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Semantic Hashing with Locality Sensitive Embeddings
Semantic hashing methods have been explored for learning transformations into binary vector spaces.
Self-Supervised Contextual Bandits in Computer Vision
In the usual self-supervision, we learn implicit labels from the training data for a secondary task.
Modeling and Simultaneously Removing Bias via Adversarial Neural Networks
In real world systems, the predictions of deployed Machine Learned models affect the training data available to build subsequent models.
Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation
We propose a stochastic algorithm for collapsed variational Bayesian inference for LDA, which is simpler and more efficient than the state of the art method.
The Time-Marginalized Coalescent Prior for Hierarchical Clustering
We introduce a new prior for use in Nonparametric Bayesian Hierarchical Clustering.
Statistical Tests for Optimization Efficiency
Learning problems such as logistic regression are typically formulated as pure optimization problems defined on some loss function.
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