pwc-archive/papers-with-abstracts huggingface dataset:
Quasi-Periodic Parallel WaveGAN: A Non-autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network
Authors: Yi-Chiao Wu, Tomoki Hayashi, Takuma Okamoto, Hisashi Kawai, Tomoki Toda
Date: 2020-07-25 00:00:00

In this paper, we propose a quasi-periodic parallel WaveGAN (QPPWG) waveform generative model, which applies a quasi-periodic (QP) structure to a parallel WaveGAN (PWG) model using pitch-dependent dilated convolution networks (PDCNNs). PWG is a small-footprint GAN-based raw waveform generative model, whose generation time is much faster than real time because of its compact model and non-autoregressive (non-AR) and non-causal mechanisms. Although PWG achieves high-fidelity speech generation, the generic and simple network architecture lacks pitch controllability for an unseen auxiliary fundamental frequency ($F_{0}$) feature such as a scaled $F_{0}$. To improve the pitch controllability and speech modeling capability, we apply a QP structure with PDCNNs to PWG, which introduces pitch information to the network by dynamically changing the network architecture corresponding to the auxiliary $F_{0}$ feature. Both objective and subjective experimental results show that QPPWG outperforms PWG when the auxiliary $F_{0}$ feature is scaled. Moreover, analyses of the intermediate outputs of QPPWG also show better tractability and interpretability of QPPWG, which respectively models spectral and excitation-like signals using the cascaded fixed and adaptive blocks of the QP structure.
Quasi-Periodic WaveNet: An Autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network
Authors: Yi-Chiao Wu, Tomoki Hayashi, Patrick Lumban Tobing, Kazuhiro Kobayashi, Tomoki Toda
Date: 2020-07-11 00:00:00

In this paper, a pitch-adaptive waveform generative model named Quasi-Periodic WaveNet (QPNet) is proposed to improve the limited pitch controllability of vanilla WaveNet (WN) using pitch-dependent dilated convolution neural networks (PDCNNs). Specifically, as a probabilistic autoregressive generation model with stacked dilated convolution layers, WN achieves high-fidelity audio waveform generation. However, the pure-data-driven nature and the lack of prior knowledge of audio signals degrade the pitch controllability of WN. For instance, it is difficult for WN to precisely generate the periodic components of audio signals when the given auxiliary fundamental frequency ($F_{0}$) features are outside the $F_{0}$ range observed in the training data. To address this problem, QPNet with two novel designs is proposed. First, the PDCNN component is applied to dynamically change the network architecture of WN according to the given auxiliary $F_{0}$ features. Second, a cascaded network structure is utilized to simultaneously model the long- and short-term dependencies of quasi-periodic signals such as speech. The performances of single-tone sinusoid and speech generations are evaluated. The experimental results show the effectiveness of the PDCNNs for unseen auxiliary $F_{0}$ features and the effectiveness of the cascaded structure for speech generation.
Quasi-Periodic WaveNet Vocoder: A Pitch Dependent Dilated Convolution Model for Parametric Speech Generation
Authors: Yi-Chiao Wu, Tomoki Hayashi, Patrick Lumban Tobing, Kazuhiro Kobayashi, Tomoki Toda
Date: 2019-07-01 00:00:00

In this paper, we propose a quasi-periodic neural network (QPNet) vocoder with a novel network architecture named pitch-dependent dilated convolution (PDCNN) to improve the pitch controllability of WaveNet (WN) vocoder. The effectiveness of the WN vocoder to generate high-fidelity speech samples from given acoustic features has been proved recently. However, because of the fixed dilated convolution and generic network architecture, the WN vocoder hardly generates speech with given F0 values which are outside the range observed in training data. Consequently, the WN vocoder lacks the pitch controllability which is one of the essential capabilities of conventional vocoders. To address this limitation, we propose the PDCNN component which has the time-variant adaptive dilation size related to the given F0 values and a cascade network structure of the QPNet vocoder to generate quasi-periodic signals such as speech. Both objective and subjective tests are conducted, and the experimental results demonstrate the better pitch controllability of the QPNet vocoder compared to the same and double sized WN vocoders while attaining comparable speech qualities. Index Terms: WaveNet, vocoder, quasi-periodic signal, pitch-dependent dilated convolution, pitch controllability
A Reliability-aware Multi-armed Bandit Approach to Learn and Select Users in Demand Response
Authors: YingYing Li, Qinran Hu, Na Li
Date: 2020-03-20 00:00:00
Tasks: Avg, Thompson Sampling

