Deep Neural Network Compiler (DNNC) is an AOT Compiler and inference framework. Part-1 of this article series showed how to use DNNC as an inference framework. This article shows how to use DNNC to compile a machine learning model on a microcontroller or microcomputer worth ranging from 10¢ to $5. A deep learning model is a collection of programs, trained parameters (weights and biases), and associated computer elements. It could be stored as a python program or stored in a format like onnx and TensorFlow protobuf.

Apple has purchased Seattle startup Xnor.ai, a company that specializes in on-device artificial intelligence, reports GeekWire, citing sources with knowledge of the acquisition. Apple is said to have paid around $200 million for Xnor.ai, and while both Apple and Xnor.ai have declined to comment on the potential acquisition, much of the Xnor.ai Xnor.ai's technology lets companies run deep learning algorithms locally on devices that include smartphones and wearables rather than requiring these computations to be done in the cloud. Xnor promised complete privacy of data along with reduced memory load and power demands. Given Apple's deep interest in personal privacy, an acquisition of technology for handling AI on device is unsurprising.

We are a passionante group of biologists, ecologists, data scientists, software developers, and AI specialists based in Montréal. We aim to make detecting whales as fast and easy as possible. Whether it's providing wildlife managers with cutting edge tools to increase their efficiency, accuracy, and lighten their workload - or helping ports manage whale presence and marine traffic in a healthy and profitable way - we want anyone interested in whale detection to have access. To learn more about us please visit our website at: https://www.whaleseeker.com. The objective of the project is to work with the multidisciplinary team to develop a solution that processes images using Machine Learning to identify whale targets among large (35 MPx and above) aerial pictures containing multiple sources of interferences like glare, waves, rocks, muddy water, etc.

Among the many things we've become addicted to on our smartphones is checking the weather. If you're anything like me, you open a weather app at least twice a day: in the morning to know what to expect for the day ahead, maybe before your commute home so you can prepare for possible rain or snow, and sometimes before bed to get an idea of what to wear or what activities to plan for the next day. Depending where you live, how much time you spend outside, and how prone your area is to rapid weather changes, maybe you check the forecast even more frequently than that. The fact that our phones now contain hour-by-hour breakdowns of temperature and likelihood of precipitation means we can be well-informed and well-prepared. But these forecasts are coming at a greater cost than we know, and they're not always right.

The Batch Normalization paper published back in 2015 by Sergey Ioffe, Christian Szegedy took the deep learning community by storm. It became one of the most implemented techniques in deep learning after it was released. Notably, its ability to accelerate training of deep learning models and achieve the same accuracy in 14 times fewer training steps was a great catch. Indeed that brought in the attention which it gets today (who doesn't want to train faster?). And there's been a lot of similar papers like layer normalization, instance normalization and a few others.

We introduce a deep neural network based method for solving a class of elliptic partial differential equations. We approximate the solution of the PDE with a deep neural network which is trained under the guidance of a probabilistic representation of the PDE in the spirit of the Feynman-Kac formula. The solution is given by an expectation of a martingale process driven by a Brownian motion. As Brownian walkers explore the domain, the deep neural network is iteratively trained using a form of reinforcement learning. Our method is a 'Derivative-Free Loss Method' since it does not require the explicit calculation of the derivatives of the neural network with respect to the input neurons in order to compute the training loss. The advantages of our method are showcased in a series of test problems: a corner singularity problem, an interface problem, and an application to a chemotaxis population model.

