A SYSTEM FOR INCREMENTAL LEARNING BASED ON ALGORITHMIC PROBABILITY
By Ray J. Solomonofi. 1989
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Yoshua Bengio
- Caiming Xiong
- Kyunghyun Cho
- Mohit Bansal
- Sergey Levine
- Richard Socher
- Ruslan Salakhutdinov
- Zhe Gan
Citations in the corpus
- ConTinTin: Continual Learning from Task Instructions (2022, background): “2Continual learning in the literature is also referred to as: lifelong learning (Silver and Mercer, 2002), incremental learning (Solomonoff, 1989), sequential learning (McCloskey ing problem was mainly studied in computer vision or robotics domains, and most work concentrated on mitigating…”
- Continual Lifelong Learning in Natural Language Processing: A Survey (2020, background): “, 2017), incremental learning (Solomonoff, 1989; Chaudhry et al., 2018), sequential learning (McCloskey and Cohen, 1989; Shin et al.”
- Knowledge Matters: Importance of Prior Information for Optimization (2013, background): “he “Guided Learning Hypothesis” humans get hints from other humans and learn high-level concepts by the guidance of other humans2. Curriculum learning (Bengio et al., 2009b) and incremental learning (Solomonoff, 1989), are examples of this. This is done by properly choosing the sequence of examples ”
- Optimal Ordered Problem Solver (2002, neutral): “Compare the concept of conceptual jump size ( Solomonoff, 1986, 1989 ).”
- Environments for Lifelong Reinforcement Learning (2018, methodology): “[Ring,1997]describesacontinuallearningagentasanautonomousagenttrainedonasequence of tasks with no final task. This process bears different names in the literature - incremental and continual learning [Solomonoff, 1989], never-endinglearning [Carlson et al., 2010], etc. In this work, we focus on lifel”
- Machine Lifelong Learning: Challenges and Benefits for Artificial General Intelligence (2011, background): “The study of lifelong learning systems will provided insight into curriculum and training sequences that are beneficial for both humans and machines [11,4].”
- Lifelong Machine Learning Systems: Beyond Learning Algorithms (2013, background): “Theories on how to select inductive bias and modify the representational space of hypotheses (Solomonoff 1989) will be of significant value to AI and brain science.”