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·Quantum Machine Learning QML has emerged as a promising field of research aiming to leverage the capabilities of quantum computing to enhance existing machine learning methodologies Recent studies have revealed that like their classical counterparts QML models based on Parametrized Quantum Circuits PQCs are also vulnerable to adversarial
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·The main contribution of this paper are 1 easy to use universal classifiers trained on 5 NLI datasets and 28 non NLI datasets with 389 diverse classes improving zeroshot performance by % compared to NLI only models; 2 a step by step guide with Juypter notebooks enabling users to train and adapt their own universal classifiers
·In this work we present a novel approach to transform supervised classifiers into effective unsupervised anomaly detectors The method we have developed termed Discriminatory Detection of Distortions DDD enhances anomaly detection by training a discriminator model on both original and artificially modified datasets We conducted a
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·Using machine learning classifier such as naive Bayes classifier for predicting the output The filtered out data will help us to predict the output on the premise of negative and positive features of the words within the comments The best method to test any of the machine tool is by translating the textual form from one language to the
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·Tabular data prediction is a fundamental machine learning task for many applications Existing methods predominantly employ discriminative modeling and operate under the assumption of a fixed target column necessitating re training for every new predictive task Inspired by the generative power of large language models LLMs this paper exploits the idea
·This work conceptualize the notion of additive UAPs for PQC based classifiers and theoretically demonstrate their existence and formulate a new method for generating unitary UAPs QuGAP U using quantum generative models and a novel loss function based on fidelity constraints Quantum Machine Learning QML has emerged as a promising field of research
·Title Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines Authors Jonas Jäger Roman V Krems View a PDF of the paper titled Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines by Jonas J"ager and Roman V Krems
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·The Universal Classifier works initially based on majority votes for crisp Universal Classification while works on Fuzzy Agg regation for Fuzzy Universal Classification T asks This shall be the basis
·Universal domain adaptation UniDA is a general unsupervised domain adaptation setting which addresses both domain and label shifts in adaptation Its main challenge lies in how to identify target samples in unshared or unknown classes Previous methods commonly strive to depict sample confidence along with a threshold for rejecting unknowns
·Generating Universal Adversarial Perturbations for Quantum arXiv CS Machine Learning Pub Date 2024 02 13 DOI Gautham Anil Vishnu Vinod Apurva Narayan Quantum Machine Learning QML has emerged as a promising field of research aiming to leverage the capabilities of quantum computing to enhance existing
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