https://github.com/snap-stanford/biodiscoveryagent
공식 코드BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments
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A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments
GreaseLM: Graph REASoning Enhanced Language Models for Question Answering
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Arctic Long Sequence Training: Scalable And Efficient Training For Multi-Million Token Sequences
SuffixDecoding: Extreme Speculative Decoding for Emerging AI Applications
A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals
GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance
Correcting misinformation on social media with a large language model
RepairAgent: An Autonomous, LLM-Based Agent for Program Repair
You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projects
Event Prediction using Case-Based Reasoning over Knowledge Graphs
Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Time-aware Graph Neural Networks for Entity Alignment between Temporal Knowledge Graphs
Decoding Concerns: Multi-label Classification of Vaccine Sentiments in Social Media
Sparse is Enough in Fine-tuning Pre-trained Large Language Models
Reasoning of Large Language Models over Knowledge Graphs with Super-Relations
SOTOPIA-$π$: Interactive Learning of Socially Intelligent Language Agents
Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs
Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approach
Leveraging Multimodal LLM for Inspirational User Interface Search
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.