Asia
A Meta-Analysis of Overfitting in Machine Learning
Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, Ludwig Schmidt
In each competition, numerous practitioners repeatedly evaluated their progress against a holdout set that forms the basis of a public ranking availablethroughout the competition. Performance on a separate test set used only oncedetermined the final ranking.
Multi-Step Generalized Policy Improvement by Leveraging Approximate Models Lucas N. Alegre 1, 2 Ana L. C. Bazzan 1 Ann Now รฉ 2 Bruno C. da Silva 3 1
We introduce a principled method for performing zero-shot transfer in reinforcement learning (RL) by exploiting approximate models of the environment. Zero-shot transfer in RL has been investigated by leveraging methods rooted in generalized policy improvement (GPI) and successor features (SFs).
A^2-Nets: Double Attention Networks
Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng
Neural Information Processing Systems http://nips.cc/
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence. While there are numerous AI models available for various domains and modalities, they cannot handle complicated AI tasks autonomously. Considering large language models (LLMs) have exhibited exceptional abilities in language understanding, generation, interaction, and reasoning, we advocate that LLMs could act as a controller to manage existing AI models to solve complicated AI tasks, with language serving as a generic interface to empower this. Based on this philosophy, we present HuggingGPT, an LLM-powered agent that leverages LLMs (e.g., ChatGPT) to connect various AI models in machine learning communities (e.g., Hugging Face) to solve AI tasks. Specifically, we use ChatGPT to conduct task planning when receiving a user request, select models according to their function descriptions available in Hugging Face, execute each subtask with the selected AI model, and summarize the response according to the execution results. By leveraging the strong language capability of ChatGPT and abundant AI models in Hugging Face, HuggingGPT can tackle a wide range of sophisticated AI tasks spanning different modalities and domains and achieve impressive results in language, vision, speech, and other challenging tasks, which paves a new way towards the realization of artificial general intelligence.
Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement
Chao Yang, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu, Junzhou Huang, Chuang Gan
Incontrast toLearning fromDemonstration (LfD) that involves both action and state supervision, LfO is more practical in leveraging previously inapplicable resources (e.g.
Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem
Neural Information Processing Systems http://nips.cc/
Statistical Model Aggregation via Parameter Matching
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Nghia Hoang
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets.
A Little Is Enough: Circumventing Defenses For Distributed Learning
Gilad Baruch, Moran Baruch, Yoav Goldberg
Neural Information Processing Systems http://nips.cc/
Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoning
We give a model of how to infer natural language rules by doing experiments.