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Found 8 papers, 4 papers with code
PASTA: Pretrained Action-State Transformer Agents
Self-supervised learning has brought about a revolutionary paradigm shift in various computing domains, including NLP, vision, and biology.
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces
While early QD algorithms view the objective and descriptor functions as black-box functions, novel tools have been introduced to use gradient information to accelerate the search and improve overall performance of those algorithms over continuous input spaces.
The Quality-Diversity Transformer: Generating Behavior-Conditioned Trajectories with Decision Transformers
In the context of neuroevolution, Quality-Diversity algorithms have proven effective in generating repertoires of diverse and efficient policies by relying on the definition of a behavior space.
Multi-Objective Quality Diversity Optimization
In this work, we consider the problem of Quality-Diversity (QD) optimization with multiple objectives.
Unsupervised Domain Adaptation for Constraining Star Formation Histories
The prevalent paradigm of machine learning today is to use past observations to predict future ones.
ADAPT : Awesome Domain Adaptation Python Toolbox
In this paper, we introduce the ADAPT library, an open source Python API providing the implementation of the main transfer learning and domain adaptation methods.
Adversarial Weighting for Domain Adaptation in Regression
We present a novel instance-based approach to handle regression tasks in the context of supervised domain adaptation under an assumption of covariate shift.
Autoencoder-based time series clustering with energy applications
Time series clustering is a challenging task due to the specific nature of the data.
