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Found 23 papers, 9 papers with code
Universal Vessel Segmentation for Multi-Modality Retinal Images
However, multi-modality retinal images are used every day in the study of retina and retinal diseases, and the study of vessel segmentation on the other modalities is scarce; (2) Even though a small amount of works extended their experiments to limited new modalities such as the Multi-Color Scanning Laser Ophthalmoscopy (MC), these works still require finetuning a separate model for the new modality.
Enhancing Reasoning to Adapt Large Language Models for Domain-Specific Applications
This paper presents SOLOMON, a novel Neuro-inspired Large Language Model (LLM) Reasoning Network architecture that enhances the adaptability of foundation models for domain-specific applications.
Topology-Preserving Image Segmentation with Spatial-Aware Persistent Feature Matching
Topological correctness is critical for segmentation of tubular structures.
FeBiM: Efficient and Compact Bayesian Inference Engine Empowered with Ferroelectric In-Memory Computing
As the first FeFET-based in-memory Bayesian inference engine, FeBiM achieves an impressive storage density of 26. 32 Mb/mm$^{2}$ and a computing efficiency of 581. 40 TOPS/W in a representative Bayesian classification task.
AlcLaM: Arabic Dialectal Language Model
Arabic-specific PLMs are trained predominantly on modern standard Arabic, which compromises their performance on regional dialects.
Leveraging Large Language Models for Patient Engagement: The Power of Conversational AI in Digital Health
The rapid advancements in large language models (LLMs) have opened up new opportunities for transforming patient engagement in healthcare through conversational AI.
Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters
By applying our neuron sparsification method to the Mistral and Mixtral models, only 2. 5 billion and 4. 3 billion parameters are activated per inference iteration, respectively, while achieving even more powerful model performance.
Comparative Analysis of Open-Source Language Models in Summarizing Medical Text Data
Unstructured text in medical notes and dialogues contains rich information.
Health Guardian: Using Multi-modal Data to Understand Individual Health
These microservices can be deployed to a clinical cohort specified through the Clinical Task Manager (CTM).
Remote Inference of Cognitive Scores in ALS Patients Using a Picture Description
In this paper, we focused on another important aspect, cognitive impairment, which affects 35-50% of the ALS population.
Enabling the Evaluation of Driver Physiology Via Vehicle Dynamics
Driving is a daily routine for many individuals across the globe.
"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language Models
This collaborative process champions the human in a dominant role, in addition to mixed and shifting levels of initiative that exist between humans and LLMs.
Global Pointer: Novel Efficient Span-based Approach for Named Entity Recognition
The ultimate goal is to enable a global view that considers the beginning and the end positions to predict the entity.
ZLPR: A Novel Loss for Multi-label Classification
To support the application of deep learning in multi-label classification (MLC) tasks, we propose the ZLPR (zero-bounded log-sum-exp \& pairwise rank-based) loss in this paper.
Experimentally realized memristive memory augmented neural network
Memory augmented neural network has been proposed to achieve the goal, but the memory module has to be stored in an off-chip memory due to its size.
BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis
This paper proposes a unified framework to address aspect categorization and aspect-based sentiment subtasks.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+2
PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application
On the one hand, it could provide item knowledge services in a uniform way with service vectors for embedding-based and item-knowledge-related task models without accessing triple data.
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting
Given the multi-scale feature representations and scale-specific inter-variable dependencies, a multi-scale temporal graph neural network is introduced to jointly model intra-variable dependencies and inter-variable dependencies.
Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting
In this paper, we propose a scale-aware neural architecture search framework for MTS forecasting (SNAS4MTF).
Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting
Time series forecasting is a significant problem in many applications, e. g., financial predictions and business optimization.
Billion-scale Pre-trained E-commerce Product Knowledge Graph Model
As a backbone for online shopping platforms, we built a billion-scale e-commerce product knowledge graph for various item knowledge services such as item recommendation.
RoFormer: Enhanced Transformer with Rotary Position Embedding
Then, we propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information.
Ranked #1 on
Semantic Text Matching
on CAIL2019-SCM - test
Linear Convergence of Proximal Gradient Algorithm with Extrapolation for a Class of Nonconvex Nonsmooth Minimization Problems
In this paper, we study the proximal gradient algorithm with extrapolation for minimizing the sum of a Lipschitz differentiable function and a proper closed convex function.
