Misreading Chat podcast

Innhold levert av Hajime Morrita , Jun Mukai. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Hajime Morrita , Jun Mukai eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.

Exile

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Episode 10: From Refugee to Radical 44:09

for 2 years siden44:09

Spill senere

Lister

Lik

Likt

44:09

When a young Eva Kollisch arrives as a refugee in New York in 1940, she finds a community among socialists who share her values and idealism. She soon discovers ‘the cause’ isn’t as idyllic as it seems. Little does she know this is the beginning of a lifelong commitment to activism and her determination to create radical change in ways that include belonging, love and one's full self. In addition to Eva Kollisch’s memoirs Girl in Movement (2000) and The Ground Under My Feet (2014), LBI’s collections include an oral history interview with Eva conducted in 2014 and the papers of Eva’s mother, poet Margarete Kolllisch, which document Eva’s childhood experience on the Kindertransport. Learn more at www.lbi.org/kollisch . Exile is a production of the Leo Baeck Institute , New York | Berlin and Antica Productions . It’s narrated by Mandy Patinkin. Executive Producers include Katrina Onstad, Stuart Coxe, and Bernie Blum. Senior Producer is Debbie Pacheco. Associate Producers are Hailey Choi and Emily Morantz. Research and translation by Isabella Kempf. Sound design and audio mix by Philip Wilson, with help from Cameron McIver. Theme music by Oliver Wickham. Voice acting by Natalia Bushnik. Special thanks to the Kollisch family for the use of Eva’s two memoirs, “Girl in Movement” and “The Ground Under My Feet”, the Sophia Smith Collection at Smith College and their “Voices of Feminism Oral History Project”, and Soundtrack New York.…

Misreading Chat

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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。

147 episoder

#コンピューターサイエンス #テクノロジー #Computer Science #Hajime Morrita, Jun Mukai

Misreading Chat

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updated for ca. et år siden

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Serier hjem•Feed

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。

147 episoder

#コンピューターサイエンス #テクノロジー #Computer Science #Hajime Morrita, Jun Mukai

모든 에피소드

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#143 – SWE-bench: Can Language Models Resolve Real-World GitHub Issues? 36:07

for 13 weeks siden36:07

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GitHub の Issue を読んでバグを直すエーアイについて森田が読みました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 [2310.06770] SWE-bench: Can Language Models Resolve Real-World GitHub Issues? [2405.15793] SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering SWE-bench Introducing SWE-bench Verified | OpenAI The new Claude 3.5 Sonnet, Computer Use, and Building SOTA Agents — with Erik Schluntz, Anthropic…

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#142: An Empirical Study of Rust-for-Linux: The Success, Dissatisfaction, and Compromise 54:05

for 14 weeks siden54:05

54:05

Rust を Linux カーネルへで使う取り組みの進捗を向井がウォッチしました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 An Empirical Study of Rust-for-Linux: The Success, Dissatisfaction, and Compromise | USENIX Rust for Linux

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#141: SQL Has Problems. We Can Fix Them: Pipe Syntax In SQL 36:55

for 17 weeks siden36:55

36:55

Google SQL の新しい文法を森田が紹介しました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 SQL Has Problems. We Can Fix Them: Pipe Syntax In SQL PRQL Pipe syntax | BigQuery | Google Cloud SQLite Forum: Interesting paper from Google on pipe syntax on SQL

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#140: GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models 39:54

for 20 weeks siden39:54

39:54

LLM にひっかけ算数問題を出してみる話を向井が読みました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 [2410.05229] GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models GitHub – openai/grade-school-math

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#139: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis 38:00

for 23 weeks siden38:00

38:00

写真を集めてシーンをレンダリングするニューラルネットを森田が読みました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis NeRF Tutorial ECCV 2022 illuminate.google.com

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#138: Distilling the Knowledge in a Neural Network 23:38

for 26 weeks siden23:38

23:38

大きなモデルから小さなモデルを作るテクニックを向井が回願しました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 [1503.02531] Distilling the Knowledge in a Neural Network

