In the last years, crowdsourcing is transforming the way classification training sets are obtained. Instead of relying on a single expert annotator, crowdsourcing shares the labelling effort among a large number of collaborators. For instance, this is being applied to the data acquired by the laureate Laser Interferometer Gravitational Waves Observatory (LIGO), in order to detect glitches which might hinder the identification of true gravitational-waves. The crowdsourcing scenario poses new challenging difficulties, as it deals with different opinions from a heterogeneous group of annotators with unknown degrees of expertise. Probabilistic methods, such as Gaussian Processes (GP), have proven successful in modeling this setting. However, GPs do not scale well to large data sets, which hampers their broad adoption in real practice (in particular at LIGO). This has led to the recent introduction of deep learning based crowdsourcing methods, which have become the state-of-the-art. However, the accurate uncertainty quantification of GPs has been partially sacrificed. This is an important aspect for astrophysicists in LIGO, since a glitch detection system should provide very accurate probability distributions of its predictions. In this work, we leverage the most popular sparse GP approximation to develop a novel GP based crowdsourcing method that factorizes into mini-batches. This makes it able to cope with previously-prohibitive data sets. The approach, which we refer to as Scalable Variational Gaussian Processes for Crowdsourcing (SVGPCR), brings back GP-based methods to the state-of-the-art, and excels at uncertainty quantification. SVGPCR is shown to outperform deep learning based methods and previous probabilistic approaches when applied to the LIGO data. Moreover, its behavior and main properties are carefully analyzed in a controlled experiment based on the MNIST data set.

Using deep neural networks for identifying physics objects at the Large Hadron Collider (LHC) has become a powerful alternative approach in recent years. After successful training of deep neural networks, examining the trained networks not only helps us understand the behaviour of neural networks, but also helps improve the performance of deep learning models through proper interpretation. We take jet tagging problem at the LHC as an example, using recursive neural networks as a starting point, aim at a thorough understanding of the behaviour of the physics-oriented DNNs and the information encoded in the embedding space. We make a comparative study on a series of different jet tagging tasks dominated by different underlying physics. Interesting observations on the latent space are obtained.

In response to growing concerns about user privacy, federated learning has emerged as a promising tool to train statistical models over networks of devices while keeping data localized. Federated learning methods run training tasks directly on user devices and do not share the raw user data with third parties. However, current methods still share model updates, which may contain private information (e.g., one's weight and height), during the training process. Existing efforts that aim to improve the privacy of federated learning make compromises in one or more of the following key areas: performance (particularly communication cost), accuracy, or privacy. To better optimize these trade-offs, we propose that \textit{sketching algorithms} have a unique advantage in that they can provide both privacy and performance benefits while maintaining accuracy. We evaluate the feasibility of sketching-based federated learning with a prototype on three representative learning models. Our initial findings show that it is possible to provide strong privacy guarantees for federated learning without sacrificing performance or accuracy. Our work highlights that there exists a fundamental connection between privacy and communication in distributed settings, and suggests important open problems surrounding the theoretical understanding, methodology, and system design of practical, private federated learning.

Even though the topic of explainable AI/ML is very popular in text and computer vision domain, most of the previous literatures are not suitable for explaining black-box models' predictions on general data mining datasets. This is because these datasets are usually in high-dimensional vectored features format that are not as friendly and comprehensible as texts and images to the end users. In this paper, we combine the best of both worlds: "explanations by intervention" from causality and "explanations are contrastive" from philosophy and social science domain to explain neural models' predictions for tabular datasets. Specifically, given a model's prediction as label X, we propose a novel idea to intervene and generate minimally modified contrastive sample to be classified as Y, that then results in a simple natural text giving answer to the question "Why X rather than Y?". We carry out experiments with several datasets of different scales and compare our approach with other baselines on three different areas: fidelity, reasonableness and explainability.

Slot Filling is the task of extracting the semantic concept from a given natural language utterance. Recently it has been shown that using contextual information, either in work representations (e.g., BERT embedding) or in the computation graph of the model, could improve the performance of the model. However, recent work uses the contextual information in a restricted manner, e.g., by concatenating the word representation and its context feature vector, limiting the model from learning any direct association between the context and the label of word. W e introduce a new deep model utilizing the contextual information for each work in the given sentence in a multi-task setting. Our model enforce consistency between the feature vectors of the context and the word while increasing the expressiveness of the context about the label of the word. Our empirical analysis on a slot filling dataset proves the superiority of the model over the baselines.

