Machine Learning

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Machine Learning

[D] How does distributed proof of work computing handle the coordination needs of neural network training?

[D] Ive been trying to understand the technical setup of a project called Qubic. It claims to use distributed proof of work computing for...

Reddit - Machine Learning · 1 min ·
Machine Learning

[R] VLMs Behavior for Long Video Understanding

I have extensively searched on long video understanding datasets such as Video-MME, MLVU, VideoBench, LongVideoBench and etc. What I have...

Reddit - Machine Learning · 1 min ·
Llms

My AI spent last night modifying its own codebase

I've been working on a local AI system called Apis that runs completely offline through Ollama. During a background run, Apis identified ...

Reddit - Artificial Intelligence · 1 min ·

All Content

[2603.26465] A Boltzmann-machine-enhanced Transformer For DNA Sequence Classification
Machine Learning

[2603.26465] A Boltzmann-machine-enhanced Transformer For DNA Sequence Classification

Abstract page for arXiv paper 2603.26465: A Boltzmann-machine-enhanced Transformer For DNA Sequence Classification

arXiv - AI · 4 min ·
[2603.26461] Neuro-Symbolic Process Anomaly Detection
Machine Learning

[2603.26461] Neuro-Symbolic Process Anomaly Detection

Abstract page for arXiv paper 2603.26461: Neuro-Symbolic Process Anomaly Detection

arXiv - AI · 4 min ·
[2603.26440] Interpretable long-term traffic modelling on national road networks using theory-informed deep learning
Machine Learning

[2603.26440] Interpretable long-term traffic modelling on national road networks using theory-informed deep learning

Abstract page for arXiv paper 2603.26440: Interpretable long-term traffic modelling on national road networks using theory-informed deep ...

arXiv - Machine Learning · 4 min ·
[2603.26415] KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching
Machine Learning

[2603.26415] KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching

Abstract page for arXiv paper 2603.26415: KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching

arXiv - AI · 4 min ·
[2603.26389] Maintaining Difficulty: A Margin Scheduler for Triplet Loss in Siamese Networks Training
Machine Learning

[2603.26389] Maintaining Difficulty: A Margin Scheduler for Triplet Loss in Siamese Networks Training

Abstract page for arXiv paper 2603.26389: Maintaining Difficulty: A Margin Scheduler for Triplet Loss in Siamese Networks Training

arXiv - Machine Learning · 4 min ·
[2603.26378] Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards
Machine Learning

[2603.26378] Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Evaluation Standards

Abstract page for arXiv paper 2603.26378: Generative Modeling in Protein Design: Neural Representations, Conditional Generation, and Eval...

arXiv - AI · 4 min ·
[2603.26363] A Formal Framework for Uncertainty Analysis of Text Generation with Large Language Models
Llms

[2603.26363] A Formal Framework for Uncertainty Analysis of Text Generation with Large Language Models

Abstract page for arXiv paper 2603.26363: A Formal Framework for Uncertainty Analysis of Text Generation with Large Language Models

arXiv - Machine Learning · 3 min ·
[2603.26308] D-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic Functional Connectivity
Machine Learning

[2603.26308] D-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic Functional Connectivity

Abstract page for arXiv paper 2603.26308: D-GATNet: Interpretable Temporal Graph Attention Learning for ADHD Identification Using Dynamic...

arXiv - Machine Learning · 4 min ·
[2603.26264] Topology-Aware Graph Reinforcement Learning for Energy Storage Systems Optimal Dispatch in Distribution Networks
Machine Learning

[2603.26264] Topology-Aware Graph Reinforcement Learning for Energy Storage Systems Optimal Dispatch in Distribution Networks

Abstract page for arXiv paper 2603.26264: Topology-Aware Graph Reinforcement Learning for Energy Storage Systems Optimal Dispatch in Dist...

arXiv - Machine Learning · 4 min ·
[2603.26254] Improving Risk Stratification in Hypertrophic Cardiomyopathy: A Novel Score Combining Echocardiography, Clinical, and Medication Data
Machine Learning

[2603.26254] Improving Risk Stratification in Hypertrophic Cardiomyopathy: A Novel Score Combining Echocardiography, Clinical, and Medication Data

