All Articles
16630 articles total
Benchmarking Machine Learning Models for Multi-Omics-Based Breast Cancer Prediction
Benchmarks classical machine learning models for breast cancer ER status prediction using multi-omics data, finding Random Forest to be most effective.
SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling
Proposes SOS-LoRA, a parameter-efficient fine-tuning method that uses static orthogonal subspaces to reduce interference during LLM optimization.
Comprehensive Evaluation of Machine Learning for Type 2 Diabetes Risk Prediction: Large-Scale External Validation and Fairness Analysis
Evaluates ML-based Type 2 diabetes risk prediction models, highlighting performance loss and fairness gaps across different demographic groups.
New US homeownership measure puts people first
A discussion on a new US homeownership measure focused on prioritizing people.
Python 3.15's Ultra-Low Overhead Interpreter Profiling Mode – Ken Jin's Blog
Exploration of Python 3.15's new ultra-low overhead interpreter profiling mode.
Quantizing Recursive Reasoning Models
Research on quantizing recursive reasoning models, proposing per-block scaling via MXInt4 to prevent accuracy collapse in 4-bit formats.
Diffusion-corrected Autoregressive Fourier Neural Operator for Droplet Evolution Prediction
Introduction of DiffARFNO, a framework combining Fourier Neural Operators and Diffusion models for high-fidelity droplet evolution prediction.
Normalized Rewards for Preference Optimization
A study on normalized rewards for preference optimization to prevent over-optimization in LLM alignment algorithms like DPO and SimPO.
KernelBench-Verified: Do LLM-Generated Kernels Actually Beat PyTorch?
Analysis showing that LLM-generated CUDA kernels often reward-hack and do not consistently outperform PyTorch when evaluated with realistic baselines.
Atlassian: Why AI speeds up employees but not organizations
Atlassian discusses how organizations fail to see AI ROI by focusing on individual productivity rather than redesigning team workflows and shared context.
Comparing Spectrogram Front-Ends for Abnormal Heart-Sound Detection with a Convolutional Neural Network
A study comparing spectrogram front-ends for abnormal heart-sound detection using CNNs, finding PCEN and multi-resolution spectrograms outperform standard logmel.
Fully-sensorized smart-eyewear platform for on-device Machine Learning
ARGO is a smart eyewear platform utilizing the STM32N6 NPU for on-device ML and real-time urban obstacle recognition with an optimized YOLOv11 model.
International Agreements to Limit Frontier AI: Objectives and Exit
A research paper proposing frameworks and conditions for international agreements to limit the development of frontier AI to mitigate global risks.
RouteCost: A Production-Inspired Multi-Stage Framework for Pre-Order Shipping Cost Estimation in E-Commerce
RouteCost is introduced as a multi-stage framework for improving pre-order shipping cost estimation in e-commerce through demand forecasting and residual correction.
From Weights to Words: Expressing and Editing Preference Model Inferences in Natural Language
The 'weights to words' method allows users to inspect and edit preference model inferences by translating high-dimensional choice data into natural language dimensions.
Token-Level Cross-Modal Transformer with Contrastive Multi-Task Learning for Breast Cancer Subtype Classification and Survival Prediction
A paper proposing a token-level cross-modal transformer for joint breast cancer subtype classification and survival prediction using genomic and clinical data.
HantaWatch: Federated Learning for Hantavirus Genomic Surveillance
HantaWatch is a federated learning framework designed for collaborative Hantavirus genomic surveillance without sharing raw sensitive data.
OpenMHC: Accelerating the Science of Wearable Foundation Models
The OpenMHC project releases the largest open-access wearable health dataset and open-source foundation model implementations to democratize wearable AI research.
The Failures of Marginal Influence-Based Attribution Methods for Global Time Series Explanations
Research demonstrating that common time-series attribution methods like SHAP fail to be DAG-faithful, conflating direct and mediated temporal dependencies.
The Growing Compute Shortage
A Hacker News discussion regarding the increasing scarcity of compute resources for AI development.