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- research-articleApril 2024
Trillion Parameter AI Serving Infrastructure for Scientific Discovery: A Survey and Vision
- Nathaniel C Hudson,
- J. Gregory Pauloski,
- Matt Baughman,
- Alok Kamatar,
- Mansi Sakarvadia,
- Logan Ward,
- Ryan Chard,
- André Bauer,
- Maksim Levental,
- Wenyi Wang,
- Will Engler,
- Owen Price Skelly,
- Ben Blaiszik,
- Rick Stevens,
- Kyle Chard,
- Ian Foster
BDCAT '23: Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and TechnologiesArticle No.: 15, Pages 1–10https://doi.org/10.1145/3632366.3632396Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we are now entering an era of Trillion Parameter Models (TPM), or models with ...
- research-articleMarch 2022
Cost-effective Video-based Poor Repertoire Detection for Preterm Infant General Movement Analysis
ICIGP '22: Proceedings of the 2022 5th International Conference on Image and Graphics ProcessingPages 51–58https://doi.org/10.1145/3512388.3512396Conventional analysis of preterm infant general movement analysis (GMA) requires long-term observation of a subject's body movement by medical experts with domain-specific knowledge, which is labor-intensive. Yet existing video-based human pose analysis ...
- posterApril 2017
Deep Learning for Hate Speech Detection in Tweets
WWW '17 Companion: Proceedings of the 26th International Conference on World Wide Web CompanionPages 759–760https://doi.org/10.1145/3041021.3054223Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or ...