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Found 23 papers, 9 papers with code
Representation Learning of Lab Values via Masked AutoEncoder
Accurate imputation of missing laboratory values in electronic health records (EHRs) is critical to enable robust clinical predictions and reduce biases in AI systems in healthcare.
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs
Current ophthalmology clinical workflows are plagued by over-referrals, long waits, and complex and heterogeneous medical records.
Latent Space Disentanglement in Diffusion Transformers Enables Precise Zero-shot Semantic Editing
In image editing, DiTs project text and image inputs to a joint latent space, from which they decode and synthesize new images.
Deep Learning for Personalized Electrocardiogram Diagnosis: A Review
The electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its interpretation traditionally reliant on the expertise of cardiologists.
Latent Space Disentanglement in Diffusion Transformers Enables Zero-shot Fine-grained Semantic Editing
Through our investigation of DiT's latent space, we have uncovered key findings that unlock the potential for zero-shot fine-grained semantic editing: (1) Both the text and image spaces in DiTs are inherently decomposable.
Dr.Academy: A Benchmark for Evaluating Questioning Capability in Education for Large Language Models
Therefore, our research introduces a benchmark to evaluate the questioning capability in education as a teacher of LLMs through evaluating their generated educational questions, utilizing Anderson and Krathwohl's taxonomy across general, monodisciplinary, and interdisciplinary domains.
MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models
Electronic health records (EHRs) are multimodal by nature, consisting of structured tabular features like lab tests and unstructured clinical notes.
Efficient In-Context Medical Segmentation with Meta-driven Visual Prompt Selection
In-context learning (ICL) with Large Vision Models (LVMs) presents a promising avenue in medical image segmentation by reducing the reliance on extensive labeling.
Adam-mini: Use Fewer Learning Rates To Gain More
Adam-mini reduces memory by cutting down the learning rate resources in Adam (i. e., $1/\sqrt{v}$).
Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective
The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes.
Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications
Our paper investigates the efficiency and effectiveness of using vector embeddings from single-modal foundation models and multi-modal Vision-Language Models (VLMs) for multimodal deep learning in low-resource environments, particularly in healthcare.
DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era
In the big data era, integrating diverse data modalities poses significant challenges, particularly in complex fields like healthcare.
Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity
To address this challenge, we propose Federated Distributionally Robust Alignment (FedDRA), a framework for federated VLP that achieves robust vision-language alignment under heterogeneous conditions.
The Role of Linguistic Priors in Measuring Compositional Generalization of Vision-Language Models
Compositionality is a common property in many modalities including natural languages and images, but the compositional generalization of multi-modal models is not well-understood.
BenchMD: A Benchmark for Unified Learning on Medical Images and Sensors
Finally, we evaluate performance on out-of-distribution data collected at different hospitals than the training data, representing naturally-occurring distribution shifts that frequently degrade the performance of medical AI models.
Hiding Data Helps: On the Benefits of Masking for Sparse Coding
Furthermore, drawing from the growing body of work on self-supervised learning, we propose a novel masking objective for which recovering the ground-truth dictionary is in fact optimal as the signal increases for a large class of data-generating processes.
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup
Mixup is a data augmentation technique that relies on training using random convex combinations of data points and their labels.
Towards Understanding the Data Dependency of Mixup-style Training
Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tasks when compared to standard training.
Beyond Lazy Training for Over-parameterized Tensor Decomposition
We show that in a lazy training regime (similar to the NTK regime for neural networks) one needs at least $m = \Omega(d^{l-1})$, while a variant of gradient descent can find an approximate tensor when $m = O^*(r^{2. 5l}\log d)$.
Dissecting Hessian: Understanding Common Structure of Hessian in Neural Networks
We can analyze the properties of these smaller matrices and prove the structure of top eigenspace random 2-layer networks.
Secure Data Sharing With Flow Model
In the classical multi-party computation setting, multiple parties jointly compute a function without revealing their own input data.
Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
Solving this problem using a learning-to-learn approach -- using meta-gradient descent on a meta-objective based on the trajectory that the optimizer generates -- was recently shown to be effective.
No Spurious Local Minima in a Two Hidden Unit ReLU Network
Deep learning models can be efficiently optimized via stochastic gradient descent, but there is little theoretical evidence to support this.
