Multipath agents for modular multitask ML systems
A standard ML model is commonly generated by a single method that specifies aspects such as architecture, initialization, training data and hyperparameters configuration. The presented work introduces a novel methodology allowing to define multiple methods as distinct agents. Agents can collaborate and compete to generate and improve ML models for a given tasks. The proposed methodology is demonstrated with the generation and extension of a dynamic modular multitask ML system solving more than one hundred image classification tasks. Diverse agents can compete to produce the best performing model for a task by reusing the modules introduced to the system by competing agents. The presented work focuses on the study of agents capable of: 1) reusing the modules generated by concurrent agents, 2) activating in parallel multiple modules in a frozen state by connecting them with trainable modules, 3) condition the activation mixture on each data sample by using a trainable router module. We demonstrate that this simple per-sample parallel routing method can boost the quality of the combined solutions by training a fraction of the activated parameters.
Code (0)
등록된 구현이 없습니다.
Tasks
image-classificationImage ClassificationSimilar Papers 제목 키워드 기반
Scalable Multi-Agent Lab Framework for Lab Optimization
Autonomous materials research systems allow scientists to fail smarter, learn faster, and spend less resources in their studies. As these systems grow in number, capability, and complexity, a new challenge arises - how w…
Decision MakingManagementProvable Pathways: Learning Multiple Tasks over Multiple Paths
Constructing useful representations across a large number of tasks is a key requirement for sample-efficient intelligent systems. A traditional idea in multitask learning (MTL) is building a shared representation across …
Generalization BoundsA Multiagent Framework for the Asynchronous and Collaborative Extension of Multitask ML Systems
The traditional ML development methodology does not enable a large number of contributors, each with distinct objectives, to work collectively on the creation and extension of a shared intelligent system. Enabling such a…
Multitask Pareto Optimization for Monotone Submodular Problems with Dynamic Constraints
Evolutionary multitasking is a recent approach that solves multiple related optimization problems within a single evolutionary run, rather than addressing each problem separately. We consider monotone submodular optimiza…
Asynchronous Tool Usage for Real-Time Agents
While frontier large language models (LLMs) are capable tool-using agents, current AI systems still operate in a strict turn-based fashion, oblivious to passage of time. This synchronous design forces user queries and to…
Automatic Speech Recognitionspeech-recognitionSpeech Recognitiontext-to-speech+1