COVID-19

StarBASE-GP: Biologically-Guided Automated Machine Learning for Genotype-to-Phenotype Association Analysis

We present the Star-Based Automated Single-locus and Epistasis analysis tool - Genetic Programming (StarBASE-GP), an automated framework for discovering meaningful genetic variants associated with phenotypic variation in large-scale genomic datasets. StarBASE-GP uses a genetic programming-based multi-objective optimization strategy to evolve machine learning pipelines that simultaneously maximiz...

Spectral Survival Analysis

Survival analysis is widely deployed in a diverse set of fields, including healthcare, business, ecology, etc. The Cox Proportional Hazard (CoxPH) model is a semi-parametric model often encountered in the literature. Despite its popularity, wide deployment, and numerous variants, scaling CoxPH to large datasets and deep architectures poses a challenge, especially in the high-dimensional regime. ...

Understanding Adversarial Training with Energy-based Models

We aim at using Energy-based Model (EBM) framework to better understand adversarial training (AT) in classifiers, and additionally to analyze the in...

Hypothesis Testing in Imaging Inverse Problems

This paper proposes a framework for semantic hypothesis testing tailored to imaging inverse problems. Modern imaging methods struggle to support hyp...

Adapting Segment Anything Model for Power Transmission Corridor Hazard Segmentation

Power transmission corridor hazard segmentation (PTCHS) aims to separate transmission equipment and surrounding hazards from complex background, con...

Concentrate on Weakness: Mining Hard Prototypes for Few-Shot Medical Image Segmentation

Few-Shot Medical Image Segmentation (FSMIS) has been widely used to train a model that can perform segmentation from only a few annotated images. Ho...

Recent advances in antibody optimization based on deep learning methods.

Antibodies currently comprise the predominant treatment modality for a variety of diseases; therefore, optimizing their properties rapidly and efficie...

A Reinforcement-Learning-Enhanced LLM Framework for Automated A/B Testing in Personalized Marketing

For personalized marketing, a new challenge of how to effectively algorithm the A/B testing to maximize user response is urgently to be overcome. In...

PrivATE: Differentially Private Confidence Intervals for Average Treatment Effects

The average treatment effect (ATE) is widely used to evaluate the effectiveness of drugs and other medical interventions. In safety-critical applica...

InstGenIE: Generative Image Editing Made Efficient with Mask-aware Caching and Scheduling

Generative image editing using diffusion models has become a prevalent application in today's AI cloud services. In production environments, image e...

Accurate Prediction of Open-Circuit Voltages of Lithium-Ion Batteries via Delta Learning.

Accurate prediction of lithium-ion battery capacity before material synthesis is crucial for accelerating battery material discovery. The capacity can...

A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis.

Sepsis is a life-threatening organ dysfunction due to a dysfunctional response to infection. Delays in diagnosis have substantial impact on survival. ...

MotionPro: A Precise Motion Controller for Image-to-Video Generation

Animating images with interactive motion control has garnered popularity for image-to-video (I2V) generation. Modern approaches typically rely on la...

The Missing Point in Vision Transformers for Universal Image Segmentation

Image segmentation remains a challenging task in computer vision, demanding robust mask generation and precise classification. Recent mask-based app...

Rep3D: Re-parameterize Large 3D Kernels with Low-Rank Receptive Modeling for Medical Imaging

In contrast to vision transformers, which model long-range dependencies through global self-attention, large kernel convolutions provide a more effi...

Structure Disruption: Subverting Malicious Diffusion-Based Inpainting via Self-Attention Query Perturbation

The rapid advancement of diffusion models has enhanced their image inpainting and editing capabilities but also introduced significant societal risk...

LlamaSeg: Image Segmentation via Autoregressive Mask Generation

We present LlamaSeg, a visual autoregressive framework that unifies multiple image segmentation tasks via natural language instructions. We reformul...

Predicting high confidence ctDNA somatic variants with ensemble machine learning models.

Circulating tumour DNA (ctDNA) is a minimally invasive cancer biomarker that can be used to inform treatment of cancer patients. The utility of ctDNA ...

AbSet: A Standardized Data Set of Antibody Structures for Machine Learning Applications.

Machine learning algorithms have played a fundamental role in the development of therapeutic antibodies by being trained on data sets of sequences and...

STRICT: Stress Test of Rendering Images Containing Text

While diffusion models have revolutionized text-to-image generation with their ability to synthesize realistic and diverse scenes, they continue to ...