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Found 4 papers, 1 papers with code
Arbitrarily Large Labelled Random Satisfiability Formulas for Machine Learning Training
As a first indication of what can be achieved with the new generator, we present a novel classifier that performs significantly better than random guessing 99% on the same datasets, for most difficulty levels.
Bad Global Minima Exist and SGD Can Reach Them
Several works have aimed to explain why overparameterized neural networks generalize well when trained by Stochastic Gradient Descent (SGD).
Skip-Gram − Zipf + Uniform = Vector Additivity
An unexpected {``}side-effect{''} of such models is that their vectors often exhibit compositionality, i. e., \textit{adding}two word-vectors results in a vector that is only a small angle away from the vector of a word representing the semantic composite of the original words, e. g., {``}man{''} + {``}royal{''} = {``}king{''}.
Near-Optimal Entrywise Sampling for Data Matrices
We consider the problem of selecting non-zero entries of a matrix $A$ in order to produce a sparse sketch of it, $B$, that minimizes $\|A-B\|_2$.
