all AI news
SLOSH: Set LOcality Sensitive Hashing via Sliced-Wasserstein Embeddings. (arXiv:2112.05872v2 [cs.LG] UPDATED)
Feb. 10, 2022, 2:11 a.m. | Yuzhe Lu, Xinran Liu, Andrea Soltoggio, Soheil Kolouri
cs.LG updates on arXiv.org arxiv.org
Learning from set-structured data is an essential problem with many
applications in machine learning and computer vision. This paper focuses on
non-parametric and data-independent learning from set-structured data using
approximate nearest neighbor (ANN) solutions, particularly locality-sensitive
hashing. We consider the problem of set retrieval from an input set query. Such
retrieval problem requires: 1) an efficient mechanism to calculate the
distances/dissimilarities between sets, and 2) an appropriate data structure
for fast nearest neighbor search. To that end, we propose Sliced-Wasserstein …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Senior AI Engineer, EdTech (Remote)
@ Lightci | Toronto, Ontario
Data Scientist for Salesforce Applications
@ ManTech | 781G - Customer Site,San Antonio,TX
AI Research Scientist
@ Gridmatic | Cupertino, CA
Data Engineer
@ Global Atlantic Financial Group | Boston, Massachusetts, United States
Machine Learning Engineer - Conversation AI
@ DoorDash | Sunnyvale, CA; San Francisco, CA; Seattle, WA; Los Angeles, CA