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On the Local Minima of the Empirical Risk
Chi Jin, Lydia T. Liu, Rong Ge, Michael I. Jordan
Neural Information Processing Systems http://nips.cc/
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Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components
Sascha Saralajew, Lars Holdijk, Maike Rees, Ebubekir Asan, Thomas Villmann
Neural networks are state-of-the-art classification approaches but are generally difficult to interpret. This issue can be partly alleviated by constructing a precise decision process within the neural network. In this work, a network architecture, denoted as Classification-By-Components network (CBC), is proposed.
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Learning Deterministic Weighted Automata with Queries and Counterexamples
Gail Weiss, Yoav Goldberg, Eran Yahav
Next Token Distribution:Pn(w): $ ![0,1], defined:Pn(w)( )= Pl(w ).
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Elephants are smart. So are their whiskers.
Environment Animals Wildlife Elephants are smart. Their 1,000 whiskers make them dextrous enough to pick up a tortilla chip. Breakthroughs, discoveries, and DIY tips sent six days a week. An elephant's trunk is a wonder of evolution. Gentle, yet dextrous, it can pick up solid items, help them communicate, and be a helpful showering too l.
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Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
Loucas Pillaud-Vivien, Alessandro Rudi, Francis Bach
Neural Information Processing Systems http://nips.cc/
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