Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Meyerson, Elliot"'
Autor:
Nisioti, Eleni, Glanois, Claire, Najarro, Elias, Dai, Andrew, Meyerson, Elliot, Pedersen, Joachim Winther, Teodorescu, Laetitia, Hayes, Conor F., Sudhakaran, Shyam, Risi, Sebastian
Large Language Models (LLMs) have taken the field of AI by storm, but their adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work we investigate the potential synergies between LLMs and ALife, drawing on
Externí odkaz:
http://arxiv.org/abs/2407.09502
Autor:
Miikkulainen, Risto, Francon, Olivier, Young, Daniel, Meyerson, Elliot, Schwingshackl, Clemens, Bieker, Jacob, Cunha, Hugo, Hodjat, Babak
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. Based on available historical data on land-use changes and a simula
Externí odkaz:
http://arxiv.org/abs/2311.12304
Autor:
Meyerson, Elliot, Nelson, Mark J., Bradley, Herbie, Gaier, Adam, Moradi, Arash, Hoover, Amy K., Lehman, Joel
This paper pursues the insight that language models naturally enable an intelligent variation operator similar in spirit to evolutionary crossover. In particular, language models of sufficient scale demonstrate in-context learning, i.e. they can lear
Externí odkaz:
http://arxiv.org/abs/2302.12170
This paper characterizes the inherent power of evolutionary algorithms. This power depends on the computational properties of the genetic encoding. With some encodings, two parents recombined with a simple crossover operator can sample from an arbitr
Externí odkaz:
http://arxiv.org/abs/2202.09679
Autor:
Meyerson, Elliot, Miikkulainen, Risto
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. The observer is completely agnostic about any particular task being solved; it cares
Externí odkaz:
http://arxiv.org/abs/2010.02354
Autor:
Miikkulainen, Risto, Francon, Olivier, Meyerson, Elliot, Qiu, Xin, Canzani, Elisa, Hodjat, Babak
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. This paper demonstrat
Externí odkaz:
http://arxiv.org/abs/2005.13766
Autor:
Francon, Olivier, Gonzalez, Santiago, Hodjat, Babak, Meyerson, Elliot, Miikkulainen, Risto, Qiu, Xin, Shahrzad, Hormoz
Publikováno v:
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2020)
There is now significant historical data available on decision making in organizations, consisting of the decision problem, what decisions were made, and how desirable the outcomes were. Using this data, it is possible to learn a surrogate model, and
Externí odkaz:
http://arxiv.org/abs/2002.05368
Publikováno v:
Published at ICLR 2020
Neural Networks (NNs) have been extensively used for a wide spectrum of real-world regression tasks, where the goal is to predict a numerical outcome such as revenue, effectiveness, or a quantitative result. In many such tasks, the point prediction i
Externí odkaz:
http://arxiv.org/abs/1906.00588
Autor:
Meyerson, Elliot, Miikkulainen, Risto
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? To approach this question, deep multi-task learning is extended in this
Externí odkaz:
http://arxiv.org/abs/1906.00097
Deep neural networks (DNNs) have produced state-of-the-art results in many benchmarks and problem domains. However, the success of DNNs depends on the proper configuration of its architecture and hyperparameters. Such a configuration is difficult and
Externí odkaz:
http://arxiv.org/abs/1902.06827