Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation
원문 초록
Artificial neural networks (ANNs) have been broadly utilized to analyze various data and solve different domain problems. However, neural networks (NNs) have been considered a black box operation for years because their underlying computation and meaning are hidden. Due to this nature, users often face difficulties in interpreting the underlying mechanism of the NNs and the benefits of using them. In this paper, to improve users' learning and understanding of NNs, an interactive learning system is designed to create digit patterns and recognize them in real time. To help users clearly understand the visual differences of digit patterns (i.e., 0 ~ 9) and their results with an NN, integrating visualization is considered to present all digit patterns in a two-dimensional display space with supporting multiple user interactions. An evaluation with multiple datasets is conducted to determine its usability for active learning. In addition, informal user testing is managed during a summer workshop by asking the workshop participants to use the system.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Near-Polynomially Competitive Active Logistic Regression
CriticLean: Critic-Guided Reinforcement Learning for Mathematical Formalization
Active Learning for Manifold Gaussian Process Regression
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Info-Coevolution: An Efficient Framework for Data Model Coevolution
ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
토론
이 논문과 연결된 포럼 글이 아직 없습니다.