Abstract page for arXiv paper 2603.26254: Improving Risk Stratification in Hypertrophic Cardiomyopathy: A Novel Score Combining Echocardi...

arXiv - Machine Learning · 4 min ·
[2603.26249] Knowledge Distillation for Efficient Transformer-Based Reinforcement Learning in Hardware-Constrained Energy Management Systems
Machine Learning

[2603.26249] Knowledge Distillation for Efficient Transformer-Based Reinforcement Learning in Hardware-Constrained Energy Management Systems

Abstract page for arXiv paper 2603.26249: Knowledge Distillation for Efficient Transformer-Based Reinforcement Learning in Hardware-Const...

arXiv - Machine Learning · 4 min ·
[2603.26178] Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow
Machine Learning

[2603.26178] Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow

Abstract page for arXiv paper 2603.26178: Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via R...

arXiv - Machine Learning · 3 min ·
[2603.26177] Can AI Scientist Agents Learn from Lab-in-the-Loop Feedback? Evidence from Iterative Perturbation Discovery
Llms

[2603.26177] Can AI Scientist Agents Learn from Lab-in-the-Loop Feedback? Evidence from Iterative Perturbation Discovery

Abstract page for arXiv paper 2603.26177: Can AI Scientist Agents Learn from Lab-in-the-Loop Feedback? Evidence from Iterative Perturbati...

arXiv - Machine Learning · 4 min ·
[2603.26164] DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models
Llms

[2603.26164] DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models

Abstract page for arXiv paper 2603.26164: DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models

arXiv - Machine Learning · 4 min ·
[2603.26140] On the Complexity of Optimal Graph Rewiring for Oversmoothing and Oversquashing in Graph Neural Networks
Machine Learning

[2603.26140] On the Complexity of Optimal Graph Rewiring for Oversmoothing and Oversquashing in Graph Neural Networks

Abstract page for arXiv paper 2603.26140: On the Complexity of Optimal Graph Rewiring for Oversmoothing and Oversquashing in Graph Neural...

arXiv - AI · 3 min ·
[2603.26138] PruneFuse: Efficient Data Selection via Weight Pruning and Network Fusion
Machine Learning

[2603.26138] PruneFuse: Efficient Data Selection via Weight Pruning and Network Fusion

Abstract page for arXiv paper 2603.26138: PruneFuse: Efficient Data Selection via Weight Pruning and Network Fusion

arXiv - Machine Learning · 4 min ·
[2603.26136] PEANUT: Perturbations by Eigenvalue Alignment for Attacking GNNs Under Topology-Driven Message Passing
Machine Learning

[2603.26136] PEANUT: Perturbations by Eigenvalue Alignment for Attacking GNNs Under Topology-Driven Message Passing

Abstract page for arXiv paper 2603.26136: PEANUT: Perturbations by Eigenvalue Alignment for Attacking GNNs Under Topology-Driven Message ...

arXiv - Machine Learning · 4 min ·
[2603.26135] TinyML for Acoustic Anomaly Detection in IoT Sensor Networks
Machine Learning

[2603.26135] TinyML for Acoustic Anomaly Detection in IoT Sensor Networks

Abstract page for arXiv paper 2603.26135: TinyML for Acoustic Anomaly Detection in IoT Sensor Networks

arXiv - Machine Learning · 3 min ·
[2603.26114] DPD-Cancer: Explainable Graph-based Deep Learning for Small Molecule Anti-Cancer Activity Prediction
Machine Learning

[2603.26114] DPD-Cancer: Explainable Graph-based Deep Learning for Small Molecule Anti-Cancer Activity Prediction

Abstract page for arXiv paper 2603.26114: DPD-Cancer: Explainable Graph-based Deep Learning for Small Molecule Anti-Cancer Activity Predi...

arXiv - AI · 4 min ·
[2603.26108] Accurate Precipitation Forecast by Efficiently Learning from Massive Atmospheric Variables and Unbalanced Distribution
Machine Learning

[2603.26108] Accurate Precipitation Forecast by Efficiently Learning from Massive Atmospheric Variables and Unbalanced Distribution

Abstract page for arXiv paper 2603.26108: Accurate Precipitation Forecast by Efficiently Learning from Massive Atmospheric Variables and ...

arXiv - Machine Learning · 4 min ·
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