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Found 14 papers, 2 papers with code
SAQAM: Spatial Audio Quality Assessment Metric
Audio quality assessment is critical for assessing the perceptual realism of sounds.
Continual self-training with bootstrapped remixing for speech enhancement
Specifically, a separation teacher model is pre-trained on an out-of-domain dataset and is used to infer estimated target signals for a batch of in-domain mixtures.
DPLM: A Deep Perceptual Spatial-Audio Localization Metric
Subjective evaluations are critical for assessing the perceptual realism of sounds in audio-synthesis driven technologies like augmented and virtual reality.
When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience Applications
Many studies in biomedical and health sciences involve small sample sizes due to logistic or financial constraints.
The Incremental Multiresolution Matrix Factorization Algorithm
Multiresolution analysis and matrix factorization are foundational tools in computer vision.
Accelerating Permutation Testing in Voxel-wise Analysis through Subspace Tracking: A new plugin for SnPM
We find that RapidPT achieves its best runtime performance on medium sized datasets ($50 \leq n \leq 200$), with speedups of 1. 5x - 38x (vs. SnPM13) and 20x-1000x (vs. NaivePT).
On architectural choices in deep learning: From network structure to gradient convergence and parameter estimation
We seek to analyze whether network architecture and input data statistics may guide the choices of learning parameters and vice versa.
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease
Consider samples from two different data sources $\{\mathbf{x_s^i}\} \sim P_{\rm source}$ and $\{\mathbf{x_t^i}\} \sim P_{\rm target}$.
A Projection Free Method for Generalized Eigenvalue Problem With a Nonsmooth Regularizer
Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation.
An NMF Perspective on Binary Hashing
In this paper, we first derive an Augmented Lagrangian approach to optimize the standard binary Hashing objective (i. e., maintain fidelity with a given distance matrix).
On the interplay of network structure and gradient convergence in deep learning
The regularization and output consistency behavior of dropout and layer-wise pretraining for learning deep networks have been fairly well studied.
Convergence rates for pretraining and dropout: Guiding learning parameters using network structure
Unsupervised pretraining and dropout have been well studied, especially with respect to regularization and output consistency.
Speeding up Permutation Testing in Neuroimaging
In this paper, we show that permutation testing in fact amounts to populating the columns of a very large matrix ${\bf P}$.
Convergence of gradient based pre-training in Denoising autoencoders
The success of deep architectures is at least in part attributed to the layer-by-layer unsupervised pre-training that initializes the network.
