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Collections including paper arxiv:2404.07129

Papers - Emergent Properties - ICL - Induction Heads

Additional reading: https://transformer-circuits.pub/2021/framework/index.html

In-context Learning and Induction Heads

Paper • 2209.11895 • Published Sep 24, 2022 • 2
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

Paper • 2404.07129 • Published Apr 10 • 3
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions

Paper • 2403.07809 • Published Mar 12 • 1

Papers - Training - Datasets - Few-Shot Learning - OmniGlot

What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

Paper • 2404.07129 • Published Apr 10 • 3

Papers - ICL - Training - Activations - Clamping

See: pattern-preserving ablation

What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

Paper • 2404.07129 • Published Apr 10 • 3

Papers - ICL - Induction Circuit

Two layer induction heads

What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

Paper • 2404.07129 • Published Apr 10 • 3
In-context Learning and Induction Heads

Paper • 2209.11895 • Published Sep 24, 2022 • 2
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions

Paper • 2403.07809 • Published Mar 12 • 1

Papers - ICL - Induction Head

What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

Paper • 2404.07129 • Published Apr 10 • 3
In-context Learning and Induction Heads

Paper • 2209.11895 • Published Sep 24, 2022 • 2
pyvene: A Library for Understanding and Improving PyTorch Models via Interventions

Paper • 2403.07809 • Published Mar 12 • 1

Papers - University - University College London

What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

Paper • 2404.07129 • Published Apr 10 • 3

Explainable Lung Disease Classification from Chest X-Ray Images Utilizing Deep Learning and XAI

Paper • 2404.11428 • Published Apr 17 • 1
A Multimodal Automated Interpretability Agent

Paper • 2404.14394 • Published Apr 22 • 20
What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formation

Paper • 2404.07129 • Published Apr 10 • 3
The Geometry of Categorical and Hierarchical Concepts in Large Language Models

Paper • 2406.01506 • Published Jun 3 • 3

Papers - Google

Lumiere: A Space-Time Diffusion Model for Video Generation

Paper • 2401.12945 • Published Jan 23 • 86
Long-form factuality in large language models

Paper • 2403.18802 • Published Mar 27 • 23
ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion

Paper • 2403.18818 • Published Mar 27 • 24
TC4D: Trajectory-Conditioned Text-to-4D Generation

Paper • 2403.17920 • Published Mar 26 • 15

Papers - ICL - In-Context Learning

Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Paper • 2311.00871 • Published Nov 1, 2023 • 2
Can large language models explore in-context?

Paper • 2403.15371 • Published Mar 22 • 31
Data Distributional Properties Drive Emergent In-Context Learning in Transformers

Paper • 2205.05055 • Published Apr 22, 2022 • 2
Long-context LLMs Struggle with Long In-context Learning

Paper • 2404.02060 • Published Apr 2 • 34

Papers - ResNet

Wide Residual Networks

Paper • 1605.07146 • Published May 23, 2016 • 2
Characterizing signal propagation to close the performance gap in unnormalized ResNets

Paper • 2101.08692 • Published Jan 21, 2021 • 2
Pareto-Optimal Quantized ResNet Is Mostly 4-bit

Paper • 2105.03536 • Published May 7, 2021 • 2
When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations

Paper • 2106.01548 • Published Jun 3, 2021 • 2

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