1,746 research outputs found
Explaining Classifiers using Adversarial Perturbations on the Perceptual Ball
We present a simple regularization of adversarial perturbations based upon
the perceptual loss. While the resulting perturbations remain imperceptible to
the human eye, they differ from existing adversarial perturbations in that they
are semi-sparse alterations that highlight objects and regions of interest
while leaving the background unaltered. As a semantically meaningful adverse
perturbations, it forms a bridge between counterfactual explanations and
adversarial perturbations in the space of images. We evaluate our approach on
several standard explainability benchmarks, namely, weak localization,
insertion deletion, and the pointing game demonstrating that perceptually
regularized counterfactuals are an effective explanation for image-based
classifiers.Comment: CVPR 202
Natural Kinds of Substance
This paper presents an extension of Putnam’s account of how substance terms such as ‘water’ and ‘gold’ function and of how a posteriori necessary truths concerning the underlying microstructures of such kinds may be derived. The paper has three aims:
(1) to refute a familiar criticism of Putnam’s account: that it presupposes what Salmon calls an ‘irredeemably metaphysical, and philosophically controversial, theory of essentialism’. I show how all the details of Putnam’s account – including those Salmon believes smuggle in such essentialist commitments – can be squared with a rejection of any such essentialist metaphysics.
(2) to reveal why Steward is wrong to suppose that, by helping himself to the claim that ‘H2O’ is a rigid designator of a substance, Kripke, too, presupposes something controversially ‘metaphysical’.
(3) to show how my proposed account also sidesteps a variety of objections raised by Needham and others who argue that Kripke’s and Putnam’s accounts of how ‘water’ and ‘gold’ function founder upon the sheer microstructural complexity of the phenomena in question
The Pandora’s Box Objection to Skeptical Theism
Skeptical theism is a leading response to the evidential argument from evil against the existence of God. Skeptical theists attempt to block the inference from the existence of inscrutable evils (evil for which we can think of no God-justifying reason) to gratuitous evils (evils for which there is no God justifying reason) by insisting that given our cognitive limitations, it wouldn’t be surprising if there were God-justifying reasons we can’t think of. A well-known objection to skeptical theism is that it opens up a skeptical Pandora’s box, generating implausibly wide-ranging forms of skepticism, including skepticism about the external world and past. This paper looks at several responses to this Pandora’s box objection, including a popular response devised by Beaudoin and Bergmann. I find that all of the examined responses fail. It appears the Pandora’s box objection to skeptical theism still stands
Wittgensteinian Accounts of Religious Belief: Non-Cognitivist, Juicer, and Atheist-Minus
Wittgenstein's views on religious belief are cryptic. We have comparatively few of his comments on religion, and most of what we do have were neither recorded by Wittgenstein himself nor intended by him for publication. Here I aim to assess some of the arguments that have been attributed to Wittgenstein in support of a view about religious belief that I call No Contradiction
Estimating Chicago's tree cover and canopy height using multi-spectral satellite imagery
Information on urban tree canopies is fundamental to mitigating climate
change [1] as well as improving quality of life [2]. Urban tree planting
initiatives face a lack of up-to-date data about the horizontal and vertical
dimensions of the tree canopy in cities. We present a pipeline that utilizes
LiDAR data as ground-truth and then trains a multi-task machine learning model
to generate reliable estimates of tree cover and canopy height in urban areas
using multi-source multi-spectral satellite imagery for the case study of
Chicago.Comment: 4 pages, 4 figures, Submitted to Tackling Climate Change with Machine
Learning: workshop at NeurIPS 202
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