In this paper, we propose a new constraint, called shift-consistency, fo...
In this paper we discuss pre- and post-processing methods to induce desi...
Modeling weather and climate is an essential endeavor to understand the ...
The partially observable Markov decision process (POMDP) framework is a
...
Most state-of-the-art approaches for weather and climate modeling are ba...
Here, we demonstrate how machine learning enables the prediction of
como...
The goal of offline reinforcement learning (RL) is to learn near-optimal...
Neural Processes (NPs) are a popular class of approaches for meta-learni...
In this paper we provide a latent-variable formulation and solution to t...
In this paper we introduce a new consistency-based approach for defining...
High-dimensional observations are a major challenge in the application o...
The control and guidance of multi-robots (swarm) is a non-trivial proble...
High-dimensional observations and unknown dynamics are major challenges ...
With the rapid advance of sophisticated control algorithms, the capabili...
The field of video compression has developed some of the most sophistica...
In apprenticeship learning (AL), agents learn by watching or acquiring h...
Swarm systems consist of large numbers of robots that collaborate
autono...
In this paper, we present a novel approach that uses deep learning techn...