Machine learning (ML) continues to permeate all layers of academia, industry and society. Despite its successes, mental frameworks to capture and represent machine learning workflows in a consistent and coherent manner are lacking. For instance, the de facto process modeling standard, Business Process Model and Notation (BPMN), managed by the Object Management Group, is widely accepted and applied. However, it is short of specific support to represent machine learning workflows. Further, the number of heterogeneous tools for deployment of machine learning solutions can easily overwhelm practitioners. Research is needed to align the process from modeling to deploying ML workflows. We analyze requirements for standard based conceptual modeling for machine learning workflows and their serverless deployment. Confronting the shortcomings with respect to consistent and coherent modeling of ML workflows in a technology independent and interoperable manner, we extend BPMN's Meta-Object Facility (MOF) metamodel and the corresponding notation and introduce BPMN4sML (BPMN for serverless machine learning). Our extension BPMN4sML follows the same outline referenced by the Object Management Group (OMG) for BPMN. We further address the heterogeneity in deployment by proposing a conceptual mapping to convert BPMN4sML models to corresponding deployment models using TOSCA. BPMN4sML allows technology-independent and interoperable modeling of machine learning workflows of various granularity and complexity across the entire machine learning lifecycle. It aids in arriving at a shared and standardized language to communicate ML solutions. Moreover, it takes the first steps toward enabling conversion of ML workflow model diagrams to corresponding deployment models for serverless deployment via TOSCA.

We propose an approach to solving partial differential equations (PDEs) using a set of neural networks which we call Neural Basis Functions (NBF). This NBF framework is a novel variation of the POD DeepONet operator learning approach where we regress a set of neural networks onto a reduced order Proper Orthogonal Decomposition (POD) basis. These networks are then used in combination with a branch network that ingests the parameters of the prescribed PDE to compute a reduced order approximation to the PDE. This approach is applied to the steady state Euler equations for high speed flow conditions (mach 10-30) where we consider the 2D flow around a cylinder which develops a shock condition. We then use the NBF predictions as initial conditions to a high fidelity Computational Fluid Dynamics (CFD) solver (CFD++) to show faster convergence. Lessons learned for training and implementing this algorithm will be presented as well.

Native language identification (NLI) is the task of training (via supervised machine learning) a classifier that guesses the native language of the author of a text. This task has been extensively researched in the last decade, and the performance of NLI systems has steadily improved over the years. We focus on a different facet of the NLI task, i.e., that of analysing the internals of an NLI classifier trained by an \emph{explainable} machine learning algorithm, in order to obtain explanations of its classification decisions, with the ultimate goal of gaining insight into which linguistic phenomena ``give a speaker's native language away''. We use this perspective in order to tackle both NLI and a (much less researched) companion task, i.e., guessing whether a text has been written by a native or a non-native speaker. Using three datasets of different provenance (two datasets of English learners' essays and a dataset of social media posts), we investigate which kind of linguistic traits (lexical, morphological, syntactic, and statistical) are most effective for solving our two tasks, namely, are most indicative of a speaker's L1. We also present two case studies, one on Spanish and one on Italian learners of English, in which we analyse individual linguistic traits that the classifiers have singled out as most important for spotting these L1s. Overall, our study shows that the use of explainable machine learning can be a valuable tool for th

This work addresses meta-learning (ML) by considering deep networks with stochastic local winner-takes-all (LWTA) activations. This type of network units results in sparse representations from each model layer, as the units are organized into blocks where only one unit generates a non-zero output. The main operating principle of the introduced units rely on stochastic principles, as the network performs posterior sampling over competing units to select the winner. Therefore, the proposed networks are explicitly designed to extract input data representations of sparse stochastic nature, as opposed to the currently standard deterministic representation paradigm. Our approach produces state-of-the-art predictive accuracy on few-shot image classification and regression experiments, as well as reduced predictive error on an active learning setting; these improvements come with an immensely reduced computational cost.

We introduce an audiovisual method for long-range text-to-video retrieval. Unlike previous approaches designed for short video retrieval (e.g., 5-15 seconds in duration), our approach aims to retrieve minute-long videos that capture complex human actions. One challenge of standard video-only approaches is the large computational cost associated with processing hundreds of densely extracted frames from such long videos. To address this issue, we propose to replace parts of the video with compact audio cues that succinctly summarize dynamic audio events and are cheap to process. Our method, named ECLIPSE (Efficient CLIP with Sound Encoding), adapts the popular CLIP model to an audiovisual video setting, by adding a unified audiovisual transformer block that captures complementary cues from the video and audio streams. In addition to being 2.92x faster and 2.34x memory-efficient than long-range video-only approaches, our method also achieves better text-to-video retrieval accuracy on several diverse long-range video datasets such as ActivityNet, QVHighlights, YouCook2, DiDeMo and Charades.

We have now clearly understood that "Data is the new oil" and "AI is the new electricity". Such is the demand and expectation from AI – ML that the demand for machine learning technologists is increasing day by day across the world. This can be seen from the number of job postings for machine learning in leading job sites. A key aspect of AI – ML (Artificial Intelligence - Machine Learning) is that the technology is evolving at a fast pace and so the learners have to keep themselves abreast of the changing landscape. Employers expect a strong grounding in basics coupled with skills in latest advances in AI – ML from potential employees.

Machine Learning Center at Georgia Tech (ML@GT) and School of Computational Science and Engineering (CSE) Ph.D. student Xinshi Chen is being recognized for her work in machine learning with a prestigious fellowship. Chen specializes in principled machine learning research with a focus on learning-based algorithm design and deep learning structured data. Her work has garnered the attention of Google and recently received the 2020 Google Ph.D. Fellowship for outstanding graduate research in machine learning. One of her recent co-authored papers aims to create a system that can automatically learn an algorithm from data and apply the learned algorithm to solve new problems. The paper, developed with CSE Associate Professor Le Song, along with Yufei Zhang and Christoph Reisinger of the University of Oxford, will be presented at the Thirty-fourth Conference on Neural Information Processing Systems, which is scheduled for Dec. 6 through 12 Chen said, "Both algorithms and deep learning models aim to solve problems and make predictions for various tasks. Our project investigates the connection between traditional algorithms and deep learning models, and the strengths of these two can be combined to help each other."

In the last few years, machine learning (ML) has been making some giant leaps in education – from predicting the next steps students need to take to improve their grades to generating teacher study material. This article discusses how machine learning can be used for education in more detail and some of the current trends in this field. A machine learning branch of artificial intelligence employs algorithms to learn from data. It can improve the accuracy, speed, and efficiency of various tasks, such as predicting customer behavior or organizing data. In the education sector, machine learning can help teachers identify and diagnose problems with their student's academic progress and help them decide which courses to teach. Machine learning can also be used to develop educational programs that can adapt to the needs of individual students.

Health Education England's Annabelle Painter and Mike Nix on how different factors influence healthcare workers' confidence in AI, and what this means for AI design and deployment. Health Education England (HEE) and the NHS AI Lab have published a collaborative report exploring the factors that influence healthcare workers' confidence in artificial intelligence (AI) technologies. While the report covers AI technologies used for any task in healthcare settings, this article discusses the challenges of incorporating AI into clinical reasoning and decision making (CRDM). The key consideration is determining the appropriate level of confidence a clinician can place in AI-derived information for a case-specific clinical decision. What influences clinicians' confidence during AI-assisted CRDM?

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