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Uncertainty-aware estimation of population abundance using machine learning

Bastiaan J. Boom, Emma Beauxis-Aussalet, Lynda Hardman, Robert B. Fisher
2015 Multimedia Systems  
It significantly improves the counting of elements per class. We further propose visualization designs for understanding and evaluating the classification uncertainty.  ...  Machine Learning is widely used for mining collections, such as images, sounds, or texts, by classifying their elements into categories.  ...  The second strategy is to Table 1 : Error in counts for the different datasets, showing that our approach is able to correct errors based on the similarity scores from other machine learning methods  ... 
doi:10.1007/s00530-015-0479-0 fatcat:stgewm5wwnf2nmug5szk5dnxyi

Deep learning with self-supervision and uncertainty regularization to count fish in underwater images [article]

Penny Tarling, Mauricio Cantor, Albert Clapés, Sergio Escalera
2021 arXiv   pre-print
To this end, we employ deep learning, with a density-based regression approach, to count fish in low-resolution sonar images.  ...  From experiments on both contrasting datasets, we demonstrate our network outperforms the few other deep learning models implemented for solving this task.  ...  Advances relative to previous work on deep learning to count fish We tested our framework against the regularization method of the only previous study on using deep learning to count schools of fish in  ... 
arXiv:2104.14964v1 fatcat:6gokk6cixfhi3kdr3pll67pk2i

The Uncertainty Bellman Equation and Exploration [article]

Brendan O'Donoghue, Ian Osband, Remi Munos, Volodymyr Mnih
2018 arXiv   pre-print
We consider the exploration/exploitation problem in reinforcement learning.  ...  This bound can be much tighter than traditional count-based bonuses that compound standard deviation rather than variance.  ...  E t P h s saQ h+1 s a − E t (P h s saQ h+1 s a ) 2 = E t (P h s sa − E Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80, 2018.  ... 
arXiv:1709.05380v4 fatcat:y5mkpm3npbcbjgccjpdn63wydy

Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning [article]

David Janz, Jiri Hron, Przemysław Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek
2019 arXiv   pre-print
Posterior sampling for reinforcement learning (PSRL) is an effective method for balancing exploration and exploitation in reinforcement learning.  ...  DQN, on 36 of those.  ...  The uncertainty Bellman equation and exploration. In International Conference on Machine Learning (ICML), 2018. Osband, I. and Van Roy, B.  ... 
arXiv:1810.06530v5 fatcat:v5hr3hie5ffkfpoctnc3qrc4eq

Uncertainty in Various Habitat Suitability Models and Its Impact on Habitat Suitability Estimates for Fish

Yu-Pin Lin, Wei-Chih Lin, Wei-Yao Wu
2015 Water  
machine learning models: the support vector machine (SVM), random forest (RF) and the artificial neural network (ANN), and an ensemble model (where the latter is the average of the preceding five models  ...  Owing to complex sources of uncertainty, such models may yield projections with varying degrees of uncertainty.  ...  The data analysis, simulation of SDM models and uncertainty analyses were conducted by Wei-Chih Lin and Wei-Yao Wu with the supervision of Yu-Pin Lin.  ... 
doi:10.3390/w7084088 fatcat:gjctafgjxbhoppakzkrrfz54x4

Individuation, counting, and statistical inference: The role of frequency and whole-object representations in judgment under uncertainty

Gary L. Brase, Leda Cosmides, John Tooby
1998 Journal of experimental psychology. General  
The ability to count depends on the ability to individuate the world: to see it as composed of discrete entities.  ...  The ability to make well-calibrated probability judgments depends, at a very basic level, on the ability to count.  ...  Gigerenzer and Hoffrage (1995) have obtained similar results on other Bayesian problems.  ... 
doi:10.1037//0096-3445.127.1.3 fatcat:puqdmsvl6jg7flfssqawwttdai

Individuation, counting, and statistical inference: The role of frequency and whole-object representations in judgment under uncertainty

Gary L. Brase, Leda Cosmides, John Tooby
1998 Journal of experimental psychology. General  
The ability to count depends on the ability to individuate the world: to see it as composed of discrete entities.  ...  The ability to make well-calibrated probability judgments depends, at a very basic level, on the ability to count.  ...  Gigerenzer and Hoffrage (1995) have obtained similar results on other Bayesian problems.  ... 
doi:10.1037/0096-3445.127.1.3 fatcat:hevj6jhgn5f4rk6z2ggi6a5nsq

Understanding Clinical Uncertainty

Sayra M. Cristancho, Tavis Apramian, Meredith Vanstone, Lorelei Lingard, Michael Ott, Richard J. Novick
2013 Academic Medicine  
A.3.5 Consequences 16 The two fish farms reduced there water intake from the river but oil exposure killed 500-600 fishes and caused taste of oil to all 30 tons of fish. 17 The alternative water supply  ...  However, on passing the outer mole, Capt. L. sets the machine controllers at 6 on all three engines, and Herald accelerated rapidly to 18 knots.  ...  This book discusses these issues on the basis of the present rapid evolution of new cognitive approaches to the study of decision making in action and dynamic, learning organisations, and the rapid change  ... 
doi:10.1097/acm.0b013e3182a3116f pmid:23969352 pmcid:PMC5578757 fatcat:t6k7l65a3jcgxgoafz7n7guisy

