- Read more about Quantum Federated Learning with Quantum Data
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Mahdi Poster.pdf
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- Read more about Phonotactic Language Recognition using a Universal Phoneme Recognizer and a Transformer Architecture
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In this paper, we describe a phonotactic language recognition model that effectively manages long and short n-gram input sequences to learn contextual phonotacticbased vector embeddings. Our approach uses a transformerbased encoder that integrates a sliding window attention to attempt finding discriminative short and long cooccurrences of language dependent n-gram phonetic units. We then evaluate and compare the use of different phoneme recognizers (Brno and Allosaurus) and sub-unit tokenizers to help select the more discriminative n-grams.
Poster.pdf
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- Read more about On Identifiable Polytope Characterization for Polytopic Matrix Factorization
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Polytopic matrix factorization (PMF) is a recently introduced matrix decomposition method in which the data vectors are modeled as linear transformations of samples from a polytope. The successful recovery of the original factors in the generative PMF model is conditioned on the "identifiability" of the chosen polytope. In this article, we investigate the problem of determining the identifiability of a polytope. The identifiability condition requires the polytope to be permutation-and/or-sign-only invariant.
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- Read more about Enhancing class understanding via prompt-tuning for zero-shot text classification
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- Read more about Modeling Of Pre-trained Neural Network Embeddings Learned From Raw Waveform For Covid-19 Infection Detection
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COVID-19 is a respiratory system disorder that can disrupt the function of lungs. Effects of dysfunctional respiratory mechanism can reflect upon other modalities which function in close coupling. Audio signals result from modulation of respiration through speech production system, and hence acoustic information can be modeled for detection of COVID-19. In that direction, this paper is addressing the second DiCOVA challenge that deals with COVID-19 detection based on speech, cough and breathing.
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- Read more about Path signatures for non-intrusive load monitoring
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- Read more about Path signatures for non-intrusive load monitoring
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- Read more about THE DAWN OF QUANTUM NATURAL LANGUAGE PROCESSING
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In this paper, we discuss the initial attempts at boosting understanding of human language based on deep-learning models with quantum computing. We successfully train a quantum-enhanced Long Short-Term Memory network to perform the parts-of-speech tagging task via numerical simulations. Moreover, a quantum-enhanced Transformer is proposed to perform the sentiment analysis based on the existing dataset.
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- Read more about A MULTI-RESOLUTION LOW-RANK TENSOR DECOMPOSITION
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The (efficient and parsimonious) decomposition of higher-order tensors is a fundamental problem with numerous applications in a variety of fields. Several methods have been proposed in the literature to that end, with the Tucker and PARAFAC decompositions being the most prominent ones. Inspired by the latter, in this work we propose a multi-resolution low-rank tensor decomposition to describe (approximate) a tensor in a hierarchical fashion.
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- Read more about Digraph Signal Processing with Generalized Boundary Conditions
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Signal processing on directed graphs (digraphs) is problematic, since the graph shift, and thus associated filters, are in general not diagonalizable. Furthermore, the Fourier transform in this case is now obtained from the Jordan decomposition, which may not be computable at all for large graphs. We propose a novel and general solution for this problem based on matrix perturbation theory: We design an algorithm that adds a small number of edges to a given digraph to destroy nontrivial Jordan blocks.
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