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Found 5 papers, 1 papers with code
Learning to Reach Goals via Diffusion
We present a novel perspective on goal-conditioned reinforcement learning by framing it within the context of denoising diffusion models.
On Diffusion Modeling for Anomaly Detection
By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE).
EqR: Equivariant Representations for Data-Efficient Reinforcement Learning
We study different notions of equivariance as an inductive bias in Reinforcement Learning (RL) and propose new mechanisms for recovering representations that are equivariant to both an agent’s action, and symmetry transformations of the state-action pairs.
Mini-batch graphs for robust image classification
Current deep learning models for classification tasks in computer vision are trained using mini-batches.
Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach
The proposed method is an extension of our previous work with the addition of a new unsupervised adversarial loss and a structured prediction based architecture.
