Search Results for author:
Found 11 papers, 5 papers with code
Multilinear Hyperplane Hashing
Despite its successful progress in classic point-to-point search, there are few studies regarding point-to-hyperplane search, which has strong practical capabilities of scaling up in many applications like active learning with SVMs.
A Unified Framework for Tuning Hyperparameters in Clustering Problems
In this paper, we provide a framework with provable guarantees for selecting hyperparameters in a number of distinct models.
Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation
To stabilize this method, we adapt to contextual generation of categorical sequences a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control.
Attention that does not Explain Away
Models based on the Transformer architecture have achieved better accuracy than the ones based on competing architectures for a large set of tasks.
Bayesian Attention Modules
Attention modules, as simple and effective tools, have not only enabled deep neural networks to achieve state-of-the-art results in many domains, but also enhanced their interpretability.
Contextual Dropout: An Efficient Sample-Dependent Dropout Module
However, the quality of uncertainty estimation is highly dependent on the dropout probabilities.
Adversarially Adaptive Normalization for Single Domain Generalization
As a generic tool, the improvement introduced by ASR-Norm is agnostic to the choice of ADA methods.
Bayesian Attention Belief Networks
Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks.
A Prototype-Oriented Framework for Unsupervised Domain Adaptation
Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space.
Alignment Attention by Matching Key and Query Distributions
The neural attention mechanism has been incorporated into deep neural networks to achieve state-of-the-art performance in various domains.
On hyperparameter tuning in general clustering problemsm
Tuning hyperparameters for unsupervised learning problems is difficult in general due to the lack of ground truth for validation.
