The digital information age has generated new outlets for content creators to publish so-called “fake news”, a new form of propaganda that is intentionally designed to mislead the reader. With the widespread effects of the fast dissemination of fake news, efforts have been made to automate the process of fake news detection. A promising solution that has come up recently is to use machine learning to detect patterns in the news sources and articles, specifically deep neural networks, which have been successful in natural language processing. However, deep networks come with lack of transparency in the decision-making process, i.e. the “black-box problem”, which obscures its reliability. In this paper, we open this “black-box” and we show that the emergent representations from deep neural networks capture subtle but consistent differences in the language of fake and real news: signatures of exaggeration and other forms of rhetoric. Unlike previous work, we test the transferability of the learning process to novel news topics. Our results demonstrate the generalization capabilities of deep learning to detect fake news in novel subjects only from language patterns.

%B workshop on "AI for Social Good", NIPS 2018 %C Montreal, Canada %8 11/2018 %G eng %U http://hdl.handle.net/1721.1/120056 %0 Generic %D 2018 %T Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results %A Luke Arend %A Yena Han %A Martin Schrimpf %A Pouya Bashivan %A Kohitij Kar %A Tomaso Poggio %A James J. DiCarlo %A Xavier Boix %Xhttp://hdl.handle.net/1721.1/118847

%0 Conference Paper %B AAAI Conference on Artificial Intelligence %D 2017 %T Active Video Summarization: Customized Summaries via On-line Interaction. %A Garcia del Molino, A %A X Boix %A Lim, J. %A Tan, A %B AAAI Conference on Artificial Intelligence %G eng %0 Generic %D 2017 %T Eccentricity Dependent Deep Neural Networks for Modeling Human Vision %A Gemma Roig %A Francis Chen %A X Boix %A Tomaso Poggio %B Vision Sciences Society %0 Conference Paper %B AAAI Spring Symposium Series, Science of Intelligence %D 2017 %T Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision %A Francis Chen %A Gemma Roig %A Leyla Isik %A X Boix %A Tomaso Poggio %XHumans can recognize objects in a way that is invariant to scale, translation, and clutter. We use invariance theory as a conceptual basis, to computationally model this phenomenon. This theory discusses the role of eccentricity in human visual processing, and is a generalization of feedforward convolutional neural networks (CNNs). Our model explains some key psychophysical observations relating to invariant perception, while maintaining important similarities with biological neural architectures. To our knowledge, this work is the first to unify explanations of all three types of invariance, all while leveraging the power and neurological grounding of CNNs.

%B AAAI Spring Symposium Series, Science of Intelligence %G eng %U https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15360 %0 Generic %D 2017 %T Theory of Deep Learning III: explaining the non-overfitting puzzle %A Tomaso Poggio %A Keji Kawaguchi %A Qianli Liao %A Brando Miranda %A Lorenzo Rosasco %A Xavier Boix %A Jack Hidary %A Hrushikesh Mhaskar %X**THIS MEMO IS REPLACED BY CBMM MEMO 90**

A main puzzle of deep networks revolves around the absence of overfitting despite overparametrization and despite the large capacity demonstrated by zero training error on randomly labeled data. In this note, we show that the dynamical systems associated with gradient descent minimization of nonlinear networks behave near zero stable minima of the empirical error as gradient system in a quadratic potential with degenerate Hessian. The proposition is supported by theoretical and numerical results, under the assumption of stable minima of the gradient.

Our proposition provides the extension to deep networks of key properties of gradient descent methods for linear networks, that as, suggested in (1), can be the key to understand generalization. Gradient descent enforces a form of implicit regular- ization controlled by the number of iterations, and asymptotically converging to the minimum norm solution. This implies that there is usually an optimum early stopping that avoids overfitting of the loss (this is relevant mainly for regression). For classification, the asymptotic convergence to the minimum norm solution implies convergence to the maximum margin solution which guarantees good classification error for “low noise” datasets.

The implied robustness to overparametrization has suggestive implications for the robustness of deep hierarchically local networks to variations of the architecture with respect to the curse of dimensionality.

%8 12/2017 %1 %2http://hdl.handle.net/1721.1/113003

%0 Generic %D 2016 %T Foveation-based Mechanisms Alleviate Adversarial Examples %A Luo, Yan %A X Boix %A Gemma Roig %A Tomaso Poggio %A Qi Zhao %XWe show that adversarial examples, *i.e.*, the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based on foveations---applying the CNN in different image regions. To see this, first, we report results in ImageNet that lead to a revision of the hypothesis that adversarial perturbations are a consequence of CNNs acting as a linear classifier: CNNs act locally linearly to changes in the image regions with objects recognized by the CNN, and in other regions the CNN may act non-linearly. Then, we corroborate that when the neural responses are linear, applying the foveation mechanism to the adversarial example tends to significantly reduce the effect of the perturbation. This is because, hypothetically, the CNNs for ImageNet are robust to changes of scale and translation of the object produced by the foveation, but this property does not generalize to transformations of the perturbation. As a result, the accuracy after a foveation is almost the same as the accuracy of the CNN without the adversarial perturbation, even if the adversarial perturbation is calculated taking into account a foveation.