pwc-archive/papers-with-abstracts huggingface dataset:
Challenges and Pitfalls of Machine Learning Evaluation and Benchmarking
Authors: Cheng Li, Abdul Dakkak, JinJun Xiong, Wen-mei Hwu
Date: 2019-04-29 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning

An increasingly complex and diverse collection of Machine Learning (ML) models as well as hardware/software stacks, collectively referred to as "ML artifacts", are being proposed - leading to a diverse landscape of ML. These ML innovations proposed have outpaced researchers' ability to analyze, study and adapt them. This is exacerbated by the complicated and sometimes non-reproducible procedures for ML evaluation. A common practice of sharing ML artifacts is through repositories where artifact authors post ad-hoc code and some documentation, but often fail to reveal critical information for others to reproduce their results. This results in users' inability to compare with artifact authors' claims or adapt the model to his/her own use. This paper discusses common challenges and pitfalls of ML evaluation and benchmarking, which can be used as a guideline for ML model authors when sharing ML artifacts, and for system developers when benchmarking or designing ML systems.
HOG feature extraction from encrypted images for privacy-preserving machine learning
Authors: Masaki Kitayama, Hitoshi Kiya
Date: 2019-04-29 00:00:00
Tasks: BIG-bench Machine Learning, Cloud Computing, image-classification, Image Classification, object-detection, Object Detection, Privacy Preserving

In this paper, we propose an extraction method of HOG
(histograms-of-oriented-gradients) features from encryption-then-compression
(EtC) images for privacy-preserving machine learning, where EtC images are
images encrypted by a block-based encryption method proposed for EtC systems
with JPEG compression, and HOG is a feature descriptor used in computer vision
for the purpose of object detection and image classification. Recently, cloud
computing and machine learning have been spreading in many fields. However, the
cloud computing has serious privacy issues for end users, due to unreliability
of providers and some accidents. Accordingly, we propose a novel block-based
extraction method of HOG features, and the proposed method enables us to carry
out any machine learning algorithms without any influence, under some
conditions. In an experiment, the proposed method is applied to a face image
recognition problem under the use of two kinds of classifiers: linear support
vector machine (SVM), gaussian SVM, to demonstrate the effectiveness.
Automatic extrinsic calibration between a camera and a 3D Lidar using 3D point and plane correspondences
Authors: Surabhi Verma, Julie Stephany Berrio, Stewart Worrall, Eduardo Nebot
Date: 2019-04-29 00:00:00

This paper proposes an automated method to obtain the extrinsic calibration
parameters between a camera and a 3D lidar with as low as 16 beams. We use a
checkerboard as a reference to obtain features of interest in both sensor
frames. The calibration board centre point and normal vector are automatically
extracted from the lidar point cloud by exploiting the geometry of the board.
The corresponding features in the camera image are obtained from the camera's
extrinsic matrix. We explain the reasons behind selecting these features, and
why they are more robust compared to other possibilities. To obtain the optimal
extrinsic parameters, we choose a genetic algorithm to address the highly
non-linear state space. The process is automated after defining the bounds of
the 3D experimental region relative to the lidar, and the true board
dimensions. In addition, the camera is assumed to be intrinsically calibrated.
Our method requires a minimum of 3 checkerboard poses, and the calibration
accuracy is demonstrated by evaluating our algorithm using real world and
simulated features.
Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised Learning
Authors: Xinyang Li, Jie Hu, Shengchuan Zhang, Xiaopeng Hong, Qixiang Ye, Chenglin Wu, Rongrong Ji
Date: 2019-04-29 00:00:00
Tasks: Attribute, Disentanglement, Image-to-Image Translation, Translation

