This paper investigates the use of nonparametric kernel-regression to obtain a tasksimilarity aware meta-learning algorithm. Our hypothesis is that the use of tasksimilarity helps meta-learning when the available tasks are limited and may contain outlier/ dissimilar tasks. While existing meta-learning approaches implicitly assume the tasks as being similar, it is generally unclear how this task-similarity could be quantified and used in the learning. As a result, most popular metalearning approaches do not actively use the similarity/dissimilarity between the tasks, but rely on availability of huge number of tasks for their working. Our contribution is a novel framework for meta-learning that explicitly uses task-similarity in the form of kernels and an associated meta-learning algorithm. We model the task-specific parameters to belong to a reproducing kernel Hilbert space where the kernel function captures the similarity across tasks. The proposed algorithm iteratively learns a meta-parameter which is used to assign a task-specific descriptor for every task. The task descriptors are then used to quantify the task-similarity through the kernel function. We show how our approach conceptually generalizes the popular meta-learning approaches of model-agnostic meta-learning (MAML) and Meta-stochastic gradient descent (Meta-SGD) approaches. Numerical experiments with regression tasks show that our algorithm outperforms these approaches when the number of tasks is limited, even in the presence of outlier or dissimilar tasks. This supports our hypothesis that task-similarity helps improve the metalearning performance in task-limited and adverse settings.

Reinforcement learning (RL) [5] studies the problem of how to make sequential decisions to learn and act in unknown environments (which is usually modeled by a Markov Decision Process (MDP)) and maximize the collected rewards. There are mainly two types of algorithms to approach the RL problems: model-based algorithms and model-free algorithms. Model-based RL algorithms keep explicit description of the learned model and make decisions based on this model. In contrast, modelfree algorithms only maintain a group of value functions instead of the complete model of the system dynamics. Due to their space-and time-efficiency, model-free RL algorithms have been getting popular in a wide range of practical tasks (e.g., DQN [16], TRPO [18], and A3C [15]). In RL theory, model-free algorithms are explicitly defined to be the ones whose space complexity is always sublinear relative to the space required to store the MDP parameters [12]. For tabular MDPs (i.e., MDPs with finite number of states and actions, usually denoted by S and A respectively), this requires that the space complexity to be opS

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. An out-of-sample generalisation ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques.

This paper describes the NEMO submission to SIGTYP 2020 shared task which deals with prediction of linguistic typological features for multiple languages using the data derived from World Atlas of Language Structures (WALS). We employ frequentist inference to represent correlations between typological features and use this representation to train simple multi-class estimators that predict individual features. We describe two submitted ridge regression-based configurations which ranked second and third overall in the constrained task. Our best configuration achieved the micro-averaged accuracy score of 0.66 on 149 test languages.

PARTNER FEATURE: Intelligent connectivity enables transformational new capabilities in transport, entertainment, industry, and much more. For technical systems to digitally match human actions with connected environments, however, they must meet the speed of our natural reaction times. The networks used must be ultra-reliable, as many critical tasks will be executed remotely. And they will also rely on cost-effective edge infrastructure to enable scaling. According to GSMA, 5G could account for as many as 1.2 billion connections by 2025.

The Ample Market Research Added A new industry research report that focuses on Artificial Intelligence Market and delivers in-depth market analysis and future outlook of Artificial Intelligence market. The study covers significant data which makes the research report a handy resource for managers, analysts, industry experts, and other key people get ready-to-access and self-analyzed study along with graphs and tables to help understand market trends, drivers and market challenges. This is the latest report, covering the current COVID-19 impact on the market. The pandemic of Coronavirus (COVID-19) has affected every aspect of life globally. This has brought along several changes in market conditions.

Swedish tech giant Ericsson has announced that its Artificial Intelligence (AI) and Analytics Hub in Egypt shipped its first Cognitive Software to be used by Ericsson customers worldwide in the design and optimisation of their networks. Ericsson's research and development hub focuses on R&D in the fields of AI and Automation, leveraging cutting-edge technologies to create data-driven, intelligent and robust systems for automation, evolution and growth. "We are excited to reach this milestone and deliver a top-notch technological innovation through our local talent in Egypt. Our AI & Analytics Hub both leverages and collaborates with our customers in order to collectively drive industrialization of new opportunities while enabling access to world-class talent from which we can recruit," said Eva Andren, vice president and head of managed services at Ericsson Middle East and Africa. The hub showcases Ericsson's commitment towards the local Egyptian market and aims to develop local talent in advanced technology areas of Artificial Intelligence and software.

Artificial intelligence promises to expertly diagnose disease in medical images and scans. However, a close look at the data used to train algorithms for diagnosing eye conditions suggests these powerful new tools may perpetuate health inequalities. A team of researchers in the UK analyzed 94 datasets--with more than 500,000 images--commonly used to train AI algorithms to spot eye diseases. They found that almost all of the data came from patients in North America, Europe, and China. Just four datasets came from South Asia, two from South America, and one from Africa; none came from Oceania.

Accurate flood detection in near real time via high resolution, high latency satellite imagery is essential to prevent loss of lives by providing quick and actionable information. Instruments and sensors useful for flood detection are only available in low resolution, low latency satellites with region re-visit periods of up to 16 days, making flood alerting systems that use such satellites unreliable. This work presents H2O-Network, a self supervised deep learning method to segment floods from satellites and aerial imagery by bridging domain gap between low and high latency satellite and coarse-to-fine label refinement. H2O-Net learns to synthesize signals highly correlative with water presence as a domain adaptation step for semantic segmentation in high resolution satellite imagery. Our work also proposes a self-supervision mechanism, which does not require any hand annotation, used during training to generate high quality ground truth data. We demonstrate that H2O-Net outperforms the state-of-the-art semantic segmentation methods on satellite imagery by 10% and 12% pixel accuracy and mIoU respectively for the task of flood segmentation. We emphasize the generalizability of our model by transferring model weights trained on satellite imagery to drone imagery, a highly different sensor and domain.

Modern machine learning methods are often overparametrized, allowing adaptation to the data at a fine level. This can seem puzzling; in the worst case, such models do not need to generalize. This puzzle inspired a great amount of work, arguing when overparametrization reduces test error, in a phenomenon called "double descent". Recent work aimed to understand in greater depth why overparametrization is helpful for generalization. This leads to discovering the unimodality of variance as a function of the level of parametrization, and to decomposing the variance into that arising from label noise, initialization, and randomness in the training data to understand the sources of the error. In this work we develop a deeper understanding of this area. Specifically, we propose using the analysis of variance (ANOVA) to decompose the variance in the test error in a symmetric way, for studying the generalization performance of certain two-layer linear and non-linear networks. The advantage of the analysis of variance is that it reveals the effects of initialization, label noise, and training data more clearly than prior approaches. Moreover, we also study the monotonicity and unimodality of the variance components. While prior work studied the unimodality of the overall variance, we study the properties of each term in variance decomposition. One key insight is that in typical settings, the interaction between training samples and initialization can dominate the variance; surprisingly being larger than their marginal effect. Also, we characterize "phase transitions" where the variance changes from unimodal to monotone. On a technical level, we leverage advanced deterministic equivalent techniques for Haar random matrices, that---to our knowledge---have not yet been used in the area. We also verify our results in numerical simulations and on empirical data examples.