Research Feed Page 37

The authors introduce Surv-IPTB, an attention-based model that improves the estimation of individual treatment benefits in survival analysis by converting the problem into a pairwise classification task.

This paper introduces a scalable framework for estimating VARMA models by decoupling computation from series length, allowing for efficient processing of high-dimensional time series data.

The researchers developed a method to generate synthetic training data for lesion segmentation by interpolating shapes and intensities using Wasserstein barycenters.

The researchers developed a method to stop neural network accumulations early by predicting the final sign of binary activations from partial sums.

The paper introduces a method to simultaneously watermark images and apply unlearnable perturbations that prevent unauthorized model training while allowing authorized users to reverse the protection.

Researchers developed a customized Mask R-CNN model to accurately detect, label, and segment teeth from uncontrolled smartphone dental photographs.

The researchers developed SAGA, an automated pipeline that uses linguistic scoring to align language models for low-resource Nordic languages without needing human preference labels.

The researchers integrate multi-view geometric priors and confidence-based weighting into 3D Gaussian Splatting to fix suboptimal geometry in complex or shiny scenes.

JoyAI-RA 0.5 enables scalable robot manipulation by aligning diverse data sources like human videos and simulation into a shared format for consistent learning.

The researchers developed a selection and curriculum framework that optimizes training environment diversity and difficulty to significantly improve multimodal agent performance.

The authors developed a method for Probabilistic Regression Trees to process missing predictor values natively during tree construction instead of relying on external data imputation.

Researchers developed an agentic AI framework using quantum circuits to predict cardiac arrest mortality from longitudinal patient data with significantly fewer parameters than traditional models.

The paper examines how artificial intelligence in Nigerian mobile applications affects digital sovereignty, evaluated through platform transparency and socio-economic context.

The paper introduces Anacreon, a system that uses specialized adapter modules to prevent large language models from collapsing diverse individual personalities into generic averages.

Researchers developed a systematic framework to audit and measure how large language models exhibit political bias when analyzing international conflicts.

The researchers evaluated if multi-turn LLM conversations can effectively debunk conspiracy theories as they emerge during crisis events.

Researchers developed a highly efficient, expert-validated model for recognizing Bangla sign language that runs locally on commodity mobile hardware.

The paper investigates the lack of rigorous evaluation methods for Explainable Artificial Intelligence and demonstrates that current techniques often fail when applied to evolving data.

Bar-JEPA uses a custom joint-embedding architecture to computationally extract numerical data from bar charts despite visual variability and a lack of real-world training data.

The paper introduces a structured data pipeline that transforms fragmented EHR records into audited clinical features, improving heart failure prediction accuracy.