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Collections including paper arxiv:2412.11768

Papers - Math - SGD - Stochastic Gradient Descent

Unraveling the Gradient Descent Dynamics of Transformers

Paper • 2411.07538 • Published Nov 12 • 2
No More Adam: Learning Rate Scaling at Initialization is All You Need

Paper • 2412.11768 • Published 10 days ago • 41

Papers - Training - Convergence - Stoch Gradient Descent

Unraveling the Gradient Descent Dynamics of Transformers

Paper • 2411.07538 • Published Nov 12 • 2
No More Adam: Learning Rate Scaling at Initialization is All You Need

Paper • 2412.11768 • Published 10 days ago • 41

Interesting Papers

HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems

Paper • 2411.02959 • Published Nov 5 • 64
GarVerseLOD: High-Fidelity 3D Garment Reconstruction from a Single In-the-Wild Image using a Dataset with Levels of Details

Paper • 2411.03047 • Published Nov 5 • 8
MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D

Paper • 2411.02336 • Published Nov 4 • 23
GenXD: Generating Any 3D and 4D Scenes

Paper • 2411.02319 • Published Nov 4 • 20

Rethinking Data Selection at Scale: Random Selection is Almost All You Need

Paper • 2410.09335 • Published Oct 12 • 16
From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning

Paper • 2410.06456 • Published Oct 9 • 35
Emergent properties with repeated examples

Paper • 2410.07041 • Published Oct 9 • 8
Personalized Visual Instruction Tuning

Paper • 2410.07113 • Published Oct 9 • 69

RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval

Paper • 2409.10516 • Published Sep 16 • 39
Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse

Paper • 2409.11242 • Published Sep 17 • 5
Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models

Paper • 2409.11136 • Published Sep 17 • 21
On the Diagram of Thought

Paper • 2409.10038 • Published Sep 16 • 12

Machine learning

Adam-mini: Use Fewer Learning Rates To Gain More

Paper • 2406.16793 • Published Jun 24 • 67
No More Adam: Learning Rate Scaling at Initialization is All You Need

Paper • 2412.11768 • Published 10 days ago • 41

Optimizer_Papers

about 17 hours ago

Adam-mini: Use Fewer Learning Rates To Gain More

Paper • 2406.16793 • Published Jun 24 • 67
No More Adam: Learning Rate Scaling at Initialization is All You Need

Paper • 2412.11768 • Published 10 days ago • 41

about 3 hours ago

CatLIP: CLIP-level Visual Recognition Accuracy with 2.7x Faster Pre-training on Web-scale Image-Text Data

Paper • 2404.15653 • Published Apr 24 • 26
MoDE: CLIP Data Experts via Clustering

Paper • 2404.16030 • Published Apr 24 • 12
MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

Paper • 2405.12130 • Published May 20 • 46
Reducing Transformer Key-Value Cache Size with Cross-Layer Attention

Paper • 2405.12981 • Published May 21 • 28

Papers - Fine-tuning - PEFT

OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework

Paper • 2404.14619 • Published Apr 22 • 126
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning

Paper • 2303.15647 • Published Mar 28, 2023 • 4
Hyper-X: A Unified Hypernetwork for Multi-Task Multilingual Transfer

Paper • 2205.12148 • Published May 24, 2022 • 2
No More Adam: Learning Rate Scaling at Initialization is All You Need

Paper • 2412.11768 • Published 10 days ago • 41

Papers - Image - Datasets - ImageNet

All you need is a good init

Paper • 1511.06422 • Published Nov 19, 2015 • 1
Align Your Steps: Optimizing Sampling Schedules in Diffusion Models

Paper • 2404.14507 • Published Apr 22 • 21
Efficient Transformer Encoders for Mask2Former-style models

Paper • 2404.15244 • Published Apr 23 • 1
Deep Residual Learning for Image Recognition

Paper • 1512.03385 • Published Dec 10, 2015 • 6

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