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) Synthetic data generation for end-to-end thermal infrared tracking
  2. (2024) ISNet: Integrate Image-Level and Semantic-Level Context for Semantic Segmentation
  3. (2024) Asymmetric Contextual Modulation for Infrared Small Target Detection
  4. (2024) LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
  5. (2024) Training Compute-Optimal Large Language Models
  6. (2024) Scaling Laws for Neural Language Models
  7. (2024) DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
  8. (2024) ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
  9. (2024) Matryoshka Representation Learning
  10. (2023) Large Language Models for Software Engineering: Survey and Open Problems
  11. (2023) High-Resolution Image Synthesis with Latent Diffusion Models
  12. (2023) Universal and Transferable Adversarial Attacks on Aligned Language Models
  13. (2023) Zero-shot Image-to-Image Translation
  14. (2023) InstructPix2Pix: Learning to Follow Image Editing Instructions
  15. (2023) An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
  16. (2023) On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
  17. (2023) Repository-Level Prompt Generation for Large Language Models of Code
  18. (2023) Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
  19. (2023) Calibrate Before Use: Improving Few-Shot Performance of Language Models
  20. (2023) Understanding Deep Learning Requires Rethinking Generalization
  21. (2023) The Implicit Bias of Gradient Descent on Separable Data
  22. (2023) Gradient Descent Provably Optimizes Over-parameterized Neural Networks
  23. (2023) Loss Landscapes and Optimization in Over-Parameterized Non-Linear Systems and Neural Networks
  24. (2023) Extracting Training Data from Large Language Models
  25. (2023) A Watermark for Large Language Models
  26. (2023) Efficiently Modeling Long Sequences with Structured State Spaces
  27. (2023) Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
  28. (2023) Accurate Detection of Wake Word Start and End Using a CNN
  29. (2023) Transformers in Speech Processing: A Survey
  30. (2023) MetaGPT: Meta Programming for Multi-Agent Collaborative Framework
  31. (2023) Improving Language Understanding by Generative Pre-Training (GPT)
  32. (2023) Generative Agents: Interactive Simulacra of Human Behavior
  33. (2023) Simple synthetic data reduces sycophancy in large language models
  34. (2023) Language Models are Unsupervised Multitask Learners (GPT-2)
  35. (2023) Dense Passage Retrieval for Open-Domain Question Answering
  36. (2023) BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
  37. (2023) Evaluating Large Language Models Trained on Code (Codex)
  38. (2023) Training language models to follow instructions with human feedback (InstructGPT)
  39. (2023) Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
  40. (2023) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
  41. (2023) Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
  42. (2023) Deep contextualized word representations (ELMo)