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Found 17 papers, 2 papers with code
Advancing Out-of-Distribution Detection through Data Purification and Dynamic Activation Function Design
Our work addresses this challenge by enhancing the detection and management of OOD samples in neural networks.
Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning
Hence, we propose fine-tuning the parameters of the attention pooling layer during the training process to encourage the model to focus on task-specific semantics.
Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs
Many score-based active learning methods have been successfully applied to graph-structured data, aiming to reduce the number of labels and achieve better performance of graph neural networks based on predefined score functions.
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks
Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over the years.
Formation Tracking for a Multi-Auv System Based on an Adaptive Sliding Mode Method in the Water Flow Environment
At last, a robust sliding mode controller with continuous model predictive control strategy for the multi-AUV system is developed to achieve leader-follower formation tracking under the presence of bounded flow disturbances, and simulations are implemented to confirm the effectiveness of the proposed method.
A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems
To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further.
Dispatchable Region for Active Distribution Networks Using Approximate Second-Order Cone Relaxation
Although DC and linearized AC power flow equations are typically used to model dispatchable regions for transmission systems, these equations are rarely suitable for distribution networks.
PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation
6D pose estimation from a single RGB image is a challenging and vital task in computer vision.
Data-Driven Dispatchable Regions with Potentially Active Boundaries for Renewable Power Generation: Concept and Construction
State-of-the-art dispatchable region (DR) research has studied how system operational constraints influence the DR but has seldom considered the effect of the uncertainty features of RPG outputs.
Robust RGB-based 6-DoF Pose Estimation without Real Pose Annotations
We achieve state of the art performance on LINEMOD, and OccludedLINEMOD in without real-pose setting, even outperforming methods that rely on real annotations during training on Occluded-LINEMOD.
Optimizing AD Pruning of Sponsored Search with Reinforcement Learning
Industrial sponsored search system (SSS) can be logically divided into three modules: keywords matching, ad retrieving, and ranking.
CDPN: Coordinates-Based Disentangled Pose Network for Real-Time RGB-Based 6-DoF Object Pose Estimation
6-DoF object pose estimation from a single RGB image is a fundamental and long-standing problem in computer vision.
Ranked #11 on
6D Pose Estimation using RGB
on LineMOD
IFAA: Robust association identification and Inference For Absolute Abundance in microbiome analyses
The target of inference in microbiome analyses is usually relative abundance (RA) because RA in a sample (e. g., stool) can be considered as an approximation of RA in an entire ecosystem (e. g., gut).
Applications
Cosmological parameter estimation from large-scale structure deep learning
We propose a light-weight deep convolutional neural network to estimate the cosmological parameters from simulated 3-dimensional dark matter distributions with high accuracy.
Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology
An end-to-end Generative Retrieval Method for Sponsored Search Engine --Decoding Efficiently into a Closed Target Domain
In this paper, we present a generative retrieval method for sponsored search engine, which uses neural machine translation (NMT) to generate keywords directly from query.
Generate Identity-Preserving Faces by Generative Adversarial Networks
Generating identity-preserving faces aims to generate various face images keeping the same identity given a target face image.
Output Constraint Transfer for Kernelized Correlation Filter in Tracking
Kernelized Correlation Filter (KCF) is one of the state-of-the-art object trackers.