Unpaired Image-to-Image Translation (UIT) focuses on translating images among
different domains by using unpaired data, which has received increasing
research focus due to its practical usage. However, existing UIT schemes defect
in the need of supervised training, as well as the lack of encoding domain
information. In this paper, we propose an Attribute Guided UIT model termed
AGUIT to tackle these two challenges. AGUIT considers multi-modal and
multi-domain tasks of UIT jointly with a novel semi-supervised setting, which
also merits in representation disentanglement and fine control of outputs.
Especially, AGUIT benefits from two-fold: (1) It adopts a novel semi-supervised
learning process by translating attributes of labeled data to unlabeled data,
and then reconstructing the unlabeled data by a cycle consistency operation.
(2) It decomposes image representation into domain-invariant content code and
domain-specific style code. The redesigned style code embeds image style into
two variables drawn from standard Gaussian distribution and the distribution of
domain label, which facilitates the fine control of translation due to the
continuity of both variables. Finally, we introduce a new challenge, i.e.,
disentangled transfer, for UIT models, which adopts the disentangled
representation to translate data less related with the training set. Extensive
experiments demonstrate the capacity of AGUIT over existing state-of-the-art
models.
Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms
Authors: Taesik Na, Minah Lee, Burhan A. Mudassar, Priyabrata Saha, Jong Hwan Ko, Saibal Mukhopadhyay
Date: 2019-04-29 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification, Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking

Deep learning on an edge device requires energy efficient operation due to
ever diminishing power budget. Intentional low quality data during the data
acquisition for longer battery life, and natural noise from the low cost sensor
degrade the quality of target output which hinders adoption of deep learning on
an edge device. To overcome these problems, we propose simple yet efficient
mixture of pre-processing experts (MoPE) model to handle various image
distortions including low resolution and noisy images. We also propose to use
adversarially trained auto encoder as a pre-processing expert for the noisy
images. We evaluate our proposed method for various machine learning tasks
including object detection on MS-COCO 2014 dataset, multiple object tracking
problem on MOT-Challenge dataset, and human activity classification on UCF 101
dataset. Experimental results show that the proposed method achieves better
detection, tracking and activity classification accuracies under noise without
sacrificing accuracies for the clean images. The overheads of our proposed MoPE
are 0.67% and 0.17% in terms of memory and computation compared to the baseline
object detection network.
A convolution recurrent autoencoder for spatio-temporal missing data imputation
Authors: Reza Asadi, Amelia Regan
Date: 2019-04-29 00:00:00
Tasks: Imputation, Multivariate Time Series Imputation

When sensors collect spatio-temporal data in a large geographical area, the
existence of missing data cannot be escaped. Missing data negatively impacts
the performance of data analysis and machine learning algorithms. In this
paper, we study deep autoencoders for missing data imputation in
spatio-temporal problems. We propose a convolution bidirectional-LSTM for
capturing spatial and temporal patterns. Moreover, we analyze an autoencoder's
latent feature representation in spatio-temporal data and illustrate its
performance for missing data imputation. Traffic flow data are used for
evaluation of our models. The result shows that the proposed convolution
recurrent neural network outperforms state-of-the-art methods.
Adversarial Speaker Adaptation
Authors: Zhong Meng, Jinyu Li, Yifan Gong
Date: 2019-04-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We propose a novel adversarial speaker adaptation (ASA) scheme, in which
adversarial learning is applied to regularize the distribution of deep hidden
features in a speaker-dependent (SD) deep neural network (DNN) acoustic model
to be close to that of a fixed speaker-independent (SI) DNN acoustic model
during adaptation. An additional discriminator network is introduced to
distinguish the deep features generated by the SD model from those produced by
the SI model. In ASA, with a fixed SI model as the reference, an SD model is
jointly optimized with the discriminator network to minimize the senone
classification loss, and simultaneously to mini-maximize the SI/SD
discrimination loss on the adaptation data. With ASA, a senone-discriminative
deep feature is learned in the SD model with a similar distribution to that of
the SI model. With such a regularized and adapted deep feature, the SD model
can perform improved automatic speech recognition on the target speaker's
speech. Evaluated on the Microsoft short message dictation dataset, ASA
achieves 14.4% and 7.9% relative word error rate improvements for supervised
and unsupervised adaptation, respectively, over an SI model trained from 2600
hours data, with 200 adaptation utterances per speaker.
Adversarial Speaker Verification
Authors: Zhong Meng, Yong Zhao, Jinyu Li, Yifan Gong
Date: 2019-04-29 00:00:00
Tasks: General Classification, Speaker Recognition, Speaker Verification, Text-Dependent Speaker Verification

