Statistical analysis of magnetic resonance imaging (MRI) can help radiologists to detect pathologies that are otherwise likely to be missed. Deep learning (DL) has shown promise in modeling complex spatial data for brain anomaly detection… However, DL models have major deficiencies: they need large amounts of high-quality training data, are difficult to design and train and are sensitive to subtle changes in scanning protocols and hardware. Here, we show that also simple statistical methods such as voxel-wise (baseline and covariance) […]

High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining. These representations have different degrees of interpretability, with efficient distributed representations coming at the cost of the loss of feature to dimension mapping… This implies that there is obfuscation in the way concepts are captured in these embedding spaces. Its effects are seen in many representations and tasks, one particularly problematic one being in language representations […]

This paper proposes a end-to-end deep network to recognize kinds of accents under the same language, where we develop and transfer the deep architecture in speaker-recognition area to accent classification task for learning utterance-level accent representation. Compared with the individual-level feature in speaker-recognition, accent recognition throws a more challenging issue in acquiring compact group-level features for the speakers with the same accent, hence a good discriminative accent feature space is desired… Our deep framework adopts multitask-learning mechanism and mainly consists […]

Convolutional neural networks (CNNs) have been observed to be inefficient in propagating information across distant spatial positions in images. Recent studies in image inpainting attempt to overcome this issue by explicitly searching reference regions throughout the entire image to fill the features from reference regions in the missing regions… This operation can be implemented as contextual attention layer (CA layer) cite{yu2018generative}, which has been widely used in many deep learning-based methods. However, it brings significant computational overhead as it computes […]

Machine learning classifiers are critically prone to evasion attacks. Adversarial examples are slightly modified inputs that are then misclassified, while remaining perceptively close to their originals… Last couple of years have witnessed a striking decrease in the amount of queries a black box attack submits to the target classifier, in order to forge adversarials. This particularly concerns the black-box score-based setup, where the attacker has access to top predicted probabilites: the amount of queries went from to millions of to […]

Optimization of resource distribution has been a challenging topic in current society. To explore this topic, we develop a Coalition Control Model(CCM) based on the Model Predictive Control(MPC) and test it using a fishing model with linear parameters… The fishing model focuses on the problem of distributing fishing fleets in certain regions to maximize fish caught using either exhaustive or heuristic search. Our method introduces a communication mechanism to allow fishing fleets to merge or split, after which new coalitions […]

In this paper, we propose a method combining variational autoencoder model of speech with a spatial clustering approach for multi-channel speech separation. The advantage of integrating spatial clustering with a spectral model was shown in several works… As the spectral model, previous works used either factorial generative models of the mixed speech or discriminative neural networks. In our work, we combine the strengths of both approaches, by building a factorial model based on a generative neural network, a variational autoencoder. […]

As the name implies, contextualized representations of language are typically motivated by their ability to encode context. Which aspects of context are captured by such representations?.. We introduce an approach to address this question using Representational Similarity Analysis (RSA). As case studies, we investigate the degree to which a verb embedding encodes the verb’s subject, a pronoun embedding encodes the pronoun’s antecedent, and a full-sentence representation encodes the sentence’s head word (as determined by a dependency parse). In all cases, […]