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Found 11 papers, 5 papers with code
Interpretable Differential Diagnosis with Dual-Inference Large Language Models
Methodological advancements to automate the generation of differential diagnosis (DDx) to predict a list of potential diseases as differentials given patients' symptom descriptions are critical to clinical reasoning and applications such as decision support.
Understanding Different Design Choices in Training Large Time Series Models
Moreover, we propose \emph{time series prompt}, a novel statistical prompting strategy tailored to time series data.
MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
Molecular property prediction (MPP) is a fundamental and crucial task in drug discovery.
UniGLM: Training One Unified Language Model for Text-Attributed Graphs
Representation learning on text-attributed graphs (TAGs), where nodes are represented by textual descriptions, is crucial for textual and relational knowledge systems and recommendation systems.
Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant
Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure.
Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint
To tackle these challenges, deep learning frameworks have been created to match patients to trials.
PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data
In this work, we present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction.
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant.
Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
Motivated by this, we investigate developing a learning-based over-sampling algorithm to optimize the classification performance, which is a challenging task because of the huge and hierarchical decision space.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
AutoVideo: An Automated Video Action Recognition System
Action recognition is an important task for video understanding with broad applications.
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.
Ranked #2 on
Error Understanding
on CUB-200-2011 (ResNet-101)
