- Bayesian learning; Bayesian signal processing (MLR-BAYL)
- Bounds on performance (MLR-PERF)
- Applications in Systems Biology (MLR-SYSB)
- Applications in Music and Audio Processing (MLR-MUSI)
- Applications in Data Fusion (MLR-FUSI)
- Cognitive information processing (MLR-COGP)
- Distributed and Cooperative Learning (MLR-DIST)
- Learning theory and algorithms (MLR-LEAR)
- Neural network learning (MLR-NNLR)
- Information-theoretic learning (MLR-INFO)
- Independent component analysis (MLR-ICAN)
- Graphical and kernel methods (MLR-GRKN)
- Other applications of machine learning (MLR-APPL)
- Pattern recognition and classification (MLR-PATT)
- Source separation (MLR-SSEP)
- Sequential learning; sequential decision methods (MLR-SLER)
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- Read more about Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation
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- Read more about Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation
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- Read more about Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders
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- Read more about ACTIVE LEARNING WITH LABEL PROPORTIONS
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Active Learning (AL) refers to the setting where the learner has the ability to perform queries to an oracle to acquire the true label of an instance or, sometimes, a set of instances. Even though Active Learning has been studied extensively, the setting is usually restricted to assume that the oracle is trustworthy and will provide the actual label. We argue that, while common, this approach can be made more flexible to account for different forms of supervision.
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- Read more about Defending DNN Adversarial Attacks with Pruning and Logits Augmentation
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Deep neural networks (DNNs) have been shown to be powerful models and perform extremely well on many complicated artificial intelligent tasks. However, recent research found that these powerful models are vulnerable to adversarial attacks, i.e., intentionally added imperceptible perturbations to DNN inputs can easily mislead the DNNs with extremely high confidence.
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- Read more about Defending DNN Adversarial Attacks with Pruning and Logits Augmentation
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Deep neural networks (DNNs) have been shown to be powerful models and perform extremely well on many complicated artificial intelligent tasks. However, recent research found that these powerful models are vulnerable to adversarial attacks, i.e., intentionally added imperceptible perturbations to DNN inputs can easily mislead the DNNs with extremely high confidence.
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- Read more about Set-Theoretic Learning for Detection in Cell-Less C-RAN Systems
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- Read more about On The Utility of Conditional Generation Based Mutual Information For Characterizing Adversarial Subspaces
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- Read more about GAN-NL: UNSUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION
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