Avoiding post-truth environmental conflict in New Zealand: communicating uncertainties in endangered species science

Anna Palliser, Giles Dodson
2019 JCOM: Journal of Science Communication  
We argue trust may be rebuilt by a combination of deliberative approaches to environmental governance, transparency about uncertainties, information gaps and divergent scientific opinions, and reformulation  ...  view getting trends is one of the hardest things to obtain.  ...  species [Fishserve, 2016] . 18 He said this was because he loved fishing and it was a family business, passed on from his dad.  ... 
doi:10.22323/2.18040205 fatcat:7rjo5zuianb5xgqy2gomgyrpve

Support vector machine under uncertainty: An application for hydroacoustic classification of fish-schools in Chile

Paul Bosch, Julio López, Héctor Ramírez, Hugo Robotham
2013 Expert systems with applications  
When this procedure is applied to the classification of fish schools we obtain a classifier with a better performance than the deterministic classifier.  ...  In this work we apply multi-class support vector machines (SVMs) and a multi-class stochastic SVM formulation to the classification of fish schools of three species: anchovy, common sardine, and Jack Mackerel  ...  of classifier's class predictions with respect to the actual outcome on some labeled learning set.  ... 
doi:10.1016/j.eswa.2013.01.006 fatcat:snvkp5g76fgqleq4tuqwzfztha

Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations [article]

Lily Xu, Shahrzad Gholami, Sara Mc Carthy, Bistra Dilkina, Andrew Plumptre, Milind Tambe, Rohit Singh, Mustapha Nsubuga, Joshua Mabonga, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Tom Okello (+1 others)
2019 arXiv   pre-print
To help combat poaching, the Protection Assistant for Wildlife Security (PAWS) is a machine learning pipeline that has been developed as a data-driven approach to identify areas at high risk of poaching  ...  We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia.  ...  We are grateful to the rangers in MFNP and SWS for supporting our field tests, especially in collecting and providing data from their patrols.  ... 
arXiv:1903.06669v3 fatcat:wvttsoa3nzgvxgijn7smpwpd5u

Optimization Models under Uncertainty in Distributed Generation Systems: A Review

Àlex Alonso-Travesset, Helena Martín, Sergio Coronas, Jordi de la Hoz
2022 Energies  
Traditionally, optimization models have been used to overcome this complexity, and currently, research is focusing on integrating uncertainties on them.  ...  Distributed generation systems (DGSs) are one of the key developments enabling the energy transition.  ...  Machine Learning (ML) The advance of artificial intelligence has led to a great development in the field of ML. ML algorithms can deal with uncertainties in DGS.  ... 
doi:10.3390/en15051932 fatcat:ayccf5pna5acpapsez45ogd5bi

Niches, models, and climate change: Assessing the assumptions and uncertainties

J. A. Wiens, D. Stralberg, D. Jongsomjit, C. A. Howell, M. A. Snyder
2009 Proceedings of the National Academy of Sciences of the United States of America  
These models contain assumptions that add to the uncertainty in model projections stemming from the structure of the models, the algorithms used to translate niche associations into distributional probabilities  ...  SDMs can provide a useful way to incorporate future conditions into conservation and management practices and decisions, but the uncertainties of model projections must be balanced with the risks of taking  ...  (55) and BIO-CLIM (56), as well as more sophisticated machine-learning algorithms such as Maxent (57) .  ... 
doi:10.1073/pnas.0901639106 pmid:19822750 pmcid:PMC2780938 fatcat:2evewbktxbbmngqe7ptgsumo7q

Enlisting Supervised Machine Learning in Mapping Scientific Uncertainty Expressed in Food Risk Analysis

Akos Rona-Tas, Antoine Cornuéjols, Sandrine Blanchemanche, Antonin Duroy, Christine Martin
2017 Sociological Methods & Research  
Acknowledgments We would like to thank Eve Feinblatt-Mélèze for assisting in the development of the ontologies and Edward Hunter, Kevin Lewis and Juan Pablo Pardo Guerra for their generous comments on  ...  Or one can count the number of times this word is present.  ...  One of the ways ML can help with coding is by calling attention to human mistakes. Thus, ML learns from human coders, and human coders can learn from ML.  ... 
doi:10.1177/0049124117729701 fatcat:ctfo6wbdzbh3nnygjtjhgs5wju

On the Uncertainty Principle

Mesut Kavak
2016 American Journal of Physics and Applications  
Only one pencil and one paper are enough. At this situation, the biggest problem is to be one of them exists and the other does not of them.  ...  Forming the theoretical, philosophical infrastructure of the some physical concepts and phenomena such as kinetic energy, uncertainty [1], length contraction [2], relative energy transformations, gravity  ...  Even so, Science Publishing Group was not there while I was thinking and preparing this article; so especially I thank to myself, never gave up on thinking for hours in every kind of thinking  ... 
doi:10.11648/j.ajpa.20160404.12 fatcat:beplby76jzexldv5yy3i5srcby
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