Search Results for author:
Found 19 papers, 10 papers with code
Neural Approaches to SAT Solving: Design Choices and Interpretability
In this contribution, we provide a comprehensive evaluation of graph neural networks applied to Boolean satisfiability problems, accompanied by an intuitive explanation of the mechanisms enabling the model to generalize to different instances.
WorkTeam: Constructing Workflows from Natural Language with Multi-Agents
Workflows play a crucial role in enhancing enterprise efficiency by orchestrating complex processes with multiple tools or components.
SChanger: Change Detection from a Semantic Change and Spatial Consistency Perspective
The model then employs a shared-weight Siamese architecture and extended Temporal Fusion Module (TFM) to preserve this prior knowledge and is fine-tuned on change detection tasks.
Ranked #1 on
Change Detection
on SYSU-CD
LF-Steering: Latent Feature Activation Steering for Enhancing Semantic Consistency in Large Language Models
Large Language Models (LLMs) often generate inconsistent responses when prompted with semantically equivalent paraphrased inputs.
ACE: Anatomically Consistent Embeddings in Composition and Decomposition
Medical images acquired from standardized protocols show consistent macroscopic or microscopic anatomical structures, and these structures consist of composable/decomposable organs and tissues, but existing self-supervised learning (SSL) methods do not appreciate such composable/decomposable structure attributes inherent to medical images.
SRFS: Parallel Processing Fault-tolerant ROS2-based Flight Software for the Space Ranger Cubesat
The results demonstrate the high reliability and ease of maintenance of the satellite flight software offering a reference framework for the rapid development of high-performance small satellite operations systems.
Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks
Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data.
Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
State Space Models (SSMs) have emerged as a potent tool in sequence modeling tasks in recent years.
A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds
We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics.
Consisaug: A Consistency-based Augmentation for Polyp Detection in Endoscopy Image Analysis
Colorectal cancer (CRC), which frequently originates from initially benign polyps, remains a significant contributor to global cancer-related mortality.
DeViDe: Faceted medical knowledge for improved medical vision-language pre-training
These descriptions outline general visual characteristics of diseases in radiographs, and when combined with abstract definitions and radiology reports, provide a holistic snapshot of knowledge.
An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis
Both functional and structural magnetic resonance imaging (fMRI and sMRI) are widely used for the diagnosis of mental disorder.
Learning Anatomically Consistent Embedding for Chest Radiography
Self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated images.
CoC-GAN: Employing Context Cluster for Unveiling a New Pathway in Image Generation
We introduce this model with the novel structure as the Context Clustering Generative Adversarial Network (CoC-GAN), which offers a distinctive viewpoint in the domain of feature aggregating and dispatching.
Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning
To tackle the source data-absent problem, we present a novel two-stage source-free domain adaptation (SFDA) framework for medical image segmentation, where only a well-trained source segmentation model and unlabeled target data are available during domain adaptation.
Angle Basis: a Generative Model and Decomposition for Functional Connectivity
We also find that both the decomposition and its residual have approximately equal predictive value, and when combined into an ensemble, exceed the AUC of FC-based prediction by up to 5%.
ImageNomer: description of a functional connectivity and omics analysis tool and case study identifying a race confound
The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset.
Lightweight Facial Attractiveness Prediction Using Dual Label Distribution
The data processing is simplified to a minimum for a lightweight design, and MobileNetV2 is selected as our backbone.
Coarse Retinal Lesion Annotations Refinement via Prototypical Learning
Deep-learning-based approaches for retinal lesion segmentation often require an abundant amount of precise pixel-wise annotated data.
