28 citations · 52 across the 11 of their papers we have counts for
Showing 2019Show all
3 papers · 1 filter
cs.CV2019★ 2 cited
Balancing Specialization, Generalization, and Compression for Detection and Tracking
Dotan Kaufman, Koby Bibas, Eran Borenstein +2
We propose a method for specializing deep detectors and trackers to restricted settings. Our approach is designed with the following goals in mind: (a) Improving accuracy in restri…
cs.LG2019★ 28 cited
A New Look at an Old Problem: A Universal Learning Approach to Linear Regression
Koby Bibas, Yaniv Fogel, Meir Feder
Linear regression is a classical paradigm in statistics. A new look at it is provided via the lens of universal learning. In applying universal learning to linear regression the hy…
cs.LG2019★ 11 cited
Deep pNML: Predictive Normalized Maximum Likelihood for Deep Neural Networks
Koby Bibas, Yaniv Fogel, Meir Feder
The Predictive Normalized Maximum Likelihood (pNML) scheme has been recently suggested for universal learning in the individual setting, where both the training and test samples ar…