One challenge in the optimization and control of societal systems is to handle the unknown and uncertain user behavior. This paper focuses on residential demand response (DR) and proposes a closed-loop learning scheme to address these issues. In particular, we consider DR programs where an aggregator calls upon residential users to change their demand so that the total load adjustment is close to a target value. To learn and select the right users, we formulate the DR problem as a combinatorial multi-armed bandit (CMAB) problem with a reliability objective. We propose a learning algorithm: CUCB-Avg (Combinatorial Upper Confidence Bound-Average), which utilizes both upper confidence bounds and sample averages to balance the tradeoff between exploration (learning) and exploitation (selecting). We consider both a fixed time-invariant target and time-varying targets, and show that CUCB-Avg achieves $O(\log T)$ and $O(\sqrt{T \log(T)})$ regrets respectively. Finally, we numerically test our algorithms using synthetic and real data, and demonstrate that our CUCB-Avg performs significantly better than the classic CUCB and also better than Thompson Sampling.
Solving RED with Weighted Proximal Methods
Authors: Tao Hong, Irad Yavneh, Michael Zibulevsky
Date: 2019-05-30 00:00:00
Tasks: Denoising

REgularization by Denoising (RED) is an attractive framework for solving inverse problems by incorporating state-of-the-art denoising algorithms as the priors. A drawback of this approach is the high computational complexity of denoisers, which dominate the computation time. In this paper, we apply a general framework called weighted proximal methods (WPMs) to solve RED efficiently. We first show that two recently introduced RED solvers (using the fixed point and accelerated proximal gradient methods) are particular cases of WPMs. Then we show by numerical experiments that slightly more sophisticated variants of WPM can lead to reduced run times for RED by requiring a significantly smaller number of calls to the denoiser.
Power to Air-transportation via Hydrogen
Authors: Alireza Soroudi, Soheil Jafari
Date: 2020-07-24 00:00:00

This paper proposes a framework to analyze the concept of power to hydrogen (P2H) for fueling the next generation of aircraft. The impact of introducing new P2H loads is investigated from different aspects namely, cost, carbon emission, and wind curtailment. The newly introduced electric load is calculated based on the idea of replacing the busiest international flight route in the Europe, Dublin-London Heathrow, by hydrogen fuel-powered aircraft as a high potential candidate for the next generation of air travel systems to cope with the ambitious targets set in Europe Flight Path 2050 by the Advisory Council for Aeronautics Research in Europe (ACARE). The simulation is performed on a representative Irish transmission network to demonstrate the effectiveness of the proposed solution.
An Efficient Concept Drift Detection Method for Streaming Data under Limited Labeling
Authors: Youngin KIM, Cheong Hee Park
Date: 2017-10-01 00:00:00
Tasks: Drift Detection

In data stream analysis, detecting the concept drift accu-
rately is important to maintain the classification performance. Most drift
detection methods assume that the class labels become available immedi-
ately after a data sample arrives. However, it is unrealistic to attempt to
acquire all of the labels when processing the data streams, as labeling costs
are high and much time is needed. In this paper, we propose a concept drift
detection method under the assumption that there is limited access or no
access to class labels. The proposed method detects concept drift on unla-
beled data streams based on the class label information which is predicted
by a classifier or a virtual classifier. Experimental results on synthetic and
real streaming data show that the proposed method is competent to detect
the concept drift on unlabeled data stream.
key words: conce
Randomized Overdrive Neural Networks
Authors: Christian J. Steinmetz, Joshua D. Reiss
Date: 2020-10-08 00:00:00

By processing audio signals in the time-domain with randomly weighted temporal convolutional networks (TCNs), we uncover a wide range of novel, yet controllable overdrive effects. We discover that architectural aspects, such as the depth of the network, the kernel size, the number of channels, the activation function, as well as the weight initialization, all have a clear impact on the sonic character of the resultant effect, without the need for training. In practice, these effects range from conventional overdrive and distortion, to more extreme effects, as the receptive field grows, similar to a fusion of distortion, equalization, delay, and reverb. To enable use by musicians and producers, we provide a real-time plugin implementation. This allows users to dynamically design networks, listening to the results in real-time. We provide a demonstration and code at https://csteinmetz1.github.io/ronn.
Practical Deep Learning for Cloud, Mobile, and Edge
Authors: Anirudh Koul, Siddha Ganju, Meher Kasam
Date: 2019-10-01 00:00:00
Tasks: Deep Learning, Transfer Learning

Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.

Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.
​
Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite
Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral
Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies
Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning
Use transfer learning to train models in minutes
Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
Bridge Structural Health Monitoring using Asynchronous Mobile Sensing Data
Authors: Soheil Sadeghi Eshkevari, Liam Cronin, Shamim N. Pakzad, Thomas J. Matarazzo
Date: 2020-07-17 00:00:00
Tasks: Structural Health Monitoring

This study presents a flexible approach for bridge modal identification using smartphone data collected by a large pool of passing vehicles. With each trip of a mobile sensor, the spatio-temporal response of the bridge is sampled, plus various sources of noise, e.g., vehicle dynamics, environmental effects, and road profile. This paper provides further evidence to support the hypothesis that through trip aggregation, such noise effects can be mitigated and the true bridge dynamics are exhibited. In this study, the continuous wavelet transform is applied to each trip, and the results are combined to estimate the structural modal response of the bridge. The Crowdsourced Modal Identification using Continuous Wavelets (CMICW) method is presented and validated in an experimental setting. In summary, the method successfully identifies natural frequencies and absolute mode shapes of a bridge with high accuracy. Notably, these results are the first to extract torsional mode shape information from mobile sensor data. Moreover, the influence of vehicle speed on the estimation accuracy is investigated. Finally, a hybrid simulation framework is proposed to account for the vehicle dynamics within the raw mobile sensing data. The proposed method is successful in removing vehicle dynamic effects and identifying modal properties. These results contribute to the growing body of knowledge on the practice of mobile crowdsensing for physical properties of transportation infrastructure.
HID: Hierarchical Multiscale Representation Learning for Information Diffusion
Authors: Honglu Zhou, Shuyuan Xu, Zuohui Fu, Gerard de Melo, Yongfeng Zhang, Mubbasir Kapadia
Date: 2020-04-19 00:00:00
Tasks: Representation Learning

Multiscale modeling has yielded immense success on various machine learning tasks. However, it has not been properly explored for the prominent task of information diffusion, which aims to understand how information propagates along users in online social networks. For a specific user, whether and when to adopt a piece of information propagated from another user is affected by complex interactions, and thus, is very challenging to model. Current state-of-the-art techniques invoke deep neural models with vector representations of users. In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling. The proposed framework can be layered on top of all information diffusion techniques that leverage user representations, so as to boost the predictive power and learning efficiency of the original technique. Extensive experiments on three real-world datasets showcase the superiority of our method.
How well does surprisal explain N400 amplitude under different experimental conditions?
Authors: James A. Michaelov, Benjamin K. Bergen
Date: 2020-10-09 00:00:00

We investigate the extent to which word surprisal can be used to predict a neural measure of human language processing difficulty - the N400. To do this, we use recurrent neural networks to calculate the surprisal of stimuli from previously published neurolinguistic studies of the N400. We find that surprisal can predict N400 amplitude in a wide range of cases, and the cases where it cannot do so provide valuable insight into the neurocognitive processes underlying the response.
Towards Hardware-Agnostic Gaze-Trackers
Authors: Jatin Sharma, Jon Campbell, Pete Ansell, Jay Beavers, Christopher O'Dowd
Date: 2020-10-11 00:00:00

Gaze-tracking is a novel way of interacting with computers which allows new scenarios, such as enabling people with motor-neuron disabilities to control their computers or doctors to interact with patient information without touching screen or keyboard. Further, there are emerging applications of gaze-tracking in interactive gaming, user experience research, human attention analysis and behavioral studies. Accurate estimation of the gaze may involve accounting for head-pose, head-position, eye rotation, distance from the object as well as operating conditions such as illumination, occlusion, background noise and various biological aspects of the user. Commercially available gaze-trackers utilize specialized sensor assemblies that usually consist of an infrared light source and camera. There are several challenges in the universal proliferation of gaze-tracking as accessibility technologies, specifically its affordability, reliability, and ease-of-use. In this paper, we try to address these challenges through the development of a hardware-agnostic gaze-tracker. We present a deep neural network architecture as an appearance-based method for constrained gaze-tracking that utilizes facial imagery captured on an ordinary RGB camera ubiquitous in all modern computing devices. Our system achieved an error of 1.8073cm on GazeCapture dataset without any calibration or device specific fine-tuning. This research shows promise that one day soon any computer, tablet, or phone will be controllable using just your eyes due to the prediction capabilities of deep neutral networks.
PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation
Authors: Xiang Yue, Shuang Zhou
Date: 2020-10-11 00:00:00
Proceeding: EMNLP (ClinicalNLP) 2020 11
Tasks: Data Augmentation, De-identification

De-identification is the task of identifying protected health information (PHI) in the clinical text. Existing neural de-identification models often fail to generalize to a new dataset. We propose a simple yet effective data augmentation method PHICON to alleviate the generalization issue. PHICON consists of PHI augmentation and Context augmentation, which creates augmented training corpora by replacing PHI entities with named-entities sampled from external sources, and by changing background context with synonym replacement or random word insertion, respectively. Experimental results on the i2b2 2006 and 2014 de-identification challenge datasets show that PHICON can help three selected de-identification models boost F1-score (by at most 8.6%) on cross-dataset test setting. We also discuss how much augmentation to use and how each augmentation method influences the performance.
Deep Active Learning for Joint Classification & Segmentation with Weak Annotator
Authors: Soufiane Belharbi, Ismail Ben Ayed, Luke McCaffrey, Eric Granger
Date: 2020-10-10 00:00:00
Tasks: Active Learning, Classification, General Classification, Self-Learning, Weakly-supervised Learning