The use of deep networks to extract embeddings for speaker recognition has
proven successfully. However, such embeddings are susceptible to performance
degradation due to the mismatches among the training, enrollment, and test
conditions. In this work, we propose an adversarial speaker verification (ASV)
scheme to learn the condition-invariant deep embedding via adversarial
multi-task training. In ASV, a speaker classification network and a condition
identification network are jointly optimized to minimize the speaker
classification loss and simultaneously mini-maximize the condition loss. The
target labels of the condition network can be categorical (environment types)
and continuous (SNR values). We further propose multi-factorial ASV to
simultaneously suppress multiple factors that constitute the condition
variability. Evaluated on a Microsoft Cortana text-dependent speaker
verification task, the ASV achieves 8.8% and 14.5% relative improvements in
equal error rates (EER) for known and unknown conditions, respectively.
Attentive Adversarial Learning for Domain-Invariant Training
Authors: Zhong Meng, Jinyu Li, Yifan Gong
Date: 2019-04-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), domain classification, speech-recognition, Speech Recognition

Adversarial domain-invariant training (ADIT) proves to be effective in
suppressing the effects of domain variability in acoustic modeling and has led
to improved performance in automatic speech recognition (ASR). In ADIT, an
auxiliary domain classifier takes in equally-weighted deep features from a deep
neural network (DNN) acoustic model and is trained to improve their
domain-invariance by optimizing an adversarial loss function. In this work, we
propose an attentive ADIT (AADIT) in which we advance the domain classifier
with an attention mechanism to automatically weight the input deep features
according to their importance in domain classification. With this attentive
re-weighting, AADIT can focus on the domain normalization of phonetic
components that are more susceptible to domain variability and generates deep
features with improved domain-invariance and senone-discriminativity over ADIT.
Most importantly, the attention block serves only as an external component to
the DNN acoustic model and is not involved in ASR, so AADIT can be used to
improve the acoustic modeling with any DNN architectures. More generally, the
same methodology can improve any adversarial learning system with an auxiliary
discriminator. Evaluated on CHiME-3 dataset, the AADIT achieves 13.6% and 9.3%
relative WER improvements, respectively, over a multi-conditional model and a
strong ADIT baseline.
Conditional Teacher-Student Learning
Authors: Zhong Meng, Jinyu Li, Yong Zhao, Yifan Gong
Date: 2019-04-28 00:00:00
Tasks: Domain Adaptation, Model Compression

The teacher-student (T/S) learning has been shown to be effective for a
variety of problems such as domain adaptation and model compression. One
shortcoming of the T/S learning is that a teacher model, not always perfect,
sporadically produces wrong guidance in form of posterior probabilities that
misleads the student model towards a suboptimal performance. To overcome this
problem, we propose a conditional T/S learning scheme, in which a "smart"
student model selectively chooses to learn from either the teacher model or the
ground truth labels conditioned on whether the teacher can correctly predict
the ground truth. Unlike a naive linear combination of the two knowledge
sources, the conditional learning is exclusively engaged with the teacher model
when the teacher model's prediction is correct, and otherwise backs off to the
ground truth. Thus, the student model is able to learn effectively from the
teacher and even potentially surpass the teacher. We examine the proposed
learning scheme on two tasks: domain adaptation on CHiME-3 dataset and speaker
adaptation on Microsoft short message dictation dataset. The proposed method
achieves 9.8% and 12.8% relative word error rate reductions, respectively, over
T/S learning for environment adaptation and speaker-independent model for
speaker adaptation.
TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text
Authors: Fady Medhat, Mahnaz Mohammadi, Sardar Jaf, Chris G. Willcocks, Toby P. Breckon, Peter Matthews, Andrew Stephen McGough, Georgios Theodoropoulos, Boguslaw Obara
Date: 2019-04-28 00:00:00

