Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing
Export Citations
4 Results
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
Searched The ACM Full-Text Collection (691,749 records)|Expand your search to The ACM Guide to Computing Literature (3,482,418 records)
Showing 1 - 4of4 Results
Select All
- research-articleJuly 2018
DeepMimic: example-guided deep reinforcement learning of physics-based character skills
ACM Transactions on Graphics (TOG), Volume 37, Issue 4August 2018, Article No.: 143, pp 1–14https://doi.org/10.1145/3197517.3201311A longstanding goal in character animation is to combine data-driven specification of behavior with a system that can execute a similar behavior in a physical simulation, thus enabling realistic responses to perturbations and environmental variation. We ...
- research-articleJuly 2018
Semantic soft segmentation
ACM Transactions on Graphics (TOG), Volume 37, Issue 4August 2018, Article No.: 72, pp 1–13https://doi.org/10.1145/3197517.3201275Accurate representation of soft transitions between image regions is essential for high-quality image editing and compositing. Current techniques for generating such representations depend heavily on interaction by a skilled visual artist, as creating ...
- research-articleFebruary 2018
Optimizing N-dimensional, winograd-based convolution for manycore CPUs
ACM SIGPLAN Notices (SIGPLAN), Volume 53, Issue 1January 2018, pp 109–123https://doi.org/10.1145/3200691.3178496Recent work on Winograd-based convolution allows for a great reduction of computational complexity, but existing implementations are limited to 2D data and a single kernel size of 3 by 3. They can achieve only slightly better, and often worse ...
Also Published in:
PPoPP '18: Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming: ISBN 9781450349826, February 2018 - research-articleFebruary 2018
Bridging the gap between deep learning and sparse matrix format selection
ACM SIGPLAN Notices (SIGPLAN), Volume 53, Issue 1January 2018, pp 94–108https://doi.org/10.1145/3200691.3178495This work presents a systematic exploration on the promise and special challenges of deep learning for sparse matrix format selection---a problem of determining the best storage format for a matrix to maximize the performance of Sparse Matrix Vector ...
Also Published in:
PPoPP '18: Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming: ISBN 9781450349826, February 2018
