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Found 4 papers, 0 papers with code
Exact Non-Oblivious Performance of Rademacher Random Embeddings
This paper revisits the performance of Rademacher random projections, establishing novel statistical guarantees that are numerically sharp and non-oblivious with respect to the input data.
Enriching Relation Extraction with OpenIE
Relation extraction (RE) is a sub-discipline of information extraction (IE) which focuses on the prediction of a relational predicate from a natural-language input unit (such as a sentence, a clause, or even a short paragraph consisting of multiple sentences and/or clauses).
Robust and Provable Guarantees for Sparse Random Embeddings
In this work, we improve upon the guarantees for sparse random embeddings, as they were recently provided and analyzed by Freksen at al. (NIPS'18) and Jagadeesan (NIPS'19).
Revisiting Initialization of Neural Networks
The proper initialization of weights is crucial for the effective training and fast convergence of deep neural networks (DNNs).
