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Found 4 papers, 2 papers with code
Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models
On the control side, we train a latent dynamics model using the Joint Embedding Predictive Architecture (JEPA) and use it for planning.
Light-weight probing of unsupervised representations for Reinforcement Learning
Unsupervised visual representation learning offers the opportunity to leverage large corpora of unlabeled trajectories to form useful visual representations, which can benefit the training of reinforcement learning (RL) algorithms.
MixUp Training Leads to Reduced Overfitting and Improved Calibration for the Transformer Architecture
MixUp is a computer vision data augmentation technique that uses convex interpolations of input data and their labels to enhance model generalization during training.
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data
Multi-task learning (MTL) is a machine learning technique aiming to improve model performance by leveraging information across many tasks.
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