Knowledge Circuits In Pretrained Transformers

Media Summary: In this session, we welcome Yunzhi Yao from Zhejiang University China , who co-authored the paper " A Walkthrough of A Mathematical Framework for Paper presented by Gail Weiss to the Neural Sequence Model Theory discord on the 24th of February 2022. Gail's references: On ...

Knowledge Circuits In Pretrained Transformers - Detailed Analysis & Overview

In this session, we welcome Yunzhi Yao from Zhejiang University China , who co-authored the paper " A Walkthrough of A Mathematical Framework for Paper presented by Gail Weiss to the Neural Sequence Model Theory discord on the 24th of February 2022. Gail's references: On ... See part 2 here: Implementing GPT-2 from Scratch I made this video to illustrate the difference between how a Breaking down how Large Language Models work, visualizing how data flows through. Instead of sponsored ad reads, these ...

Demystifying attention, the key mechanism inside Dale's Blog → Classify text with BERT → Over the past five years,

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Knowledge Circuits in Pretrained Transformers Explained

What are Transformers (Machine Learning Model)?

A Walkthrough of A Mathematical Framework for Transformer Circuits

Gail Weiss: Thinking Like Transformers

What is a Transformer? (Transformer Walkthrough Part 1/2)

How a Transformer works at inference vs training time

Transformers, the tech behind LLMs | Deep Learning Chapter 5

Attention in transformers, step-by-step | Deep Learning Chapter 6

PostLN, PreLN and ResiDual Transformers

Transformers Explained | Simple Explanation of Transformers

Transformers, explained: Understand the model behind GPT, BERT, and T5

[CVPR2026 Highlight] Circuit Mechanisms for Relational Generation in Diffusion Transformers

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Knowledge Circuits in Pretrained Transformers Explained

Knowledge Circuits in Pretrained Transformers Explained

In this session, we welcome Yunzhi Yao from Zhejiang University China , who co-authored the paper "

What are Transformers (Machine Learning Model)?

What are Transformers (Machine Learning Model)?

Learn more about

A Walkthrough of A Mathematical Framework for Transformer Circuits

A Walkthrough of A Mathematical Framework for Transformer Circuits

A Walkthrough of A Mathematical Framework for

Gail Weiss: Thinking Like Transformers

Gail Weiss: Thinking Like Transformers

Paper presented by Gail Weiss to the Neural Sequence Model Theory discord on the 24th of February 2022. Gail's references: On ...

What is a Transformer? (Transformer Walkthrough Part 1/2)

What is a Transformer? (Transformer Walkthrough Part 1/2)

See part 2 here: Implementing GPT-2 from Scratch https://neelnanda.io/

How a Transformer works at inference vs training time

How a Transformer works at inference vs training time

I made this video to illustrate the difference between how a

Transformers, the tech behind LLMs | Deep Learning Chapter 5

Transformers, the tech behind LLMs | Deep Learning Chapter 5

Breaking down how Large Language Models work, visualizing how data flows through. Instead of sponsored ad reads, these ...

Attention in transformers, step-by-step | Deep Learning Chapter 6

Attention in transformers, step-by-step | Deep Learning Chapter 6

Demystifying attention, the key mechanism inside

PostLN, PreLN and ResiDual Transformers

PostLN, PreLN and ResiDual Transformers

PostLN

Transformers Explained | Simple Explanation of Transformers

Transformers Explained | Simple Explanation of Transformers

Transformers

Transformers, explained: Understand the model behind GPT, BERT, and T5

Transformers, explained: Understand the model behind GPT, BERT, and T5

Dale's Blog → https://goo.gle/3xOeWoK Classify text with BERT → https://goo.gle/3AUB431 Over the past five years,

[CVPR2026 Highlight] Circuit Mechanisms for Relational Generation in Diffusion Transformers

[CVPR2026 Highlight] Circuit Mechanisms for Relational Generation in Diffusion Transformers

Project Page: https://animadversio.github.io/DiT-Relation-

Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

This paper investigates how effectively