Artificial Intelligence (AI) applications based on Deep Neural Networks (DNN) or Deep Learning (DL) have become popular due to their success in solving problems likeimage analysis and speech recognition. Training a DNN is computationally intensive and High Performance Computing(HPC) has been a key driver in AI growth. Virtualisation and container technology have led to the convergence of cloud and HPC infrastructure. These infrastructures with diverse hardware increase the complexity of deploying and optimising AI training workloads. AI training deployments in HPC or cloud can be optimised with target-specific libraries, graph compilers, andby improving data movement or IO. Graph compilers aim to optimise the execution of a DNN graph by generating an optimised code for a target hardware/backend. As part of SODALITE (a Horizon 2020 project), MODAK tool is developed to optimise application deployment in software defined infrastructures. Using input from the data scientist and performance modelling, MODAK maps optimal application parameters to a target infrastructure and builds an optimised container. In this paper, we introduce MODAK and review container technologies and graph compilers for AI. We illustrate optimisation of AI training deployments using graph compilers and Singularity containers. Evaluation using MNIST-CNN and ResNet50 training workloads shows that custom built optimised containers outperform the official images from DockerHub. We also found that the performance of graph compilers depends on the target hardware and the complexity of the neural network.

Finding objects is essential for almost any daily-life visual task. Saliency models have been useful to predict fixation locations in natural images, but are static, i.e., they provide no information about the time-sequence of fixations. Nowadays, one of the biggest challenges in the field is to go beyond saliency maps to predict a sequence of fixations related to a visual task, such as searching for a given target. Bayesian observer models have been proposed for this task, as they represent visual search as an active sampling process. Nevertheless, they were mostly evaluated on artificial images, and how they adapt to natural images remains largely unexplored. Here, we propose a unified Bayesian model for visual search guided by saliency maps as prior information. We validated our model with a visual search experiment in natural scenes recording eye movements. We show that, although state-of-the-art saliency models perform well in predicting the first two fixations in a visual search task, their performance degrades to chance afterward. This suggests that saliency maps alone are good to model bottom-up first impressions, but are not enough to explain the scanpaths when top-down task information is critical. Thus, we propose to use them as priors of Bayesian searchers. This approach leads to a behavior very similar to humans for the whole scanpath, both in the percentage of target found as a function of the fixation rank and the scanpath similarity, reproducing the entire sequence of eye movements.

Dimensionality reduction and clustering are often used as preliminary steps for many complex machine learning tasks. The presence of noise and outliers can deteriorate the performance of such preprocessing and therefore impair the subsequent analysis tremendously. In manifold learning, several studies indicate solutions for removing background noise or noise close to the structure when the density is substantially higher than that exhibited by the noise. However, in many applications, including astronomical datasets, the density varies alongside manifolds that are buried in a noisy background. We propose a novel method to extract manifolds in the presence of noise based on the idea of Ant colony optimization. In contrast to the existing random walk solutions, our technique captures points which are locally aligned with major directions of the manifold. Moreover, we empirically show that the biologically inspired formulation of ant pheromone reinforces this behavior enabling it to recover multiple manifolds embedded in extremely noisy data clouds. The algorithm's performance is demonstrated in comparison to the state-of-the-art approaches, such as Markov Chain, LLPD, and Disperse, on several synthetic and real astronomical datasets stemming from an N-body simulation of a cosmological volume.

Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to the exponential growth of output space. MLC encourages a popular framework named label compression (LC) for capturing label dependency with dimension reduction. Nevertheless, most existing LC methods failed to consider the influence of the feature space or misguided by original problematic features, so that may result in performance degeneration. In this paper, we present a compact learning (CL) framework to embed the features and labels simultaneously and with mutual guidance. The proposal is a versatile concept, hence the embedding way is arbitrary and independent of the subsequent learning process. Following its spirit, a simple yet effective implementation called compact multi-label learning (CMLL) is proposed to learn a compact low-dimensional representation for both spaces. CMLL maximizes the dependence between the embedded spaces of the labels and features, and minimizes the loss of label space recovery concurrently. Theoretically, we provide a general analysis for different embedding methods. Practically, we conduct extensive experiments to validate the effectiveness of the proposed method.

