About: Jeff Clune

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Jeff Clune Goto Sponge NotDistinct Permalink

An Entity of Type : wikibase:Item, within Data Space : wikidata.demo.openlinksw.com associated with source document(s)

computer scientist

Attributes	Values
rdf:type	Item
description	computer scientist (en) науковець у галузі комп'ютерних технологій (uk)
rdfs:label	Jeff Clune (en) Jeff Clune (nl) Jeff Clune (sq)
skos:prefLabel	Jeff Clune (en) Jeff Clune (nl) Jeff Clune (sq)
name	Jeff Clune (en) Jeff Clune (nl) Jeff Clune (sq)
LinkedIn personal profile ID	wds:Q77049011-c9c5b48c-406c-1fd2-66f8-673ec2efc501
LinkedIn personal profile ID	jeff-clune-56403a26
given name	wds:Q77049011-E93F3BBA-B2D9-428C-A97E-CF6CEE7E4359
given name	Jeff
instance of	wds:Q77049011-f21a09da-4bb9-2f46-2029-328fd4d09949
instance of	Homo sapiens
award received	wds:Q77049011-7f3c5c2a-48af-e4fc-8a3c-f889a618b59c
award received	Presidential Early Career Award for Scientists and Engineers
Google Knowledge Graph ID	wds:Q77049011-954a1c1c-495e-1639-3d34-7793a840e078
MR Author ID	wds:Q77049011-05e0f9c1-48fa-057a-4a7d-386f624ffc90
Mathematics Genealogy Project ID	wds:Q77049011-85e28ec1-4545-d728-eec2-2b40d0d8d570
Google Knowledge Graph ID	http://g.co/kg/g/11f0ysfgbz
Google Knowledge Graph ID	/g/11f0ysfgbz
MR Author ID	857126
Mathematics Genealogy Project ID	296044
Google Scholar author ID	wds:Q77049011-0fee936b-4c05-919f-4fa4-1f22289f01db
Google Scholar author ID	5TZ7f5wAAAAJ
occupation	wds:Q77049011-656a03a4-49da-f9b8-8a46-a804c30f414d wds:Q77049011-b48659d5-4744-081a-b5f0-c4a6f816781c
employer	wds:Q77049011-4132243f-4bbe-4681-809c-416d9a307302 wds:Q77049011-7db55095-49cc-078e-9316-b40bb089327c wds:Q77049011-9e7a65b7-4a1c-c042-a0a4-83013b839bff wds:Q77049011-d042c76a-44c5-a934-2ff3-dbf5eac934b3 wds:Q77049011-dd98b192-4cbf-7d01-25b5-5ee9373cf345
X username	wds:Q77049011-cd5509a1-4fd9-0082-7556-e8a25db6ccdf
sex or gender	wds:Q77049011-920627e8-469b-ce73-dbac-1710beeff14e
VIAF ID	wds:Q77049011-2a38e7a7-4c82-b0a1-12c4-a5116a0213d1
Library of Congress authority ID	wds:Q77049011-58d7c895-49b2-e17a-2e0e-c0367e4e4a49
DBLP author ID	wds:Q77049011-03644812-4b5d-887c-ebcc-3b922d7b763a
educated at	wds:Q77049011-1fafd23d-4383-287e-a16b-56faf46682e0 wds:Q77049011-55a59582-42e4-7921-75a8-44f8b3f30ff0
family name	wds:Q77049011-d5bd7c4b-4197-8ea6-1aad-de3748fd9736
official website	wds:Q77049011-63ce13e6-447a-6ceb-eb1b-885c7b2581d4
VIAF ID	http://viaf.org/viaf/310629078
Library of Congress authority ID	http://id.loc.gov/authorities/names/no2014114287
DBLP author ID	https://dblp.org/pid/49/2799.rdf
occupation	university teacher computer scientist
employer	OpenAI University of Wyoming University of British Columbia Vancouver campus Cornell University Uber AI Labs
X username	jeffclune
sex or gender	male
VIAF ID	310629078
Library of Congress authority ID	no2014114287
DBLP author ID	49/2799
educated at	University of Michigan Michigan State University
family name	Clune
official website	http://jeffclune.com/
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q77049011
is author of	Neural modularity helps organisms evolve to learn new skills without forgetting old skills The evolutionary origins of modularity Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes WebAL Comes of Age: A Review of the First 21 Years of Artificial Life on the Web. Robots that can adapt like animals. Selective pressures for accurate altruism targeting: evidence from digital evolution for difficult-to-test aspects of inclusive fitness theory On the Performance of Indirect Encoding Across the Continuum of Regularity Evolving neural networks that are both modular and regular Curiosity Search: Producing Generalists by Encouraging Individuals to Continually Explore and Acquire Skills throughout Their Lifetime Correction: Curiosity Search: Producing Generalists by Encouraging Individuals to Continually Explore and Acquire Skills Throughout Their Lifetime Digital evolution with avida How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System The Evolutionary Origins of Hierarchy Understanding Neural Networks via Feature Visualization: A Survey Investigating whether hyperNEAT produces modular neural networks Evolving coordinated quadruped gaits with the HyperNEAT generative encoding The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities Designing neural networks through neuroevolution First return, then explore Ontogeny tends to recapitulate phylogeny in digital organisms Improving HybrID: How to best combine indirect and direct encoding in evolutionary algorithms Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks. Problem decomposition using indirect reciprocity in evolved populations Reports on the 2013 AAAI Fall Symposium Series HybrID: A Hybridization of Indirect and Direct Encodings for Evolutionary Computation The sensitivity of HyperNEAT to different geometric representations of a problem How a Generative Encoding Fares as Problem-Regularity Decreases Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2 The Evolution of Division of Labor Synthesizing the preferred inputs for neurons in neural networks via deep generator networks How transferable are features in deep neural networks?

Faceted Search & Find service v1.16.117 as of May 05 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3239 as of May 5 2024, on Linux (x86_64-centos_6-linux-gnu), Single-Server Edition (378 GB total memory, 193 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software