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

A new problem of evil

Stephen Law explains his challenge for theist

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