The Three Ages of Data Science: When to Use Traditional Machine Learning, Deep Learning, or an LLM (Explained with One Example)
towardsdatascience.com·1d
🧠Machine Learning
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Novel Earth System Modeling: High-Dimensional Ensemble Calibration via Bayesian Sparsity
⏱️Time Series Analysis
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**Automated Serialization Traceability & Authentication via Embedded Bio-Markers**
🔐Cryptography
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SpatialThinker: Reinforcing 3D Reasoning in Multimodal LLMs via Spatial Rewards
arxiv.org·1d
🌳Tree-sitter
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When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs
arxiv.org·1d
📱Edge AI
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Prioritizing Perception-Guided Self-Supervision: A New Paradigm for Causal Modeling in End-to-End Autonomous Driving
arxiv.org·20h
🎯Reinforcement Learning
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Anatomy-VLM: A Fine-grained Vision-Language Model for Medical Interpretation
arxiv.org·20h
📱Edge AI
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CC30k: A Citation Contexts Dataset for Reproducibility-Oriented Sentiment Analysis
arxiv.org·20h
📝NLP
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LLMs vs. Traditional Sentiment Tools in Psychology: An Evaluation on Belgian-Dutch Narratives
arxiv.org·20h
💬Natural Language Processing
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Enhancing Polymer Composites via AI-Driven Reactive Extrusion Parameter Optimization
💬Prompt Engineering
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Adaptive Time Budgets for Safe and Comfortable Vehicle Control Transition in Conditionally Automated Driving
arxiv.org·1d
🎯Reinforcement Learning
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