Handling large corpuses of documents is of significant importance in many
fields, no more so than in the areas of crime investigation and defence, where
an organisation may be presented with a large volume of scanned documents which
need to be processed in a finite time. However, this problem is exacerbated
both by the volume, in terms of scanned documents and the complexity of the
pages, which need to be processed. Often containing many different elements,
which each need to be processed and understood. Text recognition, which is a
primary task of this process, is usually dependent upon the type of text, being
either handwritten or machine-printed. Accordingly, the recognition involves
prior classification of the text category, before deciding on the recognition
method to be applied. This poses a more challenging task if a document contains
both handwritten and machine-printed text. In this work, we present a generic
process flow for text recognition in scanned documents containing mixed
handwritten and machine-printed text without the need to classify text in
advance. We realize the proposed process flow using several open-source image
processing and text recognition packages1. The evaluation is performed using a
specially developed variant, presented in this work, of the IAM handwriting
database, where we achieve an average transcription accuracy of nearly 80% for
pages containing both printed and handwritten text.
Application of Autoencoder-Assisted Recurrent Neural Networks to Prevent Cases of Sudden Infant Death Syndrome
Authors: Maximilian Du
Date: 2019-04-28 00:00:00

This project develops and trains a Recurrent Neural Network (RNN) that monitors sleeping infants from an auxiliary microphone for cases of Sudden Infant Death Syndrome (SIDS), manifested in sudden or gradual respiratory arrest. To minimize invasiveness and maximize economic viability, an electret microphone, and parabolic concentrator, paired with a specially designed and tuned amplifier circuit, was used as a very sensitive audio monitoring device, which fed data to the RNN model. This RNN was trained and operated in the frequency domain, where the respiratory activity is most unique from noise. In both training and operation, a Fourier transform and an autoencoder compression were applied to the raw audio, and this transformed audio data was fed into the model in 1/8 second time steps. In operation, this model flagged each perceived breath, and the time between breaths was analyzed through a statistical T-test for slope, which detected dangerous trends. The entire model achieved 92.5% accuracy on continuous data and had an 11.25-second response rate on data that emulated total respiratory arrest. Because of the compatibility of the trained model with many off-the-shelf devices like Android phones and Raspberry Pi's, free-standing processing hardware deployment is a very feasible future goal.
Machine Learning in the Air
Authors: Deniz Gunduz, Paul de Kerret, Nicholas D. Sidiropoulos, David Gesbert, Chandra Murthy, Mihaela van der Schaar
Date: 2019-04-28 00:00:00
Tasks: BIG-bench Machine Learning

Thanks to the recent advances in processing speed and data acquisition and
storage, machine learning (ML) is penetrating every facet of our lives, and
transforming research in many areas in a fundamental manner. Wireless
communications is another success story -- ubiquitous in our lives, from
handheld devices to wearables, smart homes, and automobiles. While recent years
have seen a flurry of research activity in exploiting ML tools for various
wireless communication problems, the impact of these techniques in practical
communication systems and standards is yet to be seen. In this paper, we review
some of the major promises and challenges of ML in wireless communication
systems, focusing mainly on the physical layer. We present some of the most
striking recent accomplishments that ML techniques have achieved with respect
to classical approaches, and point to promising research directions where ML is
likely to make the biggest impact in the near future. We also highlight the
complementary problem of designing physical layer techniques to enable
distributed ML at the wireless network edge, which further emphasizes the need
to understand and connect ML with fundamental concepts in wireless
communications.
Enhancing Prediction Models for One-Year Mortality in Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome
Authors: Seyedeh Neelufar Payrovnaziri, Laura A. Barrett, Daniel Bis, Jiang Bian, Zhe He
Date: 2019-04-28 00:00:00
Tasks: Deep Learning

