Search Results for author:
Found 12 papers, 7 papers with code
Dynamic Graph Induced Contour-aware Heat Conduction Network for Event-based Object Detection
To address these issues, this paper proposes a novel dynamic graph induced contour-aware heat conduction network for event stream based object detection, termed CvHeat-DET.
Pre-Training Meta-Rule Selection Policy for Visual Generative Abductive Learning
Visual generative abductive learning studies jointly training symbol-grounded neural visual generator and inducing logic rules from data, such that after learning, the visual generation process is guided by the induced logic rules.
Guaranteed Multidimensional Time Series Prediction via Deterministic Tensor Completion Theory
Specifically, we develop a deterministic tensor completion theory and introduce the Temporal Convolutional Tensor Nuclear Norm (TCTNN) model.
Object Detection using Event Camera: A MoE Heat Conduction based Detector and A New Benchmark Dataset
Object detection in event streams has emerged as a cutting-edge research area, demonstrating superior performance in low-light conditions, scenarios with motion blur, and rapid movements.
Enhancing Knowledge Retrieval with In-Context Learning and Semantic Search through Generative AI
The refined model, Generative Tabular Text Retrieval (GTR-T), demonstrated its efficiency in large database querying, achieving an Execution Accuracy (EX) of 0. 82 and an Exact-Set-Match (EM) accuracy of 0. 60 on the Spider dataset, using an open-source LLM.
Rare Class Prediction Model for Smart Industry in Semiconductor Manufacturing
The evolution of industry has enabled the integration of physical and digital systems, facilitating the collection of extensive data on manufacturing processes.
GAMedX: Generative AI-based Medical Entity Data Extractor Using Large Language Models
In the rapidly evolving field of healthcare and beyond, the integration of generative AI in Electronic Health Records (EHRs) represents a pivotal advancement, addressing a critical gap in current information extraction techniques.
Generating by Understanding: Neural Visual Generation with Logical Symbol Groundings
Making neural visual generative models controllable by logical reasoning systems is promising for improving faithfulness, transparency, and generalizability.
Learning Bottleneck Transformer for Event Image-Voxel Feature Fusion based Classification
Additionally, a bottleneck Transformer is introduced to facilitate the fusion of the dual-stream information.
Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systems
In this paper, a general framework for deep learning-based power control methods for max-min, max-product and max-sum-rate optimization in uplink cell-free massive multiple-input multiple-output (CF mMIMO) systems is proposed.
Enhancing Population Persistence by a Protection Zone in a Reaction-Diffusion Model with Strong Allee Effect
Protecting endangered species has been an important issue in ecology.
Dynamical Systems
Learning Graph-Level Representations with Recurrent Neural Networks
In this work, we develop a new approach to learn graph-level representations, which includes a combination of unsupervised and supervised learning components.
