Lifelong Learning System

Lifelong learning systems aim to create artificial intelligence capable of continuously learning and adapting to new tasks and data without catastrophic forgetting of previously acquired knowledge. Current research emphasizes developing robust algorithms and architectures, including those based on regularization, memory-based approaches, and specialized neural network designs, to address challenges like scalability and efficient knowledge transfer across diverse domains. This field is crucial for building more adaptable and robust AI systems, impacting various applications from personalized e-commerce to real-time strategy game AI and beyond.

Papers

October 21, 2024

Solving Continual Offline RL through Selective Weights Activation on Aligned Spaces
Jifeng Hu, Sili Huang, Li Shen, Zhejian Yang, Shengchao Hu, Shisong Tang, Hechang Chen, Yi Chang, Dacheng Tao, Lichao Sun
Continual Learning Continual Training Space Binding Continual Offline Reinforcement Learning Lifelong Learning System

August 26, 2024

Towards Lifelong Learning Embeddings: An Algorithmic Approach to Dynamically Extend Embeddings
Miguel Alves Gomes, Philipp Meisen, Tobias Meisen
Deep Learning Model Latent Space Jina Embeddings Word Embeddings Algorithmic Task Dynamic Embeddings Lifelong Learning System

November 22, 2023

Evaluating Pretrained models for Deployable Lifelong Learning
Kiran Lekkala, Eshan Bhargava, Yunhao Ge, Laurent Itti
Full Model Continual LEArning Lifelong Learning Visual Reinforcement Learning Lifelong Learning System

January 18, 2023

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Ese Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha
Robust Learning New Characterization Continuous Learning Domain Agnostic Lifelong Learning System

December 8, 2022

System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
Indranil Sur, Zachary Daniels, Abrar Rahman, Kamil Faber, Gianmarco J. Gallardo, Tyler L. Hayes, Cameron E. Taylor, Mustafa Burak Gurbuz, James Smith, Sahana Joshi, Nathalie Japkowicz, Michael Baron, Zsolt Kira, Christopher Kanan, Roberto Corizzo, Ajay Divakaran, Michael Piacentino, Jesse Hostetler, Aswin Raghavan
Continual Learning Modular Framework Lifelong Reinforcement Learning Real Time Strategy Lifelong Machine Learning Lifelong Learning System

July 28, 2022

Latent Properties of Lifelong Learning Systems
Corban Rivera, Chace Ashcraft, Alexander New, James Schmidt, Gautam Vallabha
Latent Feature Lifelong Reinforcement Learning Interpretable Surrogate Model Lifelong Learning System

July 10, 2022

An Introduction to Lifelong Supervised Learning
Shagun Sodhani, Mojtaba Faramarzi, Sanket Vaibhav Mehta, Pranshu Malviya, Mohamed Abdelsalam, Janarthanan Janarthanan, Sarath Chandar
Lifelong Learning Gentle Introduction Learning Paradigm Lifelong Learning System