IEEE

Interpreting Tangled Program Graphs Under Partially Observable Dota 2 Invoker Tasks

Abstract: Interpretable learning agents directly construct models that provide insight into the relationships learnt. Moreover, to date, there has been a lot of emphasis on interpreting reactive ...
搜狐

Understanding the 'Heartbeat' of Large Models: Unveiling the Loss Function in AI Training and Future Trends

With the rapid development of artificial intelligence technology, we are increasingly focused on the capability boundaries and training mechanisms of large models. Understanding how models 'learn' has ...
BBC

Module 1 (M1) - Handling data - Presenting and interpreting data - Line graphs and scattergraphs

Data can be presented in many ways that make it quicker and easier to read. In this section we will look at some of these ways. It is important to choose the best way to present data. A line graph can ...
C&EN

Deconvoluting and Interpreting Nontargeted Chemical Data: A Data-Driven Forensic Workflow for Identifying the Most Prominent Chemical Sources in Receiving Waters

Oregon State University, Department of Biological & Ecological Engineering, Corvallis, Oregon 97331-4501, United States ...
IEEE

Integrating Clinical Knowledge Graphs and Gradient-Based Neural Systems for Enhanced Melanoma Diagnosis via the Seven-Point Checklist

Abstract: The seven-point checklist (7PCL) is a widely used diagnostic tool in dermoscopy for identifying malignant melanoma by assigning point values to seven specific attributes. However, the ...
GitHub

DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform

DeepGFT requires python version >= 3.7. We tested it in python 3.8.16 and cuda 11.6.1 on Linux. pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio ...