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Found 11 papers, 2 papers with code
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning.
A Geometric Understanding of Natural Gradient
While natural gradients have been widely studied from both theoretical and empirical perspectives, we argue that some fundamental theoretical issues regarding the existence of gradients in infinite dimensional function spaces remain underexplored.
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction
Improving the sample efficiency of reinforcement learning algorithms requires effective exploration.
Generative Particle Variational Inference via Estimation of Functional Gradients
Recently, particle-based variational inference (ParVI) methods have gained interest because they can avoid arbitrary parametric assumptions that are common in variational inference.
Siamese Natural Language Tracker: Tracking by Natural Language Descriptions with Siamese Trackers
We propose a novel Siamese Natural Language Tracker (SNLT), which brings the advancements in visual tracking to the tracking by natural language (NL) descriptions task.
Implicit Generative Modeling for Efficient Exploration
Each random draw from our generative model is a neural network that instantiates the dynamic function, hence multiple draws would approximate the posterior, and the variance in the future prediction based on this posterior is used as an intrinsic reward for exploration.
Real-time Visual Object Tracking with Natural Language Description
In benchmarks, our method is competitive with state of the art trackers, while it outperforms all other trackers on targets with unambiguous and precise language annotations.
Moment Matching for Multi-Source Domain Adaptation
Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain.
A Topological Regularizer for Classifiers via Persistent Homology
In particular, our measurement of topological complexity incorporates the importance of topological features (e. g., connected components, handles, and so on) in a meaningful manner, and provides a direct control over spurious topological structures.
A Bayesian Approach for Online Classifier Ensemble
We propose a Bayesian approach for recursively estimating the classifier weights in online learning of a classifier ensemble.
Class Probability Estimation via Differential Geometric Regularization
We study the problem of supervised learning for both binary and multiclass classification from a unified geometric perspective.
