Heartbeats in Distributed Systems
arpitbhayani.me·14h
👩‍💻Programming
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Pi Compute Modules Make for Compact Cluster
hackaday.com·2h
👩‍💻Programming
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AI’s Hidden Weak Spot: How Hackers Are Turning Smart Assistants into Secret Spies
blogger.com·4h
🤖AI
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<p>**Abstract:** This paper introduces Causal Contrastive Learning (CCL), a novel framework for generating more robust and interpretable counterfactual explanat...
freederia.com·22h
📈Machine Learning
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When Will We Make God?
unchartedterritories.tomaspueyo.com·1d
🤖AI
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Builder Design Pattern and Cyclomatic Complexity Reduction
johnjr.dev·1d·
Discuss: Hacker News
👩‍💻Programming
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Proof-Carrying Plans: Guaranteeing AI Action by Arvind Sundararajan
dev.to·6h·
Discuss: DEV
🤖AI
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Automated Spectral Analysis & Predictive Maintenance for UV-A LED Degradation in Industrial Curing Systems
dev.to·2d·
Discuss: DEV
📈Machine Learning
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Can Training Dynamics of Scale-Invariant Neural Networks Be Explained by the Thermodynamics of an Ideal Gas?
arxiv.org·1d
📈Machine Learning
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I Built Convodb So You Can Talk to Your Database (And It Talks Back With 4 AI Agents)
dev.to·2d·
Discuss: DEV
🗄️Databases
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Physics-Informed Neural Operators for Cardiac Electrophysiology
arxiv.org·9h
📈Machine Learning
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Nested Learning: The Illusion of Deep Learning Architectures
arxiviq.substack.com·1d·
Discuss: Substack
📈Machine Learning
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RELEAP: Reinforcement-Enhanced Label-Efficient Active Phenotyping for Electronic Health Records
arxiv.org·9h
📈Machine Learning
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Very cool blog by @character_ai diving into how they trained their proprietary model Kaiju (13B, 34B, 110B), before switching to OSS model, and spoiler: it has...
threadreaderapp.com·19h
📈Machine Learning
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ConeGS: Error-Guided Densification Using Pixel Cones for Improved Reconstruction with Fewer Primitives
arxiv.org·1d
📈Machine Learning
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An Adaptive Machine Learning Triage Framework for Predicting Alzheimer's Disease Progression
arxiv.org·1d
📈Machine Learning
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Optimized Lamination Mixer Design via Surrogate Modeling & Reinforcement Learning
dev.to·1d·
Discuss: DEV
📈Machine Learning
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Quantifying Edits Decay in Fine-tuned LLMs
arxiv.org·1d
📈Machine Learning
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EncouRAGe: Evaluating RAG Local, Fast, and Reliable
arxiv.org·2d
📈Machine Learning
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Consistency Is Not Always Correct: Towards Understanding the Role of Exploration in Post-Training Reasoning
arxiv.org·1d
📈Machine Learning
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