Attention and Compression is all you need for Controllably Efficient Language Models
arxiv.org·2d
⚙Technology
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Why Language Models Are “Lost in the Middle”
pub.towardsai.net·1d
⚙Technology
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Hyperdimensional Decoding of Spiking Neural Networks
arxiv.org·3h
⚙Technology
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Fine-tune VLMs for multipage document-to-JSON with SageMaker AI and SWIFT
aws.amazon.com·1d
⚙Technology
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Adaptive Sample-Level Framework Motivated by Distributionally Robust Optimization with Variance-Based Radius Assignment for Enhanced Neural Network Generalizati...
arxiv.org·1d
🦀Rust
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Automated Invoice Data Extraction: Using LLM and OCR
arxiv.org·1d
⚙Technology
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Reasoning Is All You Need for Urban Planning AI
arxiv.org·2d
⚙Technology
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Alphabet: AI Supercycle Powered By Cloud, CapEx, And TPUs
seekingalpha.com·1d
⚙Technology
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I built a runtime for Ai models to develop their own identity over time... And they remember, even when you swap out models.
⚙Technology
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GastroDL-Fusion: A Dual-Modal Deep Learning Framework Integrating Protein-Ligand Complexes and Gene Sequences for Gastrointestinal Disease Drug Discovery
arxiv.org·1d
🦀Rust
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High-dimensional Bayesian filtering through deep density approximation
arxiv.org·1d
⚙Technology
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Maestro: Learning to Collaborate via Conditional Listwise Policy Optimization for Multi-Agent LLMs
arxiv.org·1d
⚙Technology
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MoM – Mixture of Model Service
🦀Rust
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Generalization in Representation Models via Random Matrix Theory: Application to Recurrent Networks
arxiv.org·2d
🦀Rust
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Parametric Pareto Set Learning for Expensive Multi-Objective Optimization
arxiv.org·1d
⚙Technology
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