--Past research on recognizing human affect has made use of a variety of physiological sensors in many ways. Nonetheless, how affective dynamics are influenced in the context of human daily life has not yet been explored. In this work, we present a wearable affective life-log system (ALIS), that is robust as well as easy to use in daily life to detect emotional changes and determine their cause-and-effect relationship on users' lives. The proposed system records how a user feels in certain situations during long-term activities with physiological sensors. Based on the long-term monitoring, the system analyzes how the contexts of the user's life affect his/her emotion changes. Furthermore, real-world experimental results demonstrate that the proposed wearable life-log system enables us to build causal structures to find effective stress relievers suited to every stressful situation in school life. For instance, today's coffee is not always the same as yesterday's coffee. The cup of coffee we drank today may not be as enjoyable as the cup of coffee we drank yesterday. While drinking coffee generally helps to reduce a person's stress, the stress-relieving effects of coffee may vary from day to day for many reasons. For a person who likes calm and quiet surronding, a cup of coffee drunk today in a crowded coffee shop with distracting background noise is likely to be less enjoyable than a cup of coffee drunk yesterday in the quiet kitchen of one's own home. This instance shows that a person can have different emotional responses to the same life events in different circumstances. Why and how does a person experience various emotions from a single event under different situations? Answering this question could improve human life in a variety of ways, as by improving physical health. People who suffer from depression are more vulnerable to heart disease than people with no history of depression. Therefore, discovering life elements related to depression and offering guidance to avoid such elements can help sufferers to lessen their suffering and lead a meaningful life. In response to this question, recent researches on recognizing human affect has made use of a variety of physiological sensors in many ways. B. Kim and S. Jo are with the School of Computing, KAIST, Republic of Korea. S. Jo is the corresponding author.

Google announced updates to its Cloud AI Platform that enhance training and prediction capabilities of machine learning and deep learning models. Google Cloud AI Platform is an end-to-end machine learning platform as a service (ML PaaS) targeting data scientists, ML developers, and AI engineers. The Cloud AI Platform has services to tackle the lifecycle of machine learning models. From data preparation to training to model serving, the platform has all the essential building blocks to develop and deploy sophisticated machine learning models. The most recent updates make training and deploying ML models on Google Cloud Platform flexible and powerful.

More and more industries and organizations are leveraging artificial intelligence to delight customers and cut through the competition. However, development and deployment of deep learning models is time-consuming and costly – often prohibitively costly. That's when automated machine learning (AutoML) comes into play. AutoML solutions can significantly increase the efficiency of ML model development. Even more importantly, they lower the entry barriers for leveraging AI in business settings by allowing people without IT backgrounds to utilize the most advanced ML algorithms.

Together Duke is pleased to announce the Natural Language Processing Winter School (NLP-WS), being offered in January 2020, as a three-day class that provides lectures on the fundamentals of machine learning, and natural language processing. Machine learning is a field characterized by development of algorithms that are implemented in software and run on a machine (e.g., computer, mobile device, etc.). Each such algorithm is characterized by a set of parameters, and particular parameter settings yield associated algorithm characteristics. The algorithms have the capacity to learn, based on observed data. By "learn" it is meant that the algorithm can rigorously quantify which parameter settings are best matched to the data of interest.

Researchers have developed algorithms that work with wearable sensors to continuously monitor tremor, and estimate total tremor, in Parkinson's patients as they go about their daily routines. Analyses of sensor results using one algorithm, in particular, were similar to an established test assessing tremor without being dependent on the time the test is given. The study, "Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements," was published in Sensors. Resting tremor, or the rhythmic shaking of muscles while relaxed, is among the motor symptoms of Parkinson's disease (PD), and some patients also have active tremor, or shaking while engaged in voluntary muscle movement. Others motor symptoms are slowness of movement (bradykinesia), rigidity, and problems with posture, balance, and gait.