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Found 19 papers, 6 papers with code
Generalized Correspondence Matching via Flexible Hierarchical Refinement and Patch Descriptor Distillation
Taking into account the practical applicability of our method in real-world robotics applications, we also propose a novel patch descriptor distillation strategy to further reduce the computational complexity of correspondence matching.
Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models
These LoRA modules can be aligned with the expert design principles observed in Mixture-of-Experts (MoE).
CycLight: learning traffic signal cooperation with a cycle-level strategy
This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems.
Revolutionizing Pharma: Unveiling the AI and LLM Trends in the Pharmaceutical Industry
This document offers a critical overview of the emerging trends and significant advancements in artificial intelligence (AI) within the pharmaceutical industry.
Uncovering Regulatory Affairs Complexity in Medical Products: A Qualitative Assessment Utilizing Open Coding and Natural Language Processing (NLP)
This study investigates the complexity of regulatory affairs in the medical device industry, a critical factor influencing market access and patient care.
Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series
The results demonstrate that COMET consistently outperforms all baselines, particularly in setup with 10% and 1% labeled data fractions across all datasets.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
Perimeter Control with Heterogeneous Cordon Signal Behaviors: A Semi-Model Dependent Reinforcement Learning Approach
The proposed strategy integrates the MARL-based signal control method with centralized feedback PC policy and is applied to cordon signals of the PN.
Multi-modal Learning based Prediction for Disease
From the dataset, we quantitatively analyze and select clinical metadata that most contribute to NAFLD prediction.
OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist Models
As a starting point, we provide presets of 7 different modalities and 23 highly-diverse example tasks in OFASys, with which we also develop a first-in-kind, single model, OFA+, that can handle text, image, speech, video, and motion data.
The Application of Zig-Zag Sampler in Sequential Markov Chain Monte Carlo
Particle filtering methods are widely applied in sequential state estimation within nonlinear non-Gaussian state space model.
On Channel Reciprocity in Reconfigurable Intelligent Surface Assisted Wireless Network
Channel reciprocity greatly facilitates downlink precoding in time-division duplexing (TDD) multiple-input multiple-output (MIMO) communications without the need for channel state information (CSI) feedback.
Information Theory Information Theory
A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle
The RFC and the self-adaptive noise identification method are combine to propose a self-adaptive robust fission clustering (SARFC) algorithm.
Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band
The refined model gives more accurate estimates of the path loss of RISs comprised of unit cells with a deep sub-wavelength size.
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
From Design Draft to Real Attire: Unaligned Fashion Image Translation
Moreover, built upon the sampling network, we present design draft to real fashion item translation network (D2RNet), where two separate translation streams that focus on texture and shape, respectively, are combined tactfully to get both benefits.
Graph Random Neural Network for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RIS, which may pave the way for further theoretical studies and practical applications in this field.
Physical Layer Security Enhancement Exploiting Intelligent Reflecting Surface
In this letter, the use of intelligent reflecting surface (IRS) to enhance the physical layer security of downlink wireless communication is investigated.