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#137: Optimal Quantile Approximation in Streams 27:32

for 30 weeks siden27:32

27:32

ストリームにパーセンタイルを計算したい森田が教科書を読みました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 [1603.05346v2] Optimal Quantile Approximation in Streams Small Summaries for Big Data Data Types – Presto 0.288 Documentation Estimating Percentile Values | Snowflake Documentation KLL sketch vs t-digest – DataSketches…

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#136: Distinct Elements in Streams: An Algorithm for the (Text) Book 25:58

for 31 weeks siden25:58

25:58

学部生にも実装できるストリームの要素カウントアルゴリズムを向井が試しました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 [2301.10191] Distinct Elements in Streams: An Algorithm for the (Text) Book The CVM Algorithm for Estimating Distinct Elements in Streams Computer scientists invent an efficient new way to count | Hacker News…

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#135: In-Datacenter Performance Analysis of a Tensor Processing Unit 44:18

for 36 weeks siden44:18

44:18

行列の掛け算が得意なハードウェアについて森田が読みました。ご意見感想などは Reddit やおたより投書箱にお寄せください。iTunes のレビューや星もよろしくね。 [1704.04760] In-Datacenter Performance Analysis of a Tensor Processing Unit Systolic Arrays – an overview | ScienceDirect Topics Pallas: a JAX kernel language — JAX documentation About Groq – Fast AI Inference The Design Process for Google’s Training Chips: TPUv2 and TPUv3 | IEEE Journals & Magazine | IEEE Xplore…

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#134: LoRA: Low-Rank Adaptation of Large Language Models 28:40

for 39 weeks siden28:40

28:40

巨大 ML モデルの軽量 fine-tuning 手法を向井が読みました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 [2106.09685] LoRA: Low-Rank Adaptation of Large Language Models

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#133: Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations 30:19

for 42 weeks siden30:19

30:19

CUDA を書かずに済む GPU カーネルの DSL について森田が読みました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations Introducing Triton: Open-source GPU programming for neural networks | OpenAI Welcome to Triton’s documentation! — Triton documentation Hello Triton.ipynb – Colab #01: Tensor Comprehensions, Rust Belt – Misreading Chat #23 – Halide: Decoupling Algorithms from Schedules for High-Performance Image Processing – Misreading Chat #27 – Julia: A Fresh Approach to Numerical Computing – Misreading Chat…

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#132: High-Resolution Image Synthesis with Latent Diffusion Models 34:30

for 45 weeks siden34:30

34:30

Stable Diffusion の元論文を向井が読みました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 [2112.10752] High-Resolution Image Synthesis with Latent Diffusion Models [2105.05233] Diffusion Models Beat GANs on Image Synthesis Classifier-Free Diffusion Guidance | OpenReview What are Diffusion Models? | Lil’Log…

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#131: FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness 30:40

for 46 weeks siden30:40

30:40

CUDA で書かれた PyTorch 用カーネルに森田が玉砕しました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 [2205.14135] FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness GitHub – Dao-AILab/flash-attention: Fast and memory-efficient exact attention GitHub – NVIDIA/apex: A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch [2307.08691] FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning [2112.05682] Self-attention Does Not Need $O(n^2)$ Memory GitHub – tspeterkim/flash-attention-minimal: Flash Attention in ~100 lines of CUDA (forward pass only)…

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#130: Diffusion models from scratch, from a new theoretical perspective 30:44

for 49 weeks siden30:44

30:44

向井が画像生成の拡散モデルに入門しました。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 Diffusion models from scratch [1503.03585] Deep Unsupervised Learning using Nonequilibrium Thermodynamics [2006.11239] Denoising Diffusion Probabilistic Models

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#129: Programming Massively Parallel Processors (Ch.4- Ch.6) 59:43

for 50 weeks siden59:43

59:43

森田が飽きずに CUDA の教科書を読んでます。ご意見感想などは Reddit やおたより投書箱にお寄せください。 iTunes のレビューや星もよろしくね。 Programming Massively Parallel Processors: A Hands-on Approach ( Amazon.co.jp , Elsevier ) NVIDIA H100 Tensor Core GPU Architecture Overview

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