Deep Neural Networks with Net-Trim: Deep Learning and Compressed Sensing Meet
We introduce and analyze a new technique for model reduction in deep neural networks. Our algorithm prunes (sparsifies) a trained network layer-wise, removing connections at each layer by addressing a convex problem. We ...
Deep Learning to Learn
(Georgia Institute of Technology, 2018-08-20)
Reinforcement learning and imitation learning have seen success in many domains, including autonomous helicopter flight, Atari, simulated locomotion, Go, robotic manipulation. However, sample complexity of these methods ...
The Natural Language Decathlon: Multitask Learning as Question Answering
Deep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, ...
Reaching Beyond Human Accuracy With AI Datacenters
Deep learning has enabled rapid progress in diverse problems in vision, speech, healthcare, and beyond. This progress has been driven by breakthroughs in algorithms that can harness massive datasets and powerful compute ...
Few-shot Learning with Meta-Learning: Progress Made and Challenges Ahead
A lot of the recent progress on many AI tasks enabled in part by the availability of large quantities of labeled data. Yet, humans are able to learn concepts from as little as a handful of examples. Meta-learning is a very ...
Practical Applications of Signal Processing and Machine Learning in a Dynamic Retail Environment
The retail industry is the midst of rapid change due to intensifying competition from fragmented and non-traditional sources, expansion of assortment breadth and product availability, and more transparent pricing. Evolving ...