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When Distance Distracts: Representation Distance Bias in BT-Loss for Reward Models
arxiv.org·15h
🧠Machine Learning
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Words that make language models perceive
sophielwang.com·1d·
Discuss: Hacker News
🧠Neural Codecs
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How Pinterest Built a Real‑Time Radar for Violative Content using AI
medium.com·1d
📰Content Curation
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Creating a Llama or GPT Model for Next-Token Prediction
machinelearningmastery.com·1d
💻Local LLMs
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Dual-Stream Cross-Modal Representation Learning via Residual Semantic Decorrelation
arxiv.org·15h
🧮Vector Embeddings
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Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception
arxiv.org·1d
🧠Learned Codecs
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Task-Model Alignment: A Simple Path to Generalizable AI-Generated Image Detection
arxiv.org·15h
🤖Advanced OCR
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DIST-CLIP: Arbitrary Metadata and Image Guided MRI Harmonization via Disentangled Anatomy-Contrast Representations
arxiv.org·15h
🕳️Persistent Homology
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Fast training and sampling of Restricted Boltzmann Machines
arxiv.org·15h
🧠Machine Learning
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COIVis: Eye tracking-based Visual Exploration of Concept Learning in MOOC Videos
arxiv.org·15h
🗺️Competency Maps
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Evaluating and Preserving High-level Fidelity in Super-Resolution
arxiv.org·15h
🖼️JPEG XL
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Can a 14B Model Match a 100B+ Model? We Fine-Tuned 8 Models to Find Out
orq.ai·4h·
Discuss: Hacker News
🧠Intelligence Compression
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Arc Gradient Descent: A Mathematically Derived Reformulation of Gradient Descent with Phase-Aware, User-Controlled Step Dynamics
arxiv.org·15h
🌀Riemannian Computing
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Guidance: A cheat code for diffusion models
sander.ai·1d·
Discuss: Hacker News
🧠Machine Learning
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Generating Storytelling Images with Rich Chains-of-Reasoning
arxiv.org·15h
🧭Content Discovery
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Asymptotic analysis of shallow and deep forgetting in replay with Neural Collapse
arxiv.org·15h
🧮Kolmogorov Complexity
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Training, Decoding, and Hallucination in Large Language Models: A Deep Dive
dev.to·2d·
Discuss: DEV
🤖Grammar Induction
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The Impact of Data Characteristics on GNN Evaluation for Detecting Fake News
arxiv.org·15h
🧠Machine Learning
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DEAR: Dataset for Evaluating the Aesthetics of RenderingDEAR: Dataset for Evaluating the Aesthetics of Rendering
arxiv.org·1d
🏛Digital humanities
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Utility Boundary of Dataset Distillation: Scaling and Configuration-Coverage Laws
arxiv.org·1d
🧠Machine Learning
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