These categorization systems go deeper than the broader asset class based classification (equity, fixed income, etc) and provide Categorization of mutual funds or Exchange-Traded-funds (ETFs) further granular categories based on the portfolio breakdown. They have long served the financial analysts to perform peer analysis have been used to identify the top performing as well as worst for various purposes starting from competitor analysis, to quantifying performing funds within their peer groups, called peer analysis portfolio diversification. The categorization methodology of funds; to identify a home-grown fund to recommend against a usually relies on fund composition data in the structured format competitor's fund; to explain similarities and advantages of homegrown extracted from the Form N-1A. Here, we initiate a study to learn products compared to competitors' products for marketing the categorization system directly from the unstructured data as purposes; to quantify portfolio diversification of a given fund of depicted in the forms using natural language processing (NLP).

Inflammatory bowel disease (IBD), in particular ulcerative colitis (UC), is graded by endoscopists and this assessment is the basis for risk stratification and therapy monitoring. Presently, endoscopic characterisation is largely operator dependant leading to sometimes undesirable clinical outcomes for patients with IBD. We focus on the Mayo Endoscopic Scoring (MES) system which is widely used but requires the reliable identification of subtle changes in mucosal inflammation. Most existing deep learning classification methods cannot detect these fine-grained changes which make UC grading such a challenging task. In this work, we introduce a novel patch-level instance-group discrimination with pretext-invariant representation learning (PLD-PIRL) for self-supervised learning (SSL). Our experiments demonstrate both improved accuracy and robustness compared to the baseline supervised network and several state-of-the-art SSL methods. Compared to the baseline (ResNet50) supervised classification our proposed PLD-PIRL obtained an improvement of 4.75% on hold-out test data and 6.64% on unseen center test data for top-1 accuracy.

The ability to collect and process large amounts of data represents additional value for many companies in today's market. The world of sports has been no exception, starting with baseball with the emergence of SABRmetrics in the 1980s, through motor racing to sports such as basketball and soccer more recently. The creation of models and metrics through artificial intelligence allows sports fans to analyze the game from another perspective and, for their professionals, to gain a competitive advantage over their rivals. In the case of soccer, probably the most popular metric is the one known as expected goal (xG). The xG is intended to measure the probability that a shot will result in a goal, taking into account variables such as the position of the shot, the position of the goalkeeper or the part of the body with which the shot is taken. As it is a probability, it should take values between 0 and 1, so that for the clearest opportunities (for example, a shot inside the small area without a goalkeeper) it takes values close to 1, and for shots further away or with greater difficulty it tends to 0. This metric is very useful for coaching staffs and scouting teams to evaluate the finishing or chance-creating ability of different players.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. This article is the third part of the AI Ethics for Data Science essential series.

Active speaker detection (ASD) systems are important modules for analyzing multi-talker conversations. They aim to detect which speakers or none are talking in a visual scene at any given time. Existing research on ASD does not agree on the definition of active speakers. We clarify the definition in this work and require synchronization between the audio and visual speaking activities. This clarification of definition is motivated by our extensive experiments, through which we discover that existing ASD methods fail in modeling the audio-visual synchronization and often classify unsynchronized videos as active speaking. To address this problem, we propose a cross-modal contrastive learning strategy and apply positional encoding in attention modules for supervised ASD models to leverage the synchronization cue. Experimental results suggest that our model can successfully detect unsynchronized speaking as not speaking, addressing the limitation of current models.

