no code implementations • 21 Nov 2023 • Risto Miikkulainen, Olivier Francon, Daniel Young, Elliot Meyerson, Clemens Schwingshackl, Jacob Bieker, Hugo Cunha, Babak Hodjat
How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance, and therefore climate change.
1 code implementation • 23 Feb 2023 • Elliot Meyerson, Mark J. Nelson, Herbie Bradley, Adam Gaier, Arash Moradi, Amy K. Hoover, Joel Lehman
The promise of such language model crossover (which is simple to implement and can leverage many different open-source language models) is that it enables a simple mechanism to evolve semantically-rich text representations (with few domain-specific tweaks), and naturally benefits from current progress in language models.
no code implementations • 19 Feb 2022 • Elliot Meyerson, Xin Qiu, Risto Miikkulainen
The conclusion is that, across evolutionary computation areas as diverse as genetic programming, neuroevolution, genetic algorithms, and theory, expressive encodings can be a key to understanding and realizing the full power of evolution.
no code implementations • ICLR 2021 • Elliot Meyerson, Risto Miikkulainen
This paper frames a general prediction system as an observer traveling around a continuous space, measuring values at some locations, and predicting them at others.
1 code implementation • 28 May 2020 • Risto Miikkulainen, Olivier Francon, Elliot Meyerson, Xin Qiu, Elisa Canzani, Babak Hodjat
Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with non-pharmaceutical interventions (NPIs) such as social distancing restrictions and school and business closures.
1 code implementation • 13 Feb 2020 • Olivier Francon, Santiago Gonzalez, Babak Hodjat, Elliot Meyerson, Risto Miikkulainen, Xin Qiu, Hormoz Shahrzad
Using this data, it is possible to learn a surrogate model, and with that model, evolve a decision strategy that optimizes the outcomes.
2 code implementations • ICLR 2020 • Xin Qiu, Elliot Meyerson, Risto Miikkulainen
In many such tasks, the point prediction is not enough: the uncertainty (i. e. risk or confidence) of that prediction must also be estimated.
1 code implementation • NeurIPS 2019 • Elliot Meyerson, Risto Miikkulainen
As deep learning applications continue to become more diverse, an interesting question arises: Can general problem solving arise from jointly learning several such diverse tasks?
1 code implementation • 18 Feb 2019 • Jason Liang, Elliot Meyerson, Babak Hodjat, Dan Fink, Karl Mutch, Risto Miikkulainen
However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters.
no code implementations • ICML 2018 • Elliot Meyerson, Risto Miikkulainen
Deep multitask learning boosts performance by sharing learned structure across related tasks.
no code implementations • ICML 2018 • Elliot Meyerson, Risto Miikkulainen
Deep multitask learning boosts performance by sharing learned structure across related tasks.
no code implementations • 10 Mar 2018 • Jason Liang, Elliot Meyerson, Risto Miikkulainen
Multitask learning, i. e. learning several tasks at once with the same neural network, can improve performance in each of the tasks.
no code implementations • ICLR 2018 • Elliot Meyerson, Risto Miikkulainen
Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering.
no code implementations • 18 Apr 2017 • Elliot Meyerson, Risto Miikkulainen
The conclusion is that behavior domination can help illuminate the complex dynamics of behavior-driven search, and can thus lead to the design of more scalable and robust algorithms.
4 code implementations • 1 Mar 2017 • Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Dan Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, Babak Hodjat
The success of deep learning depends on finding an architecture to fit the task.
no code implementations • 4 Dec 2015 • Alexander Braylan, Mark Hollenbeck, Elliot Meyerson, Risto Miikkulainen
A general approach to knowledge transfer is introduced in which an agent controlled by a neural network adapts how it reuses existing networks as it learns in a new domain.