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Found 4 papers, 1 papers with code
DP-SGD Without Clipping: The Lipschitz Neural Network Way
To provide sensitivity bounds and bypass the drawbacks of the clipping process, we propose to rely on Lipschitz constrained networks.
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images
Our approach learns textual and visual representations jointly: latent visual factors couple together a skip-gram model for co-occurrence in linguistic data and a generative latent variable model for visual data.
A Comparison between Deep Neural Nets and Kernel Acoustic Models for Speech Recognition
We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition.
Similarity Learning for Provably Accurate Sparse Linear Classification
In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions.
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