Halle : Karras, 1877.

Bingen am Rhein : Selbstverlag des Verfassers, 1885.

Bruxelles : A. Manceaux, 1893.

Stuttgart : F. Enke, 1895.

London : Chapman & Hall, 1908.

Berlin (Louisen-Str. 53) : Published by F. Silber, [between 1800 and 1899]

London : Published by J.B. Virtue & Co. Ltd. : Jean Boussod Manzi Joyant & Co., [1897]

[1856]

London (6, Pall Mall) : Published ... by Henry Graves, & Comp.y, May 10th, 1856.

[Dublin] : National Art Union for Ireland, [between 1847 and 1851?]

[19--?]

[19--?]

[19--?]

[19--?]

[19--?]

[Place of publication not identified] : [Publisher not identified], 1895.

[London] : Waterlow Bros. & Layton, Limited, [1893]

(Printed in Austria)

Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image. In this work we adapt the information bottleneck concept for attribution. By adding noise to intermediate feature maps we restrict the flow of information and can quantify (in bits) how much information image regions provide. We compare our method against ten baselines using three different metrics on VGG-16 and ResNet-50, and find that our methods outperform all baselines in five out of six settings. The method's information-theoretic foundation provides an absolute frame of reference for attribution values (bits) and a guarantee that regions scored close to zero are not necessary for the network's decision. For reviews: https://openreview.net/forum?id=S1xWh1rYwB For code: https://github.com/BioroboticsLab/IBA

Grant (Family)

Klemm (Family)

Names, Personal -- Scottish.

Matthew Moores, Geoff Nicholls, Anthony Pettitt, Kerrie Mengersen.

Source: Bayesian Analysis, Volume 15, Number 1, 1--27.

Abstract:
The inverse temperature parameter of the Potts model governs the strength of spatial cohesion and therefore has a major influence over the resulting model fit. A difficulty arises from the dependence of an intractable normalising constant on the value of this parameter and thus there is no closed-form solution for sampling from the posterior distribution directly. There is a variety of computational approaches for sampling from the posterior without evaluating the normalising constant, including the exchange algorithm and approximate Bayesian computation (ABC). A serious drawback of these algorithms is that they do not scale well for models with a large state space, such as images with a million or more pixels. We introduce a parametric surrogate model, which approximates the score function using an integral curve. Our surrogate model incorporates known properties of the likelihood, such as heteroskedasticity and critical temperature. We demonstrate this method using synthetic data as well as remotely-sensed imagery from the Landsat-8 satellite. We achieve up to a hundredfold improvement in the elapsed runtime, compared to the exchange algorithm or ABC. An open-source implementation of our algorithm is available in the R package bayesImageS .

Stephanie McMains
Jan 12, 2011; 31:587-597
BehavioralSystemsCognitive

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