Collaborating Authors

Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks Machine Learning

As the will to deploy neural networks models on embedded systems grows, and considering the related memory footprint and energy consumption issues, finding lighter solutions to store neural networks such as weight quantization and more efficient inference methods become major research topics. Parallel to that, adversarial machine learning has risen recently with an impressive and significant attention, unveiling some critical flaws of machine learning models, especially neural networks. In particular, perturbed inputs called adversarial examples have been shown to fool a model into making incorrect predictions. In this article, we investigate the adversarial robustness of quantized neural networks under different threat models for a classical supervised image classification task. We show that quantization does not offer any robust protection, results in severe form of gradient masking and advance some hypotheses to explain it. However, we experimentally observe poor transferability capacities which we explain by quantization value shift phenomenon and gradient misalignment and explore how these results can be exploited with an ensemble-based defense.

Understanding The Accuracy-Interpretability Trade-Off


In today's article we discussed about the trade off between model accuracy and model interpretability in the context of Machine Learning. Less flexible models are more interpretable and thus are more suitable in the inference context where we are mostly interested in understanding the relationship between the inputs and the output. On the other hand, more flexible models are way less interpretable but the results can be more accurate. Depending on the problem we are working on, we may have to pick the model that best serves our use case. We should however have in mind that in most of the cases, we have to find the sweet spot between model accuracy and model interpretability.

Explaining the Almon Distributed Lag Model


That post drew quite a number of email requests for more information about the Almon estimator, and how it fits into the overall scheme of things. In addition, Almon's approach to modelling distributed lags has been used very effectively more recently in the estimation of the so-called MIDAS model. The MIDAS model (developed by Eric Ghysels and his colleagues – e.g., see Ghysels et al., 2004) is designed to handle regression analysis using data with different observation frequencies. The acronym, "MIDAS", stands for "Mixed-Data Sampling". The MIDAS model can be implemented in R, for instance (e.g., see here), as well as in EViews.

Plus-sized model 'cried when asked to be cover model'

BBC News

US plus-size model Tess Holliday says she cried when asked to be on the front cover of Cosmopolitan's UK magazine.

How to tell which iPad model you have


Updated September 6, 2017 to reflect the latest iPad models. You might think you know which iPad you have. But when you need to know exactly which model you have, or better yet, which generation, it can get a little trickier. You don't have to be an Apple Store Genius to figure it out, though you do have to know where to look... and what to look for. In addition to the marketing names that we all know so well, all iPads have a model number.