all AI news
EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling. (arXiv:2205.12243v1 [stat.ML])
May 25, 2022, 1:11 a.m. | Mitch Hill, Jonathan Mitchell, Chu Chen, Yuan Du, Mubarak Shah, Song-Chun Zhu
stat.ML updates on arXiv.org arxiv.org
This work presents strategies to learn an Energy-Based Model (EBM) according
to the desired length of its MCMC sampling trajectories. MCMC trajectories of
different lengths correspond to models with different purposes. Our experiments
cover three different trajectory magnitudes and learning outcomes: 1) shortrun
sampling for image generation; 2) midrun sampling for classifier-agnostic
adversarial defense; and 3) longrun sampling for principled modeling of image
probability densities. To achieve these outcomes, we introduce three novel
methods of MCMC initialization for negative samples …
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Cleared Senior Software Engineer, Computer Vision, Federal
@ CCRi | Chantilly, Virginia, United States
Data Analyst - B2C
@ DAZN | Hyderabad, India
Product Marketing Manager - AI Chatbot
@ SendBird | San Mateo, California, United States
Alternance Alternant Ingénieur Développement logiciel temps réel embarqué / computer vision (F/H)
@ Alstom | Villeurbanne, FR
AOT Data Analyst II - Highway Project Delivery
@ State of Vermont | Barre, VT, US