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Found 30 papers, 12 papers with code
Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis
Explainable disease diagnosis, which leverages patient information (e. g., signs and symptoms) and computational models to generate probable diagnoses and reasonings, offers clear clinical values.
Retrieval-augmented in-context learning for multimodal large language models in disease classification
We evaluated the framework on two real-world multi-modal datasets (TCGA and IU Chest X-ray), assessing its performance across multiple MLLMs (Qwen, Llava, Gemma), embedding strategies, similarity metrics, and varying numbers of demonstrations.
MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning
The framework is tested with two large language models (Llama2-7B, Llama3-8B) and three retrievers (Contriever, MedCPT, BGE-Large) to assess performance across different retrieval strategies.
Continually Evolved Multimodal Foundation Models for Cancer Prognosis
Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates.
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1.
Ranked #1 on
Mathematical Reasoning
on AIME24
DeepSeek-V3 Technical Report
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.
RAMIE: Retrieval-Augmented Multi-task Information Extraction with Large Language Models on Dietary Supplements
We compared RAMIE's performance to LLMs with instruction fine-tuning alone and conducted an ablation study to assess the contributions of multi-task learning and RAG to improved multitasking performance.
Deep Q-Exponential Processes
We demonstrate the numerical advantages of the proposed deep Q-EP model by comparing with multiple state-of-the-art deep probabilistic models.
Large Language Models for Disease Diagnosis: A Scoping Review
In this article, we perform a comprehensive review of LLM-based methods for disease diagnosis.
Open-World Electrocardiogram Classification via Domain Knowledge-Driven Contrastive Learning
Overall, the investigated problem (i. e., open-world ECG classification) helps to draw attention to the reliability of automatic ECG diagnosis, and the proposed method is proven effective in tackling the challenges.
Interpretable Differential Diagnosis with Dual-Inference Large Language Models
To the best of our knowledge, it is the first work that customizes LLMs for DDx explanation and comprehensively evaluates their interpretation performance.
Graph Anomaly Detection with Noisy Labels by Reinforcement Learning
Specifically, we aim to maximize the performance improvement (AUC) of a base detector by cutting noisy edges approximated through the nodes with high-confidence labels.
Denoising-Aware Contrastive Learning for Noisy Time Series
However, this pre-processing approach may not fully eliminate the effect of noise in SSL for two reasons: (i) the diverse types of noise in time series make it difficult to automatically determine suitable denoising methods; (ii) noise can be amplified after mapping raw data into latent space.
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
Fast Diffeomorphic Image Registration using Patch based Fully Convolutional Networks
Diffeomorphic image registration is a fundamental step in medical image analysis, owing to its capability to ensure the invertibility of transformations and preservation of topology.
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
In this paper, we base on the phenomenon and propose a general and novel research problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph and unseen testing graph to eliminate potential dangers.
SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA
Several variants of variational autoencoders (VAEs) have been proposed to learn compact data representations by encoding high-dimensional data in a lower dimensional space.
Biofilms as self-shaping growing nematics
Active nematics are the nonequilibrium analog of passive liquid crystals in which anisotropic units consume free energy to drive emergent behavior.
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
In this paper, we base on the phenomenon and propose a general and novel research problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph and unseen testing graph to eliminate potential dangers.
Unseen Anomaly Detection on Networks via Multi-Hypersphere Learning
Network anomaly detection is a crucial task since a few anomalies can cause huge losses.
Subtractive Aggregation for Attributed Network Anomaly Detection
It aims to detect nodes that significantly deviate from their corresponding background.
From Point to Space: 3D Moving Human Pose Estimation Using Commodity WiFi
To reconstruct 3D poses of people who move throughout the space rather than a fixed point, we fuse the amplitude and phase into Channel State Information (CSI) images which can provide both pose and position information.
Subject-independent Human Pose Image Construction with Commodity Wi-Fi
Existing papers achieve good results when constructing the images of subjects who are in the prior training samples.
Statistical Guarantees for Transformation Based Models with Applications to Implicit Variational Inference
More recently, transformation-based models have been used in variational inference (VI) to construct flexible implicit families of variational distributions.
PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation
De-identification is the task of identifying protected health information (PHI) in the clinical text.
When Healthcare Meets Off-the-Shelf WiFi: A Non-Wearable and Low-Costs Approach for In-Home Monitoring
The proposed approach can capture fine-grained human pose figures even through a wall and track detailed respiration status simultaneously by off-the-shelf WiFi devices.
The Data Analysis Pipeline for the SDSS-IV MaNGA IFU Galaxy Survey: Overview
We summarize assessments of the precision and accuracy of our measurements as a function of signal-to-noise and other salient metrics, noting that additional analysis and discussion of emission-line diagnostics are provided in our companion paper, Belfiore et al.
Astrophysics of Galaxies
Revisiting the proton-radius problem using constrained Gaussian processes
We have shown the impact of the physical constraints imposed on the form factor and of the range of experimental data used.
Nuclear Theory Nuclear Experiment Applications
A New Nonparametric Estimate of the Risk-Neutral Density with Applications to Variance Swaps
We develop a new nonparametric approach for estimating the risk-neutral density of asset prices and reformulate its estimation into a double-constrained optimization problem.
Adaptive posterior convergence rates in non-linear latent variable models
In this article, we study rates of posterior contraction in univariate density estimation for a class of non-linear latent variable models where unobserved U(0, 1) latent variables are related to the response variables via a random non-linear regression with an additive error.
Statistics Theory Statistics Theory
