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Found 11 papers, 1 papers with code
An Efficient Reconstructed Differential Evolution Variant by Some of the Current State-of-the-art Strategies for Solving Single Objective Bound Constrained Problems
In this paper, we propose a strategy recombination and reconstruction differential evolution algorithm called reconstructed differential evolution (RDE) to solve single-objective bounded optimization problems.
Online Poisoning Attacks Against Data-Driven Predictive Control
Data-driven predictive control (DPC) is a feedback control method for systems with unknown dynamics.
Class-Aware Adversarial Transformers for Medical Image Segmentation
In this work, we present CASTformer, a novel type of adversarial transformers, for 2D medical image segmentation.
MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution
Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence.
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation
However, most existing learning-based approaches usually suffer from limited manually annotated medical data, which poses a major practical problem for accurate and robust medical image segmentation.
Hierarchical Few-Shot Imitation with Skill Transition Models
To this end, we present Few-shot Imitation with Skill Transition Models (FIST), an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks given a few downstream demonstrations.
Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation
In this work, we present a novel Contrastive Voxel-wise Representation Learning (CVRL) method to effectively learn low-level and high-level features by capturing 3D spatial context and rich anatomical information along both the feature and the batch dimensions.
Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation
We present Contrastive Pre-training and Data Augmentation for Efficient Robotic Learning (CoDER), a method that utilizes data augmentation and unsupervised learning to achieve sample-efficient training of real-robot arm policies from sparse rewards.
Efficient Empowerment Estimation for Unsupervised Stabilization
We demonstrate our solution for sample-based unsupervised stabilization on different dynamical control systems and show the advantages of our method by comparing it to the existing VLB approaches.
Learning Efficient Representation for Intrinsic Motivation
The core idea is to represent the relation between action sequences and future states using a stochastic dynamic model in latent space with a specific form.
Dynamical System Embedding for Efficient Intrinsically Motivated Artificial Agents
In this work, we develop a novel approach for the estimation of empowerment in unknown arbitrary dynamics from visual stimulus only, without sampling for the estimation of MIAS.
