Machine Learning is a branch of Artificial Intelligence(AI) that is used to predict outcomes of an application without explicitly being programmed to do so. Supervised Learning: It is a type of Machine Learning where the machine is trained with well labeled data. Thus the model is able to predict the price on this well labeled dataset. Unsupervised Learning: It is a type of Machine Learning where the machine is trained to identify patterns and predict outcomes with unlabeled data. Example: If a machine is given a dataset containing the pictures of dolphins and whales (considering the machine has never seen any pictures of dolphins and whales).

While recent work on automated fact-checking has focused mainly on verifying and explaining claims, for which the list of claims is readily available, identifying check-worthy claim sentences from a text remains challenging. Current claim identification models rely on manual annotations for each sentence in the text, which is an expensive task and challenging to conduct on a frequent basis across multiple domains. This paper explores methodology to identify check-worthy claim sentences from fake news articles, irrespective of domain, without explicit sentence-level annotations. We leverage two internal supervisory signals - headline and the abstractive summary - to rank the sentences based on semantic similarity. We hypothesize that this ranking directly correlates to the check-worthiness of the sentences. To assess the effectiveness of this hypothesis, we build pipelines that leverage the ranking of sentences based on either the headline or the abstractive summary. The top-ranked sentences are used for the downstream fact-checking tasks of evidence retrieval and the article's veracity prediction by the pipeline. Our findings suggest that the top 3 ranked sentences contain enough information for evidence-based fact-checking of a fake news article. We also show that while the headline has more gisting similarity with how a fact-checking website writes a claim, the summary-based pipeline is the most promising for an end-to-end fact-checking system.

After having made a post asking for topics to be explored on a group on Facebook about artificial intelligence and deep learning one of the most interesting ones that emerged was training data. "Can you try to write an article on importance of Training data and how to obtain them? For some models Training data is really hard to get like for custom speech to text, it's hard for someone to get those many hours of speech data." I have written about data in various articles, however I have not written about test data yet specifically so I will do my best. First to give an explanation of what test data is in the context of artificial intelligence particularly machine learning techniques and thereafter go into a few thoughts on how to tackle such a problem.

Supervised learning is the common most type of machine learning. Most ML problems that we encounter fall into this category. As the name implies, a supervised learning algorithm is trained with input data along with some form of guidance that we can call labels. Labels are also known as targets and they act as a description of the input data. With that said, there are other advanced tasks that don't directly fall into supervised learning, but they actually are.

Supervised learning involves learning a function that maps an input to an output based on example input-output pairs. Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes. In classification models, the output is discrete. Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes. Clustering is an unsupervised technique that involves the grouping, or clustering, of data points.

The aim of this paper is to formalize a new continual semi-supervised learning (CSSL) paradigm, proposed to the attention of the machine learning community via the IJCAI 2021 International Workshop on Continual Semi-Supervised Learning (CSSL-IJCAI), with the aim of raising field awareness about this problem and mobilizing its effort in this direction. After a formal definition of continual semi-supervised learning and the appropriate training and testing protocols, the paper introduces two new benchmarks specifically designed to assess CSSL on two important computer vision tasks: activity recognition and crowd counting. We describe the Continual Activity Recognition (CAR) and Continual Crowd Counting (CCC) challenges built upon those benchmarks, the baseline models proposed for the challenges, and describe a simple CSSL baseline which consists in applying batch self-training in temporal sessions, for a limited number of rounds. The results show that learning from unlabelled data streams is extremely challenging, and stimulate the search for methods that can encode the dynamics of the data stream.

Generative adversarial networks (GANs) have shown tremendous promise in learning to generate data and effective at aiding semi-supervised classification. However, to this point, semi-supervised GAN methods make the assumption that the unlabeled data set contains only samples of the joint distribution of the classes of interest, referred to as inliers. Consequently, when presented with a sample from other distributions, referred to as outliers, GANs perform poorly at determining that it is not qualified to make a decision on the sample. The problem of discriminating outliers from inliers while maintaining classification accuracy is referred to here as the DOIC problem. In this work, we describe an architecture that combines self-organizing maps (SOMs) with SS-GANS with the goal of mitigating the DOIC problem and experimental results indicating that the architecture achieves the goal. Multiple experiments were conducted on hyperspectral image data sets. The SS-GANS performed slightly better than supervised GANS on classification problems with and without the SOM. Incorporating the SOMs into the SS-GANs and the supervised GANS led to substantially mitigation of the DOIC problem when compared to SS-GANS and GANs without the SOMs. Furthermore, the SS-GANS performed much better than GANS on the DOIC problem, even without the SOMs.

Across all experiments, Hyperseed convincingly machine learning and robotics context is currently gaining a demonstrates its key novelties of learning from a few input great momentum [1]-[6]. In classification tasks, the use of vectors and single vector operation learning rule, both of which VSA leads to order of magnitude increase in energy efficiency contribute towards reduced time and computation complexity. of computations on the one hand and natively enables oneshot The paper is structured as follows. Section II describes and multitask learning on the other [7]. It is prospected the related work relevant to Hyperseed operations. The used that VSA will play a key role in the development of novel methods including the fundamentals of VSA are presented neuromorphic computer architectures [8] as an algorithmic in Section III. Section IV presents the main contribution - abstraction [9], [10]. The main contribution of this paper is the method for unsupervised learning Hyperseed. Section V a novel algorithm for unsupervised learning called Hyperseed, reports the results of the performance evaluation the experiments.

Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier. A challenge is to identify which points to label to best improve performance while limiting the number of new labels. "Model-change" active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s). We pair this idea with graph-based semi-supervised learning methods, that use the spectrum of the graph Laplacian matrix, which can be truncated to avoid prohibitively large computational and storage costs. We consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior distribution. We show a variety of multiclass examples that illustrate improved performance over prior state-of-art.

The required amount of labeled data is one of the biggest issues in deep learning. Semi-Supervised Learning can potentially solve this issue by using additional unlabeled data. However, many datasets suffer from variability in the annotations. The aggregated labels from these annotation are not consistent between different annotators and thus are considered fuzzy. These fuzzy labels are often not considered by Semi-Supervised Learning. This leads either to an inferior performance or to higher initial annotation costs in the complete machine learning development cycle. We envision the incorporation of fuzzy labels into Semi-Supervised Learning and give a proof-of-concept of the potential lower costs and higher consistency in the complete development cycle. As part of our concept, we discuss current limitations, futures research opportunities and potential broad impacts.