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Scoured 7195 posts in 18.3 ms
Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation  🎛️Feed Filtering
arxiv.org·6d
Three Cobblers, One Zhuge Liang: Making Cheaper Models Work Together  🪄Prompt Engineering
markhuang.ai·21h·Hacker News
House panels probe Airbnb, Anysphere over use of Chinese AI models  🇨🇳Chinese AI
nextgov.com·1d·Hacker News
A chemistry lab that runs itself to find the perfect reaction  🔬AI Labs
nature.com
·3d·Hacker News
Improving Diversity in Black-box Few-shot Knowledge Distillation  📚RAG
arxiv.org·1d
U.S. Orders Global Diplomatic Warning on Chinese ‘Distillation’…  🇨🇳China Tech Policy
underlines.news
·4d·Hacker News
PAINT: Partial-Solution Adaptive Interpolated Training for Self-Distilled Reasoners  ⚙️MLOps
arxiv.org·17h
Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models  🤖LLM
arxiv.org·17h
On the Memorization of Consistency Distillation for Diffusion Models  🤖LLM
arxiv.org·2d
GaitKD: A Universal Decoupled Distillation Framework for Efficient Gait Recognition  ✨Gemini
arxiv.org·17h
The Surprising Effectiveness of Canonical Knowledge Distillation for Semantic Segmentation  ✨Gemini
arxiv.org·1d
Edge AI for Automotive Vulnerable Road User Safety: Deployable Detection via Knowledge Distillation  ⚡Edge AI
arxiv.org·17h
S-SONDO: Self-Supervised Knowledge Distillation for General Audio Foundation Models  ✨Gemini
arxiv.org·1d
On-the-fly LTLf Synthesis under Partial Observability  🪄Prompt Engineering
arxiv.org·17h
Knowledge Distillation Must Account for What It Loses  🔢BitNet
arxiv.org·1d
Efficient Diffusion Distillation via Embedding Loss  🔢BitNet
arxiv.org·3d
Select to Think: Unlocking SLM Potential with Local Sufficiency  ✨LLMs
arxiv.org·17h
Diverse Image Priors for Black-box Data-free Knowledge Distillation  ✨Gemini
arxiv.org·1d
AlphaJet: Automated Conceptual Aircraft Synthesis via Disentangled Generative Priors and Topology-Preserving Evolutionary Search  🔍AI Interpretability
arxiv.org·17h
Aligning Dense Retrievers with LLM Utility via DistillationAligning Dense Retrievers with LLM Utility via Distillation  📚RAG
arxiv.org·3d

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