The state of the art of deep learning-based Wi-Fi indoor positioning: A review
Wi-Fi positioning has drawn great attention in the field of indoor positioning, due to its low
cost, easy deployment, and large positioning range. However, the Wi-Fi signal is highly …
cost, easy deployment, and large positioning range. However, the Wi-Fi signal is highly …
A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling
Ten established, data-driven dynamic algorithms are surveyed and a practical guide for
understanding these methods generated. Existing Python programming packages for …
understanding these methods generated. Existing Python programming packages for …
Confidence estimation and deletion prediction using bidirectional recurrent neural networks
The standard approach to assess reliability of automatic speech transcriptions is through the
use of confidence scores. If accurate, these scores provide a flexible mechanism to flag …
use of confidence scores. If accurate, these scores provide a flexible mechanism to flag …
An evaluation of word-level confidence estimation for end-to-end automatic speech recognition
Quantifying the confidence (or conversely the uncertainty) of a prediction is a highly
desirable trait of an automatic system, as it improves the robustness and usefulness in …
desirable trait of an automatic system, as it improves the robustness and usefulness in …
Kronecker CP decomposition with fast multiplication for compressing RNNs
D Wang, B Wu, G Zhao, M Yao, H Chen… - … on Neural Networks …, 2021 - ieeexplore.ieee.org
Recurrent neural networks (RNNs) are powerful in the tasks oriented to sequential data,
such as natural language processing and video recognition. However, because the modern …
such as natural language processing and video recognition. However, because the modern …
[PDF][PDF] Utterance Confidence Measure for End-to-End Speech Recognition with Applications to Distributed Speech Recognition Scenarios.
In this paper, we present techniques to compute confidence score on the predictions made
by an end-to-end speech recognition model. Our proposed neural confidence measure …
by an end-to-end speech recognition model. Our proposed neural confidence measure …
Bi-directional lattice recurrent neural networks for confidence estimation
The standard approach to mitigate errors made by an automatic speech recognition system
is to use confidence scores associated with each predicted word. In the simplest case, these …
is to use confidence scores associated with each predicted word. In the simplest case, these …
Residual energy-based models for end-to-end speech recognition
End-to-end models with auto-regressive decoders have shown impressive results for
automatic speech recognition (ASR). These models formulate the sequence-level probability …
automatic speech recognition (ASR). These models formulate the sequence-level probability …
Multi-task learning for end-to-end ASR word and utterance confidence with deletion prediction
Confidence scores are very useful for downstream applications of automatic speech
recognition (ASR) systems. Recent works have proposed using neural networks to learn …
recognition (ASR) systems. Recent works have proposed using neural networks to learn …
Realistic acceleration of neural networks with fine-grained tensor decomposition
R Lv, D Wang, J Zheng, Y **e, ZX Yang - Neurocomputing, 2022 - Elsevier
As the modern deep neural networks (DNNs) have become more and more large-scale and
expensive, the topic of DNN compression grows into a hot direction nowadays. Among …
expensive, the topic of DNN compression grows into a hot direction nowadays. Among …