אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Predictions in modern inference problems can be increasingly understood in terms of high-dimensional structures such as arrangements of objects in computer vision, parses in natural language processing or molecular structures in computational biology. To interactively annotate an image, one needs to efficiently sample possible interactions to suggest the user to annotate areas with high uncertainty. Unfortunately, traditional approaches to handle high-dimensional structures involve the Gibbs distribution for which sampling and inference are notoriously hard. In this talk I will present recent advances in machine learning that facilitate sampling and probabilistic reasoning in high-dimensional problems. This approach constructs new distributions over high-dimensional structures that randomly perturb the image information followed by applying graph-cuts (max-solver).