Author

L. Talas

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Animal Behavior & Cognition· 3 Ecology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Approach:contrastive learning· 1 Research Focus:ecology & biodiversity· 1 Assay:imaging· 1 Assay:metagenomics· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

I. Cuthill· 2 shared J. Fennell· 2 shared N. Scott-Samuel· 2 shared R. Baddeley· 2 shared A. Heinsalu· 1 shared A. Kisand· 1 shared I. C. Cuthill· 1 shared I. Tonno· 1 shared

Activity by Year

2019-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

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2 shared papersAnimal Behavior & Cognition

I. Cuthill

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Author profile →

4 papers

Recent·Most cited

Drivers of change and ecosystem status in a temperate lake over the last Post-Glacial period from 14.5 kyr

I. Tonno, R. Freiberg, L. Talas, A. Kisand, S. Belle, N. Stivrins, T. Alliksaar, A. Heinsalu, S. Veski, V. Kisand

The paper analyzes long-term ecological changes in a temperate lake over 14.5 kyr using paleoarchives to study the impact of climate and human activity on ecosystem status.

bioRxiv · Mar 27, 2020 · PreprintEcology

The Camouflage Machine: Optimising protective colouration using deep learning with genetic algorithms

J. Fennell, L. Talas, R. Baddeley, I. Cuthill, N. Scott-Samuel

This paper develops a method using deep learning and genetic algorithms to identify the most effective camouflage and conspicuous patterns in different environments.

bioRxiv · Jan 12, 2020Animal Behavior & Cognition

Evolving optimum camouflage with Generative Adversarial Networks

L. Talas, J. G. Fennell, K. Kjernsmo, I. C. Cuthill, N. E. Scott-Samuel, R. J. Baddeley

This paper uses Generative Adversarial Networks to simulate an evolutionary arms race between synthetic prey camouflage and predators, producing more effective concealment than existing techniques.

bioRxiv · Mar 18, 2019Animal Behavior & Cognition

Optimising colour for camouflage and visibility using deep learning: the effects of the environment and the observer’s visual system

J. Fennell, L. Talas, R. Baddeley, I. Cuthill, N. Scott-Samuel

This paper uses deep learning to identify optimal colors for camouflage or visibility in different environments and for different visual systems.

bioRxiv · Mar 18, 2019Animal Behavior & Cognition