Predicting the risk of mortality for patients with acute myocardial
infarction (AMI) using electronic health records (EHRs) data can help identify
risky patients who might need more tailored care. In our previous work, we
built computational models to predict one-year mortality of patients admitted
to an intensive care unit (ICU) with AMI or post myocardial infarction
syndrome. Our prior work only used the structured clinical data from MIMIC-III,
a publicly available ICU clinical database. In this study, we enhanced our work
by adding the word embedding features from free-text discharge summaries. Using
a richer set of features resulted in significant improvement in the performance
of our deep learning models. The average accuracy of our deep learning models
was 92.89% and the average F-measure was 0.928. We further reported the impact
of different combinations of features extracted from structured and/or
unstructured data on the performance of the deep learning models.
Softmax Optimizations for Intel Xeon Processor-based Platforms
Authors: Jacek Czaja, Michal Gallus, Tomasz Patejko, Jian Tang
Date: 2019-04-28 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Softmax is popular normalization method used in machine learning. Deep learning solutions like Transformer or BERT use the softmax function intensively, so it is worthwhile to optimize its performance. This article presents our methodology of optimization and its results applied to softmax. By presenting this methodology, we hope to increase an interest in deep learning optimizations for CPUs. We believe that the optimization process presented here could be transferred to other deep learning frameworks such as TensorFlow or PyTorch.
Counterexample-Driven Synthesis for Probabilistic Program Sketches
Authors: Milan Češka, Christian Hensel, Sebastian Junges, Joost-Pieter Katoen
Date: 2019-04-28 00:00:00

Probabilistic programs are key to deal with uncertainty in e.g. controller
synthesis. They are typically small but intricate. Their development is complex
and error prone requiring quantitative reasoning over a myriad of alternative
designs. To mitigate this complexity, we adopt counterexample-guided inductive
synthesis (CEGIS) to automatically synthesise finite-state probabilistic
programs. Our approach leverages efficient model checking, modern SMT solving,
and counterexample generation at program level. Experiments on practically
relevant case studies show that design spaces with millions of candidate
designs can be fully explored using a few thousand verification queries.
Low-Rank Principal Eigenmatrix Analysis
Authors: Krishna Balasubramanian, Elynn Y. Chen, Jianqing Fan, Xiang Wu
Date: 2019-04-28 00:00:00

Sparse PCA is a widely used technique for high-dimensional data analysis. In
this paper, we propose a new method called low-rank principal eigenmatrix
analysis. Different from sparse PCA, the dominant eigenvectors are allowed to
be dense but are assumed to have a low-rank structure when matricized
appropriately. Such a structure arises naturally in several practical cases:
Indeed the top eigenvector of a circulant matrix, when matricized appropriately
is a rank-1 matrix. We propose a matricized rank-truncated power method that
could be efficiently implemented and establish its computational and
statistical properties. Extensive experiments on several synthetic data sets
demonstrate the competitive empirical performance of our method.
Towards Efficient Model Compression via Learned Global Ranking
Authors: Ting-Wu Chin, Ruizhou Ding, Cha Zhang, Diana Marculescu
Date: 2019-04-28 00:00:00
Proceeding: CVPR 2020 6
Tasks: Model Compression

Pruning convolutional filters has demonstrated its effectiveness in compressing ConvNets. Prior art in filter pruning requires users to specify a target model complexity (e.g., model size or FLOP count) for the resulting architecture. However, determining a target model complexity can be difficult for optimizing various embodied AI applications such as autonomous robots, drones, and user-facing applications. First, both the accuracy and the speed of ConvNets can affect the performance of the application. Second, the performance of the application can be hard to assess without evaluating ConvNets during inference. As a consequence, finding a sweet-spot between the accuracy and speed via filter pruning, which needs to be done in a trial-and-error fashion, can be time-consuming. This work takes a first step toward making this process more efficient by altering the goal of model compression to producing a set of ConvNets with various accuracy and latency trade-offs instead of producing one ConvNet targeting some pre-defined latency constraint. To this end, we propose to learn a global ranking of the filters across different layers of the ConvNet, which is used to obtain a set of ConvNet architectures that have different accuracy/latency trade-offs by pruning the bottom-ranked filters. Our proposed algorithm, LeGR, is shown to be 2x to 3x faster than prior work while having comparable or better performance when targeting seven pruned ResNet-56 with different accuracy/FLOPs profiles on the CIFAR-100 dataset. Additionally, we have evaluated LeGR on ImageNet and Bird-200 with ResNet-50 and MobileNetV2 to demonstrate its effectiveness. Code available at https://github.com/cmu-enyac/LeGR.