Page Index - Paper-Reading-Study/2025 GitHub Wiki

37 page(s) in this GitHub Wiki:

• Home
• 2025
• May 2025
• Apr 2025
• Mar 2025
• Feb 2025
• Jan 2025
• [25.01.13] Attention is All You Need
• [25.01.16] Training Large Language Models to Reason in a Continuous Latent Space
• [25.01.20] BERT: Pre‐training of Deep Bidirectional Transformers for Language Understanding
• [25.01.23] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)
• [25.02.03] Learning Transferable Visual Models From Natural Language Supervision (CLIP)
• [25.02.06] Robust Speech Recognition via Large‐Scale Weak Supervision (Whisper)
• [25.02.10] Mamba: Linear‐Time Sequence Modeling with Selective State Spaces
• [25.02.13] RoFormer: Enhanced Transformer with Rotary Position Embedding
• [25.02.17] Direct Preference Optimization: Your Language Model is Secretly a Reward Model
• [25.02.22] NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
• [25.02.24] Auto‐Encoding Variational Bayes
• [25.02.27] Generative Adversarial Nets
• [25.03.04] Deep Dive into LLMs like ChatGPT by Andrej Karpathy
• [25.03.06] Neural Discrete Representation Learning (VQ‐VAE)
• [25.03.10] Denoising Diffusion Probabilistic Models
• [25.03.18] The Llama 3 Herd of Models
• [25.03.26] The Llama 3 Herd of Models(2)
• [25.04.03] KAN: Kolmogorov‐Arnold Networks
• [25.04.07] On the Biology of a Large Language Model
• [25.04.10] Retrieval‐Augmented Generation for Knowledge‐Intensive NLP Tasks
• [25.04.12] Inference‐Time Scaling for Generalist Reward Modeling
• [25.04.14] Welcome to the Era of Experience
• [25.04.17] Solving Olympiad Geometry Without Human Demonstrations (AlphaGeometry)
• [25.04.26] Neural Machine Translation by Jointly Learning to Align and Translate
• [25.04.28] Dropout: A Simple Way to Prevent Neural Networks from Overfitting
• [25.05.03] Trust Region Policy Optimization
• [25.05.08] Layers at Similar Depths Generate Similar Activations Across LLM Architectures
• [25.05.08] Proximal Policy Optimization
• [25.05.12] All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine‐Tuning
• Prompt Template