CNN visualization and interpretation methods, like class-activation maps (CAMs), are typically used to highlight the image regions linked to class predictions. These models allow to simultaneously classify images and extract class-dependent saliency maps, without the need for costly pixel-level annotations. However, they typically yield segmentations with high false-positive rates and, therefore, coarse visualisations, more so when processing challenging images, as encountered in histology. To mitigate this issue, we propose an active learning (AL) framework, which progressively integrates pixel-level annotations during training. Given training data with global image-level labels, our deep weakly-supervised learning model jointly performs supervised image-level classification and active learning for segmentation, integrating pixel annotations by an oracle. Unlike standard AL methods that focus on sample selection, we also leverage large numbers of unlabeled images via pseudo-segmentations (i.e., self-learning at the pixel level), and integrate them with the oracle-annotated samples during training. We report extensive experiments over two challenging benchmarks -- high-resolution medical images (histology GlaS data for colon cancer) and natural images (CUB-200-2011 for bird species). Our results indicate that, by simply using random sample selection, the proposed approach can significantly outperform state-of the-art CAMs and AL methods, with an identical oracle-supervision budget. Our code is publicly available.
Discourse structure interacts with reference but not syntax in neural language models
Authors: Forrest Davis, Marten Van Schijndel
Date: 2020-10-10 00:00:00
Proceeding: CONLL 2020
Tasks: coreference-resolution, Coreference Resolution, Language Modeling, Language Modelling

Language models (LMs) trained on large quantities of text have been claimed to acquire abstract linguistic representations. Our work tests the robustness of these abstractions by focusing on the ability of LMs to learn interactions between different linguistic representations. In particular, we utilized stimuli from psycholinguistic studies showing that humans can condition reference (i.e. coreference resolution) and syntactic processing on the same discourse structure (implicit causality). We compared both transformer and long short-term memory LMs to find that, contrary to humans, implicit causality only influences LM behavior for reference, not syntax, despite model representations that encode the necessary discourse information. Our results further suggest that LM behavior can contradict not only learned representations of discourse but also syntactic agreement, pointing to shortcomings of standard language modeling.
Partial FC: Training 10 Million Identities on a Single Machine
Authors: Xiang An, Xuhan Zhu, Yang Xiao, Lan Wu, Ming Zhang, Yuan Gao, Bin Qin, Debing Zhang, Ying Fu
Date: 2020-10-11 00:00:00
Tasks: Face Identification, Face Recognition, Face Verification, GPU, Representation Learning

Face recognition has been an active and vital topic among computer vision community for a long time. Previous researches mainly focus on loss functions used for facial feature extraction network, among which the improvements of softmax-based loss functions greatly promote the performance of face recognition. However, the contradiction between the drastically increasing number of face identities and the shortage of GPU memories is gradually becoming irreconcilable. In this paper, we thoroughly analyze the optimization goal of softmax-based loss functions and the difficulty of training massive identities. We find that the importance of negative classes in softmax function in face representation learning is not as high as we previously thought. The experiment demonstrates no loss of accuracy when training with only 10\% randomly sampled classes for the softmax-based loss functions, compared with training with full classes using state-of-the-art models on mainstream benchmarks. We also implement a very efficient distributed sampling algorithm, taking into account model accuracy and training efficiency, which uses only eight NVIDIA RTX2080Ti to complete classification tasks with tens of millions of identities. The code of this paper has been made available https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc.
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification
Authors: Yulin Wang, Kangchen Lv, Rui Huang, Shiji Song, Le Yang, Gao Huang
Date: 2020-10-11 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Computational Efficiency, General Classification, image-classification, Image Classification

The accuracy of deep convolutional neural networks (CNNs) generally improves when fueled with high resolution images. However, this often comes at a high computational cost and high memory footprint. Inspired by the fact that not all regions in an image are task-relevant, we propose a novel framework that performs efficient image classification by processing a sequence of relatively small inputs, which are strategically selected from the original image with reinforcement learning. Such a dynamic decision process naturally facilitates adaptive inference at test time, i.e., it can be terminated once the model is sufficiently confident about its prediction and thus avoids further redundant computation. Notably, our framework is general and flexible as it is compatible with most of the state-of-the-art light-weighted CNNs (such as MobileNets, EfficientNets and RegNets), which can be conveniently deployed as the backbone feature extractor. Experiments on ImageNet show that our method consistently improves the computational efficiency of a wide variety of deep models. For example, it further reduces the average latency of the highly efficient MobileNet-V3 on an iPhone XS Max by 20% without sacrificing accuracy. Code and pre-trained models are available at https://github.com/blackfeather-wang/GFNet-Pytorch.