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Found 8 papers, 5 papers with code
Self-supervised Learning with Local Contrastive Loss for Detection and Semantic Segmentation
We evaluate our SSL approach on two downstream tasks -- object detection and semantic segmentation, using COCO, PASCAL VOC, and CityScapes datasets.
Scalable Anytime Planning for Multi-Agent MDPs
We also introduce a multi-drone delivery domain with dynamic, i. e., state-dependent coordination graphs, and demonstrate how our approach scales to large problems on this domain that are intractable for other MCTS methods.
Signal Enhancement for Magnetic Navigation Challenge Problem
It is difficult to separate the Earth magnetic anomaly field, which is crucial for navigation, from the total magnetic field reading from the sensor.
Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning
Coordination graph based formalization allows reasoning about the joint action based on the structure of interactions.
Towards Recurrent Autoregressive Flow Models
The proposed method defines a conditional distribution for each variable in a sequential process by conditioning the parameters of a normalizing flow with recurrent neural connections.
Self-Supervised Feature Extraction for 3D Axon Segmentation
Existing learning-based methods to automatically trace axons in 3D brain imagery often rely on manually annotated segmentation labels.
Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces
As a result, network discovery algorithms optimized for specific downstream learning tasks given resource collection constraints are of great interest.
Feature Forwarding for Efficient Single Image Dehazing
Haze degrades content and obscures information of images, which can negatively impact vision-based decision-making in real-time systems.
