Human Learning

Human learning research seeks to understand how humans acquire knowledge and skills, focusing on how different learning theories can be reconciled and integrated into computational models. Current research emphasizes the development of agent-based models, incorporating elements of variation theory, rate-distortion theory, and curriculum learning to optimize active learning and improve the efficiency of AI-based tutoring systems. This work has implications for designing more effective educational tools and human-AI collaborative systems, improving learning outcomes and fostering more natural human-computer interactions.

Papers

March 26, 2023

Lazy learning: a biologically-inspired plasticity rule for fast and energy efficient synaptic plasticity
Aaron Pache, Mark CW van Rossum
Back Propagation Energy Policy Research Synaptic Plasticity Human Learning MNIST Canadian Institute for Advanced Lazy Learning

March 7, 2023

ERUDITE: Human-in-the-Loop IoT for an Adaptive Personalized Learning System
Mojtaba Taherisadr, Mohammad Abdullah Al Faruque, Salma Elmalaki
Human in the Loop Adaptive Learning Brain Signal Human Learning Wearable Brain Computer Interface

January 3, 2023

A Theory of Human-Like Few-Shot Learning
Zhiying Jiang, Rui Wang, Dongbo Bu, Ming Li
LeArning Abstract Shot Learning Theoretical Understanding Human Like Deep Generative Model Human Learning

January 2, 2023

Towards Modeling and Influencing the Dynamics of Human Learning
Ran Tian, Masayoshi Tomizuka, Anca Dragan, Andrea Bajcsy
Human Robot Interaction Learning Dynamic Human Learning Internal Model

February 5, 2022

Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning
Daniel T. Chang
Shot Learning Class Incremental Learning Contrastive Self Supervised Learning Human Learning Shot Class

November 23, 2021

Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review
Richa Upadhyay, Ronald Phlypo, Rajkumar Saini, Marcus Liwicki
Transfer Learning Multi Task Learning Meta Learning Human Learning Meta Review

November 21, 2021

Accretionary Learning with Deep Neural Networks
Xinyu Wei, Biing-Hwang Fred Juang, Ouya Wang, Shenglong Zhou, Geoffrey Ye Li
Deep Neural Network DNN Architecture Human Learning Generative Dynamic

November 14, 2021

Free Will Belief as a consequence of Model-based Reinforcement Learning
Erik M. Rehn
Reinforcement Learning LeArning Abstract Model Based Reinforcement Learning Human Decision Artificial Intelligence Agent Human Learning Experiential Consequence