146 Episodo

  1. #143 – SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

    Publicado: 11/12/2024
  2. #142: An Empirical Study of Rust-for-Linux: The Success, Dissatisfaction, and Compromise

    Publicado: 4/12/2024
  3. #141: SQL Has Problems. We Can Fix Them: Pipe Syntax In SQL

    Publicado: 13/11/2024
  4. #140: GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

    Publicado: 24/10/2024
  5. #139: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

    Publicado: 2/10/2024
  6. #138: Distilling the Knowledge in a Neural Network

    Publicado: 11/9/2024
  7. #137: Optimal Quantile Approximation in Streams

    Publicado: 13/8/2024
  8. #136: Distinct Elements in Streams: An Algorithm for the (Text) Book

    Publicado: 7/8/2024
  9. #135: In-Datacenter Performance Analysis of a Tensor Processing Unit

    Publicado: 2/7/2024
  10. #134: LoRA: Low-Rank Adaptation of Large Language Models

    Publicado: 9/6/2024
  11. #133: Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations

    Publicado: 21/5/2024
  12. #132: High-Resolution Image Synthesis with Latent Diffusion Models

    Publicado: 2/5/2024
  13. #131: FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

    Publicado: 23/4/2024
  14. #130: Diffusion models from scratch, from a new theoretical perspective

    Publicado: 5/4/2024
  15. #129: Programming Massively Parallel Processors (Ch.4- Ch.6)

    Publicado: 26/3/2024
  16. #128: Faiss: A library for efficient similarity search and clustering of dense vectors.

    Publicado: 9/3/2024
  17. #127: Programming Massively Parallel Processors (Ch.1- Ch.3)

    Publicado: 29/2/2024
  18. #126: Vector Database Management Systems

    Publicado: 30/1/2024
  19. #125: Always-on Vision Processing Unit for Mobile Applications

    Publicado: 23/1/2024
  20. #124: GAIA: a benchmark for General AI Assistants

    Publicado: 22/12/2023

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森田と向井がコンピュータサイエンス周辺の論文をなんとなく読んで感心する Podcast です。