All Articles
17030 articles total
Mitigating The Effect of Class Imbalance in Data with Hierarchical and Dependable Structure
Proposes a Hierarchy-Aware RoBERTa framework to improve CWE vulnerability classification by incorporating structural information.
Are we Merging the Right Models? Impact of Expert Training Duration on Model Merging for LLMs
Studies how training duration of domain experts affects the quality of multi-task model merging for LLMs.
HPC-Enabled Video-based Coastal Wave Parameter Estimation Using V-JEPA and Deep Spatiotemporal Learning
An HPC-enabled deep learning framework for estimating coastal wave parameters from monocular coastal video.
Who's running all those tiny RPKI servers?
A community discussion on Hacker News regarding the operational infrastructure and entities responsible for maintaining the numerous small Resource Public Key Infrastructure (RPKI) servers.
BattVAE-GP: Generative Modeling of Long-Horizon Battery Degradation with Uncertainty Quantification
Introduction of BattVAE-GP, a hybrid physics-probabilistic learning framework for efficient, uncertainty-aware surrogate modeling of lithium-ion battery degradation trajectories.
Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite
A generalized distribution-free semi-supervised learning framework using risk rewriting to provide unbiased risk estimators for multiclass classification with reduced variance.
BAT-RM: A Boundary-Aware Transformer with Region-Aware Multi-Directional Mamba for Clinically Deployed Cervical Cancer Radiotherapy Auto-Contouring
BAT-RM, a boundary-aware Transformer and Mamba hybrid architecture, is deployed for cervical cancer radiotherapy auto-contouring, significantly reducing contouring time and improving consistency.
Scale-Aware Attention for Scarce Neural Data: An RG-Flow Transformer on Sleep-EDF EEG
Evaluation of an RG-Flow Transformer with renormalization-group inductive bias for EEG sleep staging, showing limited performance gains but improved interpretability of spectral exponents.
Graph-Constrained Policy Learning for Extreme Clinical Code Prediction
A graph-constrained traversal policy using language models to predict clinical ICD-10-CM codes, outperforming flat multi-label classification in extreme label spaces.
Exact and Certified Data Shapley for Weighted k-Nearest-Neighbor Regression and Soft-Label Prediction
Introduction of the first pseudo-polynomial-time exact algorithm for weighted KNN-regression Data Shapley, including a certified FPTAS and an open-source CPU-only library.
Evaluating Reliability in Machine Learning Models for Early Chronic Kidney Disease Prediction: A Systematic Review of Data Leakage and Predictor Stability
A systematic review of kidney disease prediction ML models, revealing that high data leakage often leads to inflated performance metrics and low predictor stability.
Beyond Coordinate Gauge: An Audited Protocol for Detecting Donor-Specific Functional Fingerprints after Neural Collapse
An audited protocol for detecting donor-specific functional fingerprints in neural networks after neural collapse, establishing detectability under specific alignment mappings.
Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems
An enhanced in-context learning framework using a Transformer backbone for pilot-based beamforming in MU-MISO communication systems, improving adaptation to unseen channel models.
Surprising lessons from my research scientist job search
A personal account and discussion regarding the challenges and lessons learned during a research scientist job search.
Starlink’s V5 dish is now available — here’s how it compares
SpaceX releases the Starlink V5 residential dish, featuring a smaller, lighter design and improved power efficiency over the V4.
Burst Spiking Neural Networks
Introduction of Burst Spiking Neural Networks (BuSNNs), which improve accuracy and robustness in low-power SNNs through burst firing and dynamic weight constraints.
QDEvo: A Multi-Objective Quality-Diversity Framework for Automated Heuristic Design
QDEvo is a new multi-objective framework that uses LLMs and quality-diversity evolution to design semantically diverse and high-performing heuristics for combinatorial optimization.
AAAI-26 Dual Submissions: Novel Challenges
AAAI-26 organizers report a significant rise in dual submissions, often masked by generative AI, and propose new detection tools and policies.
Do You Remember? Toward Memory-Centric Multimodal AI
The DoYouRemember architecture introduces reconstructive memory to MLLMs using VQ-VAE and a Diffusion Decoder to address the lack of visual memory in LLMs.
Sensitivity to Subjective Expected Utility Maximization: A Methodological Study, with an Illustrative Application to LLM Decision-Making
A methodological study on subjective expected utility (SEU) maximization in LLM decision-making, analyzing how models like GPT-4o and Claude 3.5 Sonnet handle uncertainty.