Asia
Decentralized Accelerated Proximal Gradient Descent Haishan Y e 1 Ziang Zhou 1 Luo
In this paper, we study the decentralized composite optimization problem with a non-smooth regularization term.
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The difference is significant, sincesp(h) Dand the gap between the twocanbearbitrarily large.
X-CAL: Explicit Calibration for Survival Analysis Mark Goldstein
When a model's predicted number of events within any time interval is similar to the observed number, it is called well-calibrated . A survival model's calibration can be measured using, for instance, distributional calibration (
Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysis
The high-level question in this work is: If we learn a task using a sufficiently deep NN, how can we uncover the underlying hierarchical organization of sub-functions in that task?
The MAGICAL Benchmark for Robust Imitation
The robot could learn from these demonstrations to complete the tasks autonomously. For IL algorithms to be useful, however, they must be able to learn how to perform tasks from few demonstrations. A domestic robot wouldn't be very helpful if it required thirty demonstrations before it figured out that you are deliberately washing your purple cravat
ParallelandEfficientHierarchicalk-Median Clustering
Inparticular,standardmetricformulations as hierarchical k-center,k-means, andk-median received a lot of attention and the problems have been studied extensively in different models of computation.
