Experimental Approach

Experimental approaches are being revolutionized by the integration of machine learning, aiming to optimize experimental design, accelerate data analysis, and improve the efficiency of scientific discovery. Current research focuses on applying machine learning models, such as graph transformer networks and various deep learning architectures, to predict experimental outcomes, analyze complex datasets (including text and images), and even automate experimental workflows in real-time. This integration promises to significantly enhance the speed, accuracy, and cost-effectiveness of scientific research across diverse fields, from materials science and biology to drug discovery and high-throughput experimentation at large-scale facilities.

Papers

August 10, 2024

Modeling Multi-Step Scientific Processes with Graph Transformer Networks
Amanda A. Volk, Robert W. Epps, Jeffrey G. Ethier, Luke A. Baldwin
Graph Transformer Geometric Learning Reaction Path Experimental Approach

July 10, 2024

Analyzing Large language models chatbots: An experimental approach using a probability test
Melise Peruchini, Julio Monteiro Teixeira
Large Language Model Chatbot Response Probabilistic Logic Experimental Approach

April 21, 2024

Automated Text Mining of Experimental Methodologies from Biomedical Literature
Ziqing Guo
BERT Model Text Mining BioMedical Literature Biomedical Text Biomedical Text Mining Pre Trained Generative Language Model Experimental Approach Traditional Text Classification

April 2, 2024

Evaluating Large Language Models Using Contrast Sets: An Experimental Approach
Manish Sanwal
Language Understanding Natural Language Inference Contrast Set Experimental Approach

August 10, 2023

Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluation
Hao Liang, Pietro Perona, Guha Balakrishnan
Face Recognition Human Evaluation Synthetic Face Algorithmic Bias Perceptual Similarity Face Generator Experimental Approach

May 16, 2023

RAMario: Experimental Approach to Reptile Algorithm -- Reinforcement Learning for Mario
Sanyam Jain
Reinforcement Learning Game AI Experimental Approach Reptile Algorithm

November 1, 2022

Machine learning can guide experimental approaches for protein digestibility estimations
Sara Malvar, Anvita Bhagavathula, Maria Angels de Luis Balaguer, Swati Sharma, Ranveer Chandra
Machine Learning Experimental Approach Cultured Meat

January 9, 2022

Machine learning enabling high-throughput and remote operations at large-scale user facilities
Tatiana Konstantinova, Phillip M. Maffettone, Bruce Ravel, Stuart I. Campbell, Andi M. Barbour, Daniel Olds
Machine Learning New Machine High Throughput Remote Human Operator Experimental Approach

Experimental Approach

Papers

Modeling Multi-Step Scientific Processes with Graph Transformer Networks

Analyzing Large language models chatbots: An experimental approach using a probability test

Automated Text Mining of Experimental Methodologies from Biomedical Literature

Evaluating Large Language Models Using Contrast Sets: An Experimental Approach

Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluation

RAMario: Experimental Approach to Reptile Algorithm -- Reinforcement Learning for Mario

Machine learning can guide experimental approaches for protein digestibility estimations

Machine learning enabling high-throughput and remote operations at large-scale user facilities