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Collections including paper arxiv:2405.21060

Papers I want to read

Papers in my to-read list

RLHF Workflow: From Reward Modeling to Online RLHF

Paper • 2405.07863 • Published May 13, 2024 • 67
Chameleon: Mixed-Modal Early-Fusion Foundation Models

Paper • 2405.09818 • Published May 16, 2024 • 130
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models

Paper • 2405.15574 • Published May 24, 2024 • 53
An Introduction to Vision-Language Modeling

Paper • 2405.17247 • Published May 27, 2024 • 87

Mamba based models

Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Paper • 2401.09417 • Published Jan 17, 2024 • 60
VMamba: Visual State Space Model

Paper • 2401.10166 • Published Jan 18, 2024 • 39
DiM: Diffusion Mamba for Efficient High-Resolution Image Synthesis

Paper • 2405.14224 • Published May 23, 2024 • 14
Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Paper • 2312.00752 • Published Dec 1, 2023 • 140

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Paper • 2405.21060 • Published May 31, 2024 • 64
Your Transformer is Secretly Linear

Paper • 2405.12250 • Published May 19, 2024 • 151
KAN: Kolmogorov-Arnold Networks

Paper • 2404.19756 • Published Apr 30, 2024 • 109
Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography

Paper • 2501.08970 • Published 17 days ago • 6

Associative Recurrent Memory Transformer

Paper • 2407.04841 • Published Jul 5, 2024 • 32
Mixture-of-Agents Enhances Large Language Model Capabilities

Paper • 2406.04692 • Published Jun 7, 2024 • 56
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Paper • 2405.21060 • Published May 31, 2024 • 64
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Paper • 2404.14219 • Published Apr 22, 2024 • 256

DS' Daily paper

Instruction Pre-Training: Language Models are Supervised Multitask Learners

Paper • 2406.14491 • Published Jun 20, 2024 • 87
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Paper • 2405.21060 • Published May 31, 2024 • 64
Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models

Paper • 2405.20541 • Published May 30, 2024 • 22
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Paper • 2406.01574 • Published Jun 3, 2024 • 45

Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Paper • 2406.06525 • Published Jun 10, 2024 • 67
Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning

Paper • 2406.06469 • Published Jun 10, 2024 • 25
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Paper • 2406.04271 • Published Jun 6, 2024 • 29
Block Transformer: Global-to-Local Language Modeling for Fast Inference

Paper • 2406.02657 • Published Jun 4, 2024 • 38

SOTA architecture

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Paper • 2405.21060 • Published May 31, 2024 • 64

Papers - Mamba - Mamba 2

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Paper • 2405.21060 • Published May 31, 2024 • 64

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Paper • 2405.21060 • Published May 31, 2024 • 64
Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models

Paper • 2406.04320 • Published Jun 6, 2024 • 7
MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection

Paper • 2403.19888 • Published Mar 29, 2024 • 12
Graph Mamba: Towards Learning on Graphs with State Space Models

Paper • 2402.08678 • Published Feb 13, 2024 • 15

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Paper • 2405.21060 • Published May 31, 2024 • 64

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