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Found 7 papers, 2 papers with code
Fearless Steps APOLLO: Advanced Naturalistic Corpora Development
We aim to make this entire resource and supporting speech technology meta-data creation publicly available as a Community Resource for the development of speech and behavioral science.
On Speech Pre-emphasis as a Simple and Inexpensive Method to Boost Speech Enhancement
Pre-emphasis filtering, compensating for the natural energy decay of speech at higher frequencies, has been considered as a common pre-processing step in a number of speech processing tasks over the years.
Topology Optimization using Neural Networks with Conditioning Field Initialization for Improved Efficiency
We employ the strain energy field calculated on the initial design domain as an additional conditioning field input to the neural network throughout the optimization.
DMF-TONN: Direct Mesh-free Topology Optimization using Neural Networks
We show that using a suitable Fourier Features neural network architecture and hyperparameters, the density field approximation neural network can learn the weights to represent the optimal density field for the given domain and boundary conditions, by directly backpropagating the loss gradient through the displacement field approximation neural network, and unlike prior work there is no requirement of a sensitivity filter, optimality criterion method, or a separate training of density network in each topology optimization iteration.
Fearless Steps Challenge Phase-1 Evaluation Plan
The Fearless Steps Challenge 2019 Phase-1 (FSC-P1) is the inaugural Challenge of the Fearless Steps Initiative hosted by the Center for Robust Speech Systems (CRSS) at the University of Texas at Dallas.
Concurrent build direction, part segmentation, and topology optimization for additive manufacturing using neural networks
Through training, the network learns a material density and segment classification in the continuous 3D space.
Is Q-Learning Provably Efficient? An Extended Analysis
This work extends the analysis of the theoretical results presented within the paper Is Q-Learning Provably Efficient?
