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Found 7 papers, 4 papers with code
Complex Clipping for Improved Generalization in Machine Learning
For many machine learning applications, a common input representation is a spectrogram.
Estimating and Analyzing Neural Information Flow Using Signal Processing on Graphs
We demonstrate how communication in the brain can be estimated from recorded neural activity using concepts from graph signal processing.
Using a Novel COVID-19 Calculator to Measure Positive U.S. Socio-Economic Impact of a COVID-19 Pre-Screening Solution (AI/ML)
The COVID-19 pandemic has been a scourge upon humanity, claiming the lives of more than 5. 1 million people worldwide; the global economy contracted by 3. 5% in 2020.
Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding
This interpretability also provides principled initializations that enable faster training and convergence to better solutions compared to conventional random initialization.
Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery
Recurrent neural networks (RNNs) are powerful and effective for processing sequential data.
Full-Capacity Unitary Recurrent Neural Networks
To address this question, we propose full-capacity uRNNs that optimize their recurrence matrix over all unitary matrices, leading to significantly improved performance over uRNNs that use a restricted-capacity recurrence matrix.
Ranked #25 on
Sequential Image Classification
on Sequential MNIST
Open-Ended Question Answering
Sequential Image Classification
Enhancement and Recognition of Reverberant and Noisy Speech by Extending Its Coherence
Our approach centers around using a single-channel minimum mean-square error log-spectral amplitude (MMSE-LSA) estimator proposed by Habets, which scales coefficients in a time-frequency domain to suppress noise and reverberation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
