Search Results for author:
Found 22 papers, 5 papers with code
ANNs-SaDE: A Machine-Learning-Based Design Automation Framework for Microwave Branch-Line Couplers
The traditional method for designing branch-line couplers involves a trial-and-error optimization process that requires multiple design iterations through electromagnetic (EM) simulations.
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
Text-Attributed Graphs (TAGs), where each node is associated with text descriptions, are ubiquitous in real-world scenarios.
InstructAgent: Building User Controllable Recommender via LLM Agent
Traditional recommender systems usually take the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms.
Denoising VAE as an Explainable Feature Reduction and Diagnostic Pipeline for Autism Based on Resting state fMRI
By using a denoising variational autoencoder, our proposed pipeline further compresses the connectivity features into 5 latent Gaussian distributions, providing is a low-dimensional representation of the data to promote computational efficiency and interpretability.
When Diffusion MRI Meets Diffusion Model: A Novel Deep Generative Model for Diffusion MRI Generation
This achievement underscores a substantial progression in enhancing dMRI quality, highlighting the potential of our novel generative approach to revolutionize dMRI imaging standards.
When Box Meets Graph Neural Network in Tag-aware Recommendation
Last year has witnessed the re-flourishment of tag-aware recommender systems supported by the LLM-enriched tags.
Efficient Model Compression for Hierarchical Federated Learning
Federated learning (FL), as an emerging collaborative learning paradigm, has garnered significant attention due to its capacity to preserve privacy within distributed learning systems.
EMERGE: Integrating RAG for Improved Multimodal EHR Predictive Modeling
However, current models that utilize clinical notes and multivariate time-series EHR data often lack the necessary medical context for precise clinical tasks.
Knowledge Graph Pruning for Recommendation
Recent years have witnessed the prosperity of knowledge graph based recommendation system (KGRS), which enriches the representation of users, items, and entities by structural knowledge with striking improvement.
DynLLM: When Large Language Models Meet Dynamic Graph Recommendation
To bridge this gap, in this paper, we propose a novel framework, called DynLLM, to deal with the dynamic graph recommendation task with LLMs.
BezierFormer: A Unified Architecture for 2D and 3D Lane Detection
B\'{e}zierFormer formulate queries as B\'{e}zier control points and incorporate a novel B\'{e}zier curve attention mechanism.
AIOS: LLM Agent Operating System
This AIOS kernel provides fundamental services (e. g., scheduling, context management, memory management, storage management, access control) and efficient management of resources (e. g., LLM and external tools) for runtime agents.
REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models
Leveraging clinical notes and multivariate time-series EHR, existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge, particularly from the knowledge graph (KG).
A survey on deep learning approaches for data integration in autonomous driving system
The perception module of self-driving vehicles relies on a multi-sensor system to understand its environment.
NeMO: Neural Map Growing System for Spatiotemporal Fusion in Bird's-Eye-View and BDD-Map Benchmark
We also provide a new scene-level BEV map evaluation setting along with the corresponding baseline for a more comprehensive comparison.
Anchor Free remote sensing detector based on solving discrete polar coordinate equation
Finally, referring to the calculation idea of horizontal IoU, we design a rotating IoU based on the split polar coordinate plane, namely JIoU, which is expressed as the intersection ratio following discretization of the inner ellipse of the rotating bounding box, to solve the correlation between angle and side length in the regression process of the rotating bounding box.
Tensor Network for Supervised Learning at Finite Temperature
Tensor network is chosen since it is the best platform to introduce thermal fluctuation.
Overcoming Language Priors with Self-supervised Learning for Visual Question Answering
Our method can compensate for the data biases by generating balanced data without introducing external annotations.
Symmetrical Graph Neural Network for Quantum Chemistry, with Dual R/K Space
Most of current neural network models in quantum chemistry (QC) exclude the molecular symmetry, separate the well-correlated real space (R space), and momenta space (K space) into two individuals, which lack the essential physics in molecular chemistry.
Chemical Physics
Fixed-Size Ordinally Forgetting Encoding Based Word Sense Disambiguation
In this paper, we present our method of using fixed-size ordinally forgetting encoding (FOFE) to solve the word sense disambiguation (WSD) problem.
The Newton Scheme for Deep Learning
Then, by classifying the training model as a quick procedure of 'force pattern' recognition, we developed the Newton physics-based NS scheme.
Pixel Chem: A Representation for Predicting Material Properties with Neural Network
In this work we developed a new representation of the chemical information for the machine learning models, with benefits from both the real space (R-space) and energy space (K-space).
