Search Results for author:
Found 10 papers, 2 papers with code
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor
Neural architecture search has proven to be a powerful approach to designing and refining neural networks, often boosting their performance and efficiency over manually-designed variations, but comes with computational overhead.
Federated Self-supervised Speech Representations: Are We There Yet?
The ubiquity of microphone-enabled devices has lead to large amounts of unlabelled audio data being produced at the edge.
Conditioning Sequence-to-sequence Networks with Learned Activations
Conditional neural networks play an important role in a number of sequence-to-sequence modeling tasks, including personalized sound enhancement (PSE), speaker dependent automatic speech recognition (ASR), and generative modeling such as text-to-speech synthesis.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Smart at what cost? Characterising Mobile Deep Neural Networks in the wild
To this end, we analyse over 16k of the most popular apps in the Google Play Store to characterise their DNN usage and performance across devices of different capabilities, both across tiers and generations.
Zero-Cost Proxies for Lightweight NAS
For example, Spearman's rank correlation coefficient between final validation accuracy and our best zero-cost proxy on NAS-Bench-201 is 0. 82, compared to 0. 61 for EcoNAS (a recently proposed reduced-training proxy).
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition
These datasets, however, focus predominantly on computer vision and NLP tasks and thus suffer from the problem of limited coverage of application domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Bunched LPCNet : Vocoder for Low-cost Neural Text-To-Speech Systems
LPCNet is an efficient vocoder that combines linear prediction and deep neural network modules to keep the computational complexity low.
Iterative Compression of End-to-End ASR Model using AutoML
Increasing demand for on-device Automatic Speech Recognition (ASR) systems has resulted in renewed interests in developing automatic model compression techniques.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Graph Input Representations for Machine Learning Applications in Urban Network Analysis
In this paper, we design and evaluate six different graph input representations (i. e., representations of the network paths), by considering the network's topological and temporal characteristics, for being used as inputs for machine learning models to learn the behavior of urban networks paths.
Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities
We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network.
