ML Paper Summaries

Summaries and critiques of papers (mostly in machine learning) I’ve read. This is not a summary of the traditional sense which will carefully go over all the major concepts in the paper (due to time constraints); instead, it will be rather concise and only contain the key points that I find interesting, with the expectation that the reader already has some familiarity with the paper.

This serves to both catalog my own reading and academic progress, and may also be of interest to others to find interesting papers to check out.

The format is inspired by the paper summaries.

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  1. (2024) Asymmetric Contextual Modulation for Infrared Small Target Detection
  2. (2024) LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
  3. (2024) Training Compute-Optimal Large Language Models
  4. (2024) Scaling Laws for Neural Language Models
  5. (2024) DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
  6. (2024) ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
  7. (2024) Matryoshka Representation Learning
  8. (2023) Large Language Models for Software Engineering: Survey and Open Problems
  9. (2023) High-Resolution Image Synthesis with Latent Diffusion Models
  10. (2023) Universal and Transferable Adversarial Attacks on Aligned Language Models
  11. (2023) Zero-shot Image-to-Image Translation
  12. (2023) InstructPix2Pix: Learning to Follow Image Editing Instructions
  13. (2023) An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
  14. (2023) On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
  15. (2023) Repository-Level Prompt Generation for Large Language Models of Code
  16. (2023) Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
  17. (2023) Calibrate Before Use: Improving Few-Shot Performance of Language Models
  18. (2023) Understanding Deep Learning Requires Rethinking Generalization
  19. (2023) The Implicit Bias of Gradient Descent on Separable Data
  20. (2023) Gradient Descent Provably Optimizes Over-parameterized Neural Networks
  21. (2023) Loss Landscapes and Optimization in Over-Parameterized Non-Linear Systems and Neural Networks
  22. (2023) Extracting Training Data from Large Language Models
  23. (2023) A Watermark for Large Language Models
  24. (2023) Efficiently Modeling Long Sequences with Structured State Spaces
  25. (2023) Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
  26. (2023) Accurate Detection of Wake Word Start and End Using a CNN
  27. (2023) Transformers in Speech Processing: A Survey
  28. (2023) MetaGPT: Meta Programming for Multi-Agent Collaborative Framework
  29. (2023) Improving Language Understanding by Generative Pre-Training (GPT)
  30. (2023) Generative Agents: Interactive Simulacra of Human Behavior
  31. (2023) Simple synthetic data reduces sycophancy in large language models
  32. (2023) Language Models are Unsupervised Multitask Learners (GPT-2)
  33. (2023) Dense Passage Retrieval for Open-Domain Question Answering
  34. (2023) BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
  35. (2023) Evaluating Large Language Models Trained on Code (Codex)
  36. (2023) Training language models to follow instructions with human feedback (InstructGPT)
  37. (2023) Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  38. (2023) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  39. (2023) Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
  40. (2023) Deep contextualized word representations (ELMo)