From Noise to Latent: Generating Gaussian Latents for INR-Based Image Compression
arxiv.org·4d
🧠Neural Compression
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Brand Tagging with VLMs
🕸️WebP Analysis
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Aligning machine and human visual representations across abstraction levels
nature.com·3d
📊Learned Metrics
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ML Systems Textbook by Havard
🧠Machine Learning
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I Measured Neural Network Training Every 5 Steps for 10,000 Iterations
towardsdatascience.com·20h
📊Learned Metrics
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DeepEyesV2 outperforms bigger rivals by favoring tools over sheer knowledge
the-decoder.com·26m
🔍Vector Forensics
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🔥 LLM Interview Series(5): Self-supervised Learning and Next-token Prediction
🤖Grammar Induction
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TSU 101 an New Type of Computing Hardware
⚡Homebrew CPUs
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AMD vs. Intel: a Unicode benchmark
🇨🇳Chinese Computing
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CDT in NLP Distinguished Lecture Series: Dan Roth
informatics.ed.ac.uk·1d
🎭Cultural Informatics
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LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics
🧠Machine Learning
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Goodbye *ibe Coding
🌀Brotli Internals
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Enhanced Level Gauge Data Analysis via Adaptive Fourier Domain Decomposition
📊Spectrograms
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Split-Layer: Enhancing Implicit Neural Representation by Maximizing the Dimensionality of Feature Space
arxiv.org·2d
🧠Machine Learning
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Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory
arxiv.org·2d
🧠Machine Learning
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