An ensemble method should cleverly combine a group of base classifiers to yield an improved classifier. The majority vote is an example of a methodology used to combine classifiers in an ensemble method. In this paper, we propose to combine classifiers using an associative memory model. Precisely, we introduce ensemble methods based on recurrent correlation associative memories (RCAMs) for binary classification problems. We show that an RCAM-based ensemble classifier can be viewed as a majority vote classifier whose weights depend on the similarity between the base classifiers and the resulting ensemble method. More precisely, the RCAM-based ensemble combines the classifiers using a recurrent consult and vote scheme. Furthermore, computational experiments confirm the potential application of the RCAM-based ensemble method for binary classification problems.

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually surprising edges. In this work, we propose MIDAS, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. We further propose MIDAS-F, to solve the problem by which anomalies are incorporated into the algorithm's internal states, creating a 'poisoning' effect which can allow future anomalies to slip through undetected. MIDAS-F introduces two modifications: 1) We modify the anomaly scoring function, aiming to reduce the 'poisoning' effect of newly arriving edges; 2) We introduce a conditional merge step, which updates the algorithm's data structures after each time tick, but only if the anomaly score is below a threshold value, also to reduce the `poisoning' effect. Experiments show that MIDAS-F has significantly higher accuracy than MIDAS. MIDAS has the following properties: (a) it detects microcluster anomalies while providing theoretical guarantees about its false positive probability; (b) it is online, thus processing each edge in constant time and constant memory, and also processes the data 130 to 929 times faster than state-of-the-art approaches; (c) it provides 41% to 55% higher accuracy (in terms of ROC-AUC) than state-of-the-art approaches.

In this work, we present a framework for incorporating descriptive logical rules in state-of-the-art neural networks, enabling them to learn how to handle unseen labels without the introduction of any new training data. The rules are integrated into existing networks without modifying their architecture, through an additional term in the network's loss function that penalizes states of the network that do not obey the designed rules. As a case of study, the framework is applied to an existing neural-based Dialog State Tracker. Our experiments demonstrate that the inclusion of logical rules allows the prediction of unseen labels, without deteriorating the predictive capacity of the original system.

Toronto, Ontario--(Newsfile Corp. - September 16, 2020) - GoldSpot Discoveries Corp. (TSXV: SPOT) (the "Company" or "GoldSpot") has been engaged by Cerrado Gold Inc. ("Cerrado") to apply machine learning and its proprietary data science expertise to identify new exploration targets on Cerrado's Minera Don Nicolas (MDN) project, located in Santa Cruz, Argentina. In its analysis, GoldSpot will work with Cerrado's technical team to integrate and analyze geological and remote sensing data available in the area. The process will explore the potential for gold mineralization within the MDN properties, to produce GoldSpot Smart Targets which fuse geoscience knowledge with data science insights. "Minera Don Nicolas is in the mineral and data rich Deseado Massif, an area where GoldSpot is having significant success, particularly at Yamana Gold's Cerro Moro project. MDN has robust property-wide datasets and we look forward to supporting Cerrado's technical team and advancing exploration efforts. The project has significant potential with a land package of more than 273,000 hectares," stated Denis Laviolette, Executive Chairman and President of GoldSpot Discoveries.

Artificial intelligence offers a chance for the Latin America's economies to leapfrog to greater innovation and economic progress. E-commerce firms have faced a conundrum in Latin America: How can they deliver packages in a region where 25% of urban populations live in informal, squatter neighborhoods with no addresses? Enter Chazki, a logistics startup from Peru, which partnered with Arequipa's Universidad San Pablo to build an artificial intelligence robot to generate new postal maps across the country. The company has now expanded to Argentina, Mexico and Chile, introducing remote communities and city outskirts to online deliveries. That's just one example of how machine learning is bringing unique Latin American solutions to unique Latin American challenges.

Thanks to Covid-19, the mantra for 2020 has got to be "quarantine and chill." Good thing Netflix is here to "entertain people all over the world," as the company's cofounder Reed Hastings explained at this year's WIRED25. Sating the global entertainment palate, though, requires an undying spirit of invention as well as narratives that span both the US and abroad. Netflix's secret, according to Hasting's new book No Rules Rules, is that it values its workers over its work process. It's this employee-centric attitude that allows a startup to maintain a culture of innovation as it grows from, say, a 30-person rent-by-mail DVD provider into the world's largest streaming service, with a film production arm that rivals Hollywood's Big Six.