Technology
Data-Efficient Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang (Shane) Gu, Honglak Lee, Sergey Levine
Neural Information Processing Systems http://nips.cc/
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning
Igor Colin, Ludovic DOS SANTOS, Kevin Scaman
Neural Information Processing Systems http://nips.cc/
f29e2360ef277f77595dfae0aab78138-AuthorFeedback.pdf
However, this does not mean that it could not be useful for ML, as: 1) many practical aspects of PPRS can yet be27 improved.
When Can We Track Significant Preference Shifts in Dueling Bandits?
We show that the answer to this question depends on the properties of underlying preference distributions.
Neural Attribution for Semantic Bug-Localization in Student Programs
Rahul Gupta, Aditya Kanade, Shirish Shevade
Most open online courses on programming makeuse ofautomated grading systems tosupport programming assignments and give real-time feedback. These systems usually rely on test results toquantify theprograms' functional correctness.
Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding
Hajin Shim, Sung Ju Hwang, Eunho Yang
Neural Information Processing Systems http://nips.cc/
MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems Bin Lei
Recent advancements in large language models, such as GPT -4, have demonstrated remarkable capabilities in processing standard queries.