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Papers Explained 342: U-ViTU-ViT is a simple and general ViT-based architecture for image generation with diffusion models, characterized by treating all inputs…9h ago9h ago
Papers Explained 341: U-NetU-Net consists of a contracting path (left) and an expansive path (right).1d ago1d ago
Papers Explained 340: CHASECHallenging AI with Synthetic Evaluations (CHASE) is a unified framework to synthetically generate challenging problems using LLMs without…Mar 28Mar 28
Papers Explained 339: Code Guided Synthetic data generation system (CoSyn)Code Guided Synthetic data generation system (CoSyn) is a framework that leverages the coding capabilities of text-only LLMs to…Mar 27Mar 27
Papers Explained 338: Large-Scale Data Selection for Instruction TuningThis work presents a systematic study of how well data selection methods scale, It finds that:Mar 26Mar 26
Papers Explained 337: Logic-RLThis study explores the potential of rule-based reinforcement learning (RL) in large reasoning models. Synthetic logic puzzles are used as…Mar 25Mar 25
Papers Explained 336: Rethinking Compute-Optimal Test-Time ScalingThis paper focuses on two core questions:Mar 24Mar 24
Papers Explained 335: Transformers without NormalizationThis work demonstrates that Transformers without normalization can achieve the same or better performance using a remarkably simple…Mar 211Mar 211
