Meta’s Generative Ads Model (GEM): The Central Brain Accelerating Ads Recommendation AI Innovation
engineering.fb.com·2d·
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What Changed? Pin-pointing behavior shift
world.hey.com·1d·
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I Am Calling It: Palantir, Not OpenAI, Is Winning The AI Race
seekingalpha.com·1d
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Don’t Fight the Weights
dbreunig.com·1d·
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From Zero to LLMOps Hero: Your 101 Guide to Running LLMs in Production
analyticsvidhya.com·2d
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Sequential Human Assembly and Disassembly Motions in Human-Robot Coexisting Environments
nature.com·1d
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Garbage In, Garbage Out: The Case for Better Robot Data Understanding
huggingface.co·2d·
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AI-Powered Websites: The Complete Guide to Transforming Your Business in 2025
dev.to·12h·
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LLM-Guided Reinforcement Learning with Representative Agents for Traffic Modeling
arxiv.org·1d
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Federated AI's Achilles' Heel: Can Collaborative Teaching Fix Data Corruption?
dev.to·3h·
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Optimized Lamination Mixer Design via Surrogate Modeling & Reinforcement Learning
dev.to·2d·
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How Machines See: The Power of Computer Vision in AI (Explained for Developers)
dev.to·2d·
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Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators
arxiv.org·2d
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Running Out of Data: How Synthetic Data is Saving the Future of AI
dev.to·1d·
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Symbol Detection in Multi-channel Multi-tag Ambient Backscatter Communication Under IQ Imbalance
arxiv.org·23h
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Dynamic Neuro-Network Resilience via Stochastic Gradient Amplification and Adaptive Sparsity (DNSAS)
dev.to·6d·
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Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compression
arxiv.org·23h
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CG-TTRL: Context-Guided Test-Time Reinforcement Learning for On-Device Large Language Models
arxiv.org·1d
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