Binary Neural Networks (BNNs) are neural networks which use binary weights and activations instead of the typical 32-bit floating point values. They have reduced model sizes and allow for efficient inference on mobile or embedded devices with limited power and computational resources. However, the binarization of weights and activations leads to feature maps of lower quality and lower capacity and thus a drop in accuracy compared to traditional networks. Previous work has increased the number of channels or used multiple binary bases to alleviate these problems. In this paper, we instead present MeliusNet consisting of alternating two block designs, which consecutively increase the number of features and then improve the quality of these features. In addition, we propose a redesign of those layers that use 32-bit values in previous approaches to reduce the required number of operations. Experiments on the ImageNet dataset demonstrate the superior performance of our MeliusNet over a variety of popular binary architectures with regards to both computation savings and accuracy. Furthermore, with our method we trained BNN models, which for the first time can match the accuracy of the popular compact network MobileNet in terms of model size and accuracy. Our code is published online: https://github.com/hpi-xnor/BMXNet-v2

A Little Fog for a Large T urn Harshitha Machiraju, Vineeth N Balasubramanian Indian Institute of Technology, Hyderabad, India {ee14btech11011, vineethnb }@iith.ac.in Abstract Small, carefully crafted perturbations called adversarial perturbations can easily fool neural networks. However, these perturbations are largely additive and not naturally found. W e turn our attention to the field of Autonomous navigation wherein adverse weather conditions such as fog have a drastic effect on the predictions of these systems. These weather conditions are capable of acting like natural adversaries that can help in testing models. T o this end, we introduce a general notion of adversarial perturbations, which can be created using generative models and provide a methodology inspired by Cycle-Consistent Generative Adversarial Networks to generate adversarial weather conditions for a given image. Our formulation and results show that these images provide a suitable testbed for steering models used in Autonomous navigation models. Our work also presents a more natural and general definition of Adversarial perturbations based on Perceptual Similarity. 1 1. Introduction Autonomous navigation has occupied a central position in the efforts of computer vision researchers in recent years. Autonomous vehicles can not only aid navigation in urban areas but also provide critical support in disaster-affected areas, places with unknown topography (such as Mars), and many more. The vast potential of the applications thereof and the feasibility of the solutions in contemporary times has led to the growth of several organizations across industry, academia, and government institutions that are investing significant efforts on self-driving vehicles.

Particle identification is one of the core tasks in the data analysis pipeline at the Large Hadron Collider (LHC). Statistically, this entails the identification of rare signal events buried in immense backgrounds that mimic the properties of the former. In machine learning parlance, particle identification represents a classification problem characterized by overlapping and imbalanced classes. Boosted decision trees (BDTs) have had tremendous success in the particle identification domain but more recently have been overshadowed by deep learning (DNNs) approaches. This work proposes an algorithm to extract more out of standard boosted decision trees by targeting their main weakness, susceptibility to overfitting. This novel construction harnesses the meta-learning techniques of boosting and bagging simultaneously and performs remarkably well on the ATLAS Higgs (H) to tau-tau data set (ATLAS et al., 2014) which was the subject of the 2014 Higgs ML Challenge (Adam-Bourdarios et al., 2015). While the decay of Higgs to a pair of tau leptons was established in 2018 (CMS collaboration et al., 2017) at the 4.9$\sigma$ significance based on the 2016 data taking period, the 2014 public data set continues to serve as a benchmark data set to test the performance of supervised classification schemes. We show that the score achieved by the proposed algorithm is very close to the published winning score which leverages an ensemble of deep neural networks (DNNs). Although this paper focuses on a single application, it is expected that this simple and robust technique will find wider applications in high energy physics.

We report on the use of sentiment analysis on news and social media to analyze and predict the price of Bitcoin. Bitcoin is the leading cryptocurrency and has the highest market capitalization among digital currencies. Predicting Bitcoin values may help understand and predict potential market movement and future growth of the technology. Unlike (mostly) repeating phenomena like weather, cryptocurrency values do not follow a repeating pattern and mere past value of Bitcoin does not reveal any secret of future Bitcoin value. Humans follow general sentiments and technical analysis to invest in the market. Hence considering people's sentiment can give a good degree of prediction. We focus on using social sentiment as a feature to predict future Bitcoin value, and in particular, consider Google News and Reddit posts. We find that social sentiment gives a good estimate of how future Bitcoin values may move. We achieve the lowest test RMSE of 434.87 using an LSTM that takes as inputs the historical price of various cryptocurrencies, the sentiment of news articles and the sentiment of Reddit posts.