Authors

* External authors

Venue

Date

Share

Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks

Lemeng Wu*

Bo Liu*

Peter Stone

Qiang Liu*

* External authors

NeurIPS-2020

2020

Abstract

We propose firefly neural architecture descent, a general framework for progressively and dynamically growing neural networks to jointly optimize the networks' parameters and architectures. Our method works in a steepest descent fashion, which iteratively finds the best network within a functional neighborhood of the original network that includes a diverse set of candidate network structures. By using Taylor approximation, the optimal network structure in the neighborhood can be found with a greedy selection procedure. We show that firefly descent can flexibly grow networks both wider and deeper, and can be applied to learn accurate but resource-efficient neural architectures that avoid catastrophic forgetting in continual learning. Empirically, firefly descent achieves promising results on both neural architecture search and continual learning. In particular, on a challenging continual image classification task, it learns networks that are smaller in size but have higher average accuracy than those learned by the state-of-the-art methods.

Related Publications

Benchmarking Reinforcement Learning Techniques for Autonomous Navigation

ICRA, 2023
Zifan Xu*, Bo Liu*, Xuesu Xiao*, Anirudh Nair*, Peter Stone

Deep reinforcement learning (RL) has broughtmany successes for autonomous robot navigation. However,there still exists important limitations that prevent real-worlduse of RL-based navigation systems. For example, most learningapproaches lack safety guarantees; and learned na…

Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways

ICRA, 2023
Jin-Soo Park*, Xuesu Xiao*, Garrett Warnell*, Harel Yedidsion*, Peter Stone

While current systems for autonomous robot navigation can produce safe and efficient motion plans in static environments, they usually generate suboptimal behaviors when multiple robots must navigate together in confined spaces. For example, when two robots meet each other i…

A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems

Neural Networks, 2023
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*, 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*

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to “real world” events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and syst…

  • HOME
  • Publications
  • Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.