In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is time-consuming and may delay the condition. With the development of deep learning, some researches on ophthalmic diseases have achieved good results, however, most of them are just based on one disease. During fundus screening, ophthalmologists usually give diagnoses of multi-disease on binocular fundus image, so we release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients. We did some benchmark experiments on it through some state-of-the-art deep neural networks. We found simply increasing the scale of network cannot bring good results for multi-disease classification, and a well-structured feature fusion method combines characteristics of multi-disease is needed. Through this work, we hope to advance the research of related fields.

Title to be confirmed Speaker: Fabio Petroni Organised by: Stanford MLSys Join the email list to find out how to register for each seminar. Title to be confirmed Speaker: Chad Jenkins (University of Michigan) Organised by: Robotics Today Watch the seminar here. Title to be confirmed Speaker: Samory K. Kpotufe Organised by: London School of Economics and Political Science Register here.

Today, the use of social networking data has attracted a lot of academic and commercial attention in predicting the stock market. In most studies in this area, the sentiment analysis of the content of user posts on social networks is used to predict market fluctuations. Predicting stock marketing is challenging because of the variables involved. In the short run, the market behaves like a voting machine, but in the long run, it acts like a weighing machine. The purpose of this study is to predict EUR/USD stock behavior using Capsule Network on finance texts and Candlestick images. One of the most important features of Capsule Network is the maintenance of features in a vector, which also takes into account the space between features. The proposed model, TI-Capsule (Text and Image information based Capsule Neural Network), is trained with both the text and image information simultaneously. Extensive experiments carried on the collected dataset have demonstrated the effectiveness of TI-Capsule in solving the stock exchange prediction problem with 91% accuracy.

Bayesian Neural Networks (BNNs) [37, 47] offer a probabilistic interpretation for deep learning models by imposing a prior distribution on the weight parameters and aim to obtain a posterior distribution instead of only point estimates. By marginalizing over this posterior for prediction, BNNs perform a procedure of ensemble learning. These principles facilitate the model to improve generalization, robustness and allow for uncertainty quantification. However, computing exactly the posterior of non-linear Bayesian networks is infeasible and approximate inference has been devised. The core challenge is how to construct an expressive approximation for the true posterior while maintaining computational efficiency and scalability, especially for modern deep learning architectures. Variational inference is a popular deterministic approximation approach to to deal with this challenge. The first practical methods are proposed in [15, 5, 28], in which, the approximate posterior is assumed to be a fully factorized distribution, also called mean-field variational inference. Generally, the mean-field approximation family encourages some advantages in inference including computational tractability and effective optimization with the stochastic gradient-based methods. However, it will ignore strong statistical dependencies among random weights of the neural networks, which leads to an inability to capture the complicated structure of the true posterior and to estimate true model uncertainty.

The task of learning to rank has been widely studied by the machine learning community, mainly due to its use and great importance in information retrieval, data mining, and natural language processing. Therefore, ranking accurately and learning to rank are crucial tasks. Context-Based Information Retrieval systems have been of great importance to reduce the effort of finding relevant data. Such systems have evolved by using machine learning techniques to improve their results, but they are mainly dependent on user feedback. Although information retrieval has been addressed in different works along with classifiers based on Optimum-Path Forest (OPF), these have so far not been applied to the learning to rank task. Therefore, the main contribution of this work is to evaluate classifiers based on Optimum-Path Forest, in such a context. Experiments were performed considering the image retrieval and ranking scenarios, and the performance of OPF-based approaches was compared to the well-known SVM-Rank pairwise technique and a baseline based on distance calculation. The experiments showed competitive results concerning precision and outperformed traditional techniques in terms of computational load.

Embodied agents is a term used to denote intelligent agents, which are a component of devices belonging to the Internet of Things (IoT) domain. Each agent is provided with sensors and actuators to interact with the environment, and with a 'controller' that usually contains an artificial neural network (ANN). In previous publications, we introduced three software approaches to design, implement and test IoT embodied agents. In this paper, we propose a reference model based on statecharts that offers abstractions tailored to the development of IoT applications. The model represents embodied agents that are controlled by neural networks. Our model includes the ANN training process, represented as a reconfiguration step such as changing agent features or neural net connections. Our contributions include the identification of the main characteristics of IoT embodied agents, a reference model specification based on statecharts, and an illustrative application of the model to support autonomous street lights. The proposal aims to support the design and implementation of IoT applications by providing high-level design abstractions and models, thus enabling the designer to have a uniform approach to conceiving, designing and explaining such applications.

A significant amount of research has been devoted to automatic personalization in digital applications, especially in Internet applications Computer games represent an ideal research domain for the next [8]. As the content of the Internet services grows, personalized generation of personalized digital applications. This paper presents applications such as recommendation systems help to mitigate information a player-centered framework of AI for game personalization, complementary overload and decision fatigue [8]. This body of work to the commonly used system-centered approaches.

In this paper, we introduce a Model-based Algorithm Tuning Engine, namely MATE, where the parameters of an algorithm are represented as expressions of the features of a target optimisation problem. In contrast to most static (feature-independent) algorithm tuning engines such as irace and SPOT, our approach aims to derive the best parameter configuration of a given algorithm for a specific problem, exploiting the relationships between the algorithm parameters and the features of the problem. We formulate the problem of finding the relationships between the parameters and the problem features as a symbolic regression problem and we use genetic programming to extract these expressions in a human-readable form. For the evaluation, we apply our approach to the configuration of the (1 1) EA and RLS algorithms for the One-Max, LeadingOnes, BinValue and Jump optimisation problems, where the theoretically optimal algorithm parameters to the problems are available as functions of the features of the problems. Our study shows that the found relationships typically comply with known theoretical results - this demonstrates (1) the potential of model-based parameter tuning as an alternative to existing static algorithm tuning engines, and (2) its potential to discover relationships between algorithm performance and instance features in human-readable form.

A viral video shows a young woman conducting an exercise class on a roundabout in the Burmese capital, Nyapyidaw. Behind her a military convoy approaches a checkpoint to go conduct arrests at the Parliament building. Has she inadvertently filmed a coup? The video later became a viral meme, but for the first days, online amateur sleuths debated if it was green-screened or otherwise manipulated, often using the jargon of verification and image forensics. Yet claims of audiovisual manipulation are increasingly being used to make people wonder if what is real is a fake.

The arrival of the New Year brings us to think in many areas. What does 2021 hold in store for artificial intelligence? Here are 10 Artificial Intelligence predictions, from academic research to capital markets to regulation. We will take stock in December 2021 to assess the results. Autonomous vehicle developers like Waymo and Cruise have ongoing and massive cash flow needs.