Transformers Architecture: How Google’s ‘Attention Is All You Need’ Changed Deep Learning Forever
pub.towardsai.net·6h
💬Natural Language Processing
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Deciphering Human Language for Machines: A Developer's Guide to NLP
💬Natural Language Processing
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Continuous Autoregressive Language Models
📱Edge AI
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Regularization Through Reasoning: Systematic Improvements in Language Model Classification via Explanation-Enhanced Fine-Tuning
arxiv.org·7h
📱Edge AI
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Feature Stores 2.0: The Next Frontier of Scalable Data Engineering for AI
hackernoon.com·7h
🎨Design Systems
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An introduction to program synthesis (Part II) - Automatically generating features for machine learning
🎭Program Synthesis
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Post-training methods for language models
developers.redhat.com·1d
💬Prompt Engineering
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Gated DeltaNet (Linear Attention variant in Qwen3-Next and Kimi Linear)
💬Prompt Engineering
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Beyond Standard LLMs
🎯Reinforcement Learning
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'No Free Lunch: Deconstruct Efficient Attention with MiniMax M2'
lmsys.org·1d
📱Edge AI
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Topographical sparse mapping: A training framework for deep learning models
👁️Computer Vision
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Attention ISN'T all you need?! New Qwen3 variant Brumby-14B-Base leverages Power Retention technique
venturebeat.com·16h
⚡Incremental Computation
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Linear Differential Vision Transformer: Learning Visual Contrasts via Pairwise Differentials
arxiv.org·1d
👁️Computer Vision
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Attention Illuminates LLM Reasoning: The Preplan-and-Anchor Rhythm EnablesFine-Grained Policy Optimization
💬Prompt Engineering
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