Collection by 
Feb 21, 2024
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Feb 20, 2024
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Feb 20, 2024
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Jan 6
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Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
Paper • 2405.19076 • Published • 2 -
X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Design
Paper • 2402.07148 • Published • 6 -
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning
Paper • 2402.04268 • Published -
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge
Paper • 2311.08166 • Published • 2
Collection by 
Feb 20, 2024
Collection by 
Feb 20, 2024
Collection by 
Mar 7, 2024
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Graph Mamba: Towards Learning on Graphs with State Space Models
Paper • 2402.08678 • Published • 17 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 32 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 59 -
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Paper • 2401.09417 • Published • 62
Collection by 
Feb 21, 2024
Collection by 
Feb 20, 2024
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The Unreasonable Effectiveness of Eccentric Automatic Prompts
Paper • 2402.10949 • Published • 5 -
State of What Art? A Call for Multi-Prompt LLM Evaluation
Paper • 2401.00595 • Published • 3 -
Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
Paper • 2312.16171 • Published • 37 -
The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models
Paper • 2401.05618 • Published • 1
Collection by 
Feb 20, 2024
Collection by Feb 21, 2024
Collection by Feb 20, 2024
Collection by Feb 20, 2024
Collection by Mar 7, 2024
-
Graph Mamba: Towards Learning on Graphs with State Space Models
Paper • 2402.08678 • Published • 17 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 32 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 59 -
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Paper • 2401.09417 • Published • 62
Collection by Feb 20, 2024
Collection by Feb 21, 2024
Collection by Jan 6
-
Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design
Paper • 2405.19076 • Published • 2 -
X-LoRA: Mixture of Low-Rank Adapter Experts, a Flexible Framework for Large Language Models with Applications in Protein Mechanics and Design
Paper • 2402.07148 • Published • 6 -
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning
Paper • 2402.04268 • Published -
MechAgents: Large language model multi-agent collaborations can solve mechanics problems, generate new data, and integrate knowledge
Paper • 2311.08166 • Published • 2
Collection by Feb 20, 2024
-
The Unreasonable Effectiveness of Eccentric Automatic Prompts
Paper • 2402.10949 • Published • 5 -
State of What Art? A Call for Multi-Prompt LLM Evaluation
Paper • 2401.00595 • Published • 3 -
Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4
Paper • 2312.16171 • Published • 37 -
The Benefits of a Concise Chain of Thought on Problem-Solving in Large Language Models
Paper • 2401.05618 • Published • 1
Collection by Feb 20, 2024
Collection by Feb 20, 2024