In this work we use Equal Oppportunity (EO) doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness. We contrast formal EO approaches that narrowly focus on fair contests at discrete decision points, with substantive EO doctrines that look at people's fair life chances more holistically over the course of a lifetime. We use this taxonomy to provide a moral interpretation of the impossibility results as the incompatibility between different conceptions of a fair contest -- foward-facing versus backward-facing -- when people do not have fair life chances. We use this result to motivate substantive conceptions of algorithmic fairness and outline two plausible fair decision procedures based on the luck-egalitarian doctrine of EO, and Rawls's principle of fair equality of opportunity. Equality of Opportunity (EO) is a philosophical doctrine that objects to morally arbitrary and irrelevant factors affecting people's access to desirable positions, and the social goods attached to them (such as opportunity and wealth). In an EO-respecting society, all people, irrespective of their morally arbitrary characteristics, such as socio-economic background, gender, race, or disability status, have comparable access to the opportunities that they desire. Similarly, in fair machine learning (fair-ML), we are usually interested in ensuring that the outputs of algorithmic systems, specially those used in critical social contexts, do not systematically skew along the lines of membership in protected groups based on gender, race, or disability. In so far as protected groups are constructed on the basis of morally arbitrary factors, the moral desiderata of EO doctrines from political philosophy align exactly with the fairness-related concerns in machine learning. In this work, we employ ideas from the rich literature on Equality of Opportunity from political philosophy [1-11] to clarify the normative foundations of fairness and justice-related interventions, and gauge the efficacy of current algorithmic approaches that attempt to codify these criteria. There are two broad principles of EO, namely, the principle of fair contests and the principle of fair life chances. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. The principle of fair contests, commonly understood as the nondiscrimination principle, says that competitions for desirable positions should be open to all and should be adjudicated based on competitors' relevant merits, or qualifications.

Both in the domains of Feature Selection and Interpretable AI, there exists a desire to `rank' features based on their importance. Such feature importance rankings can then be used to either: (1) reduce the dataset size or (2) interpret the Machine Learning model. In the literature, however, such Feature Rankers are not evaluated in a systematic, consistent way. Many papers have a different way of arguing which feature importance ranker works best. This paper fills this gap, by proposing a new evaluation methodology. By making use of synthetic datasets, feature importance scores can be known beforehand, allowing more systematic evaluation. To facilitate large-scale experimentation using the new methodology, a benchmarking framework was built in Python, called fseval. The framework allows running experiments in parallel and distributed over machines on HPC systems. By integrating with an online platform called Weights and Biases, charts can be interactively explored on a live dashboard. The software was released as open-source software, and is published as a package on the PyPi platform. The research concludes by exploring one such large-scale experiment, to find the strengths and weaknesses of the participating algorithms, on many fronts.

How to sample high quality negative instances from unlabeled data, i.e., negative sampling, is important for training implicit collaborative filtering and contrastive learning models. Although previous studies have proposed some approaches to sample informative instances, few has been done to discriminating false negative from true negative for unbiased negative sampling. On the basis of our order relation analysis of negatives' scores, we first derive the class conditional density of true negatives and that of false negatives. We next design a Bayesian classifier for negative classification, from which we define a model-agnostic posterior probability estimate of an instance being true negative as a quantitative negative signal measure. We also propose a Bayesian optimal sampling rule to sample high-quality negatives. The proposed Bayesian Negative Sampling (BNS) algorithm has a linear time complexity. Experimental studies validate the superiority of BNS over the peers in terms of better sampling quality and better recommendation performance.

This article was published as a part of the Data Science Blogathon. Now, we enter the secret sauce: CM_Norm adjusts the colour-bar, such that its point of origin is equal to the accuracy expected for a random prediction. Essentially, the "naive-prediction accuracy" is our "point of origin" because a model which predicts worse than a coin-flip, is not a helpful model to begin with (hence the name: "coin-flip confusion-matrix"). In other words, we are interested in a models "excess performance", rather than its "absolute" error rates. To give two examples: For 3 different classes, the "point of origin", of the colour-bar, would be set at 1/3, or for 10 classes it would be set at 1/10.

Unlike linear models and SVM (see Part 1), some machine learning models are really complex to learn from their mathematical formulation. Fortunately, they can be understood by following a step-by-step process they execute on a small dummy dataset. This way, you can uncover machine learning models under the hood without the "math bottleneck". You will learn three more models in this story after Part 1: K-Nearest Neighbors (KNN), Naive Bayes, and Decision Tree. KNN is a non-generalizing machine learning model since it simply "remembers" all of its train data.