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Found 13 papers, 0 papers with code
Optimizing over a Restricted Policy Class in Markov Decision Processes
However, under a condition that is akin to the occupancy measures of the base policies having large overlap, we show that there exists an efficient algorithm that finds a policy that is almost as good as the best convex combination of the base policies.
Robust Locally-Linear Controllable Embedding
We also propose a principled variational approximation of the embedding posterior that takes the future observation into account, and thus, makes the variational approximation more robust against the noise.
Disentangling Dynamics and Content for Control and Planning
In this paper, We study the problem of learning a controllable representation for high-dimensional observations of dynamical systems.
JADE: Joint Autoencoders for Dis-Entanglement
The problem of feature disentanglement has been explored in the literature, for the purpose of image and video processing and text analysis.
Fast Spectral Clustering Using Autoencoders and Landmarks
Spectral clustering is a powerful clustering algorithm that suffers from high computational complexity, due to eigen decomposition.
Generative Mixture of Networks
The model consists of K networks that are trained together to learn the underlying distribution of a given data set.
Radon-Gabor Barcodes for Medical Image Retrieval
The objective of this paper is to harness the potentials of both Gabor and Radon transforms in order to introduce expressive binary features, called barcodes, for image annotation/tagging tasks.
Semi-Supervised Representation Learning based on Probabilistic Labeling
Then, we map the data to lower-dimensional space using a linear transformation such that the dependency between the transformed data and the assigned labels is maximized.
Gabor Barcodes for Medical Image Retrieval
This has significantly heightened the need for automated annotation of the images on one side, and fast and memory-efficient content-based image retrieval systems on the other side.
Deep Variational Sufficient Dimensionality Reduction
We consider the problem of sufficient dimensionality reduction (SDR), where the high-dimensional observation is transformed to a low-dimensional sub-space in which the information of the observations regarding the label variable is preserved.
Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning
In autonomous driving (AD), accurately predicting changes in the environment can effectively improve safety and comfort.
Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map
While supervised learning is widely used for perception modules in conventional autonomous driving solutions, scalability is hindered by the huge amount of data labeling needed.
Neural Relational Inference with Node-Specific Information
Inferring interactions among entities is an important problem in studying dynamicalsystems, which greatly impacts the performance of downstream tasks, such asprediction.
