pwc-archive/papers-with-abstracts huggingface dataset:
The CAPIO 2017 Conversational Speech Recognition System
Authors: Kyu J. Han, Akshay Chandrashekaran, Jungsuk Kim, Ian Lane
Date: 2017-12-29 00:00:00
Tasks: image-classification, Image Classification, speech-recognition, Speech Recognition

In this paper we show how we have achieved the state-of-the-art performance
on the industry-standard NIST 2000 Hub5 English evaluation set. We explore
densely connected LSTMs, inspired by the densely connected convolutional
networks recently introduced for image classification tasks. We also propose an
acoustic model adaptation scheme that simply averages the parameters of a seed
neural network acoustic model and its adapted version. This method was applied
with the CallHome training corpus and improved individual system performances
by on average 6.1% (relative) against the CallHome portion of the evaluation
set with no performance loss on the Switchboard portion. With RNN-LM rescoring
and lattice combination on the 5 systems trained across three different phone
sets, our 2017 speech recognition system has obtained 5.0% and 9.1% on
Switchboard and CallHome, respectively, both of which are the best word error
rates reported thus far. According to IBM in their latest work to compare human
and machine transcriptions, our reported Switchboard word error rate can be
considered to surpass the human parity (5.1%) of transcribing conversational
telephone speech.
Recurrent Neural Networks for Person Re-identification Revisited
Authors: Jean-Baptiste Boin, Andre Araujo, Bernd Girod
Date: 2018-04-10 00:00:00
Tasks: Person Re-Identification

The task of person re-identification has recently received rising attention
due to the high performance achieved by new methods based on deep learning. In
particular, in the context of video-based re-identification, many
state-of-the-art works have explored the use of Recurrent Neural Networks
(RNNs) to process input sequences. In this work, we revisit this tool by
deriving an approximation which reveals the small effect of recurrent
connections, leading to a much simpler feed-forward architecture. Using the
same parameters as the recurrent version, our proposed feed-forward
architecture obtains very similar accuracy. More importantly, our model can be
combined with a new training process to significantly improve re-identification
performance. Our experiments demonstrate that the proposed models converge
substantially faster than recurrent ones, with accuracy improvements by up to
5% on two datasets. The performance achieved is better or on par with other
RNN-based person re-identification techniques.
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer
Authors: Milad Zafar Nezhad, Najibesadat Sadati, Kai Yang, Dongxiao Zhu
Date: 2018-04-10 00:00:00
Tasks: Active Learning, Survival Analysis

Survival analysis has been developed and applied in the number of areas
including manufacturing, finance, economics and healthcare. In healthcare
domain, usually clinical data are high-dimensional, sparse and complex and
sometimes there exists few amount of time-to-event (labeled) instances.
Therefore building an accurate survival model from electronic health records is
challenging. With this motivation, we address this issue and provide a new
survival analysis framework using deep learning and active learning with a
novel sampling strategy. First, our approach provides better representation
with lower dimensions from clinical features using labeled (time-to-event) and
unlabeled (censored) instances and then actively trains the survival model by
labeling the censored data using an oracle. As a clinical assistive tool, we
introduce a simple effective treatment recommendation approach based on our
survival model. In the experimental study, we apply our approach on
SEER-Medicare data related to prostate cancer among African-Americans and white
patients. The results indicate that our approach outperforms significantly than
baseline models.
On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization
Authors: Pin-Yu Chen, Dennis Wei
Date: 2018-04-09 00:00:00
Tasks: Community Detection, General Classification

Active graph-based semi-supervised learning (AG-SSL) aims to select a small
set of labeled examples and utilize their graph-based relation to other
unlabeled examples to aid in machine learning tasks. It is also closely related
to the sampling theory in graph signal processing. In this paper, we revisit
the original formulation of graph-based SSL and prove the supermodularity of an
AG-SSL objective function under a broad class of regularization functions
parameterized by Stieltjes matrices. Under this setting, supermodularity yields
a novel greedy label sampling algorithm with guaranteed performance relative to
the optimal sampling set. Compared to three state-of-the-art graph signal
sampling and recovery methods on two real-life community detection datasets,
the proposed AG-SSL method attains superior classification accuracy given
limited sample budgets.
Fine-grained Activity Recognition in Baseball Videos
Authors: AJ Piergiovanni, Michael S. Ryoo
Date: 2018-04-09 00:00:00
Tasks: Action Detection, Activity Detection, Activity Recognition, General Classification, Video Classification

In this paper, we introduce a challenging new dataset, MLB-YouTube, designed
for fine-grained activity detection. The dataset contains two settings:
segmented video classification as well as activity detection in continuous
videos. We experimentally compare various recognition approaches capturing
temporal structure in activity videos, by classifying segmented videos and
extending those approaches to continuous videos. We also compare models on the
extremely difficult task of predicting pitch speed and pitch type from
broadcast baseball videos. We find that learning temporal structure is valuable
for fine-grained activity recognition.
Building Function Approximators on top of Haar Scattering Networks
Authors: Fernando Fernandes Neto
Date: 2018-04-09 00:00:00
Tasks: Econometrics

In this article we propose building general-purpose function approximators on
top of Haar Scattering Networks. We advocate that this architecture enables a
better comprehension of feature extraction, in addition to its implementation
simplicity and low computational costs. We show its approximation and feature
extraction capabilities in a wide range of different problems, which can be
applied on several phenomena in signal processing, system identification,
econometrics and other potential fields.
Large scale distributed neural network training through online distillation
Authors: Rohan Anil, Gabriel Pereyra, Alexandre Passos, Robert Ormandi, George E. Dahl, Geoffrey E. Hinton
Date: 2018-04-09 00:00:00
Proceeding: ICLR 2018 1
Tasks: Language Modeling, Language Modelling

Techniques such as ensembling and distillation promise model quality improvements when paired with almost any base model. However, due to increased test-time cost (for ensembles) and increased complexity of the training pipeline (for distillation), these techniques are challenging to use in industrial settings. In this paper we explore a variant of distillation which is relatively straightforward to use as it does not require a complicated multi-stage setup or many new hyperparameters. Our first claim is that online distillation enables us to use extra parallelism to fit very large datasets about twice as fast. Crucially, we can still speed up training even after we have already reached the point at which additional parallelism provides no benefit for synchronous or asynchronous stochastic gradient descent. Two neural networks trained on disjoint subsets of the data can share knowledge by encouraging each model to agree with the predictions the other model would have made. These predictions can come from a stale version of the other model so they can be safely computed using weights that only rarely get transmitted. Our second claim is that online distillation is a cost-effective way to make the exact predictions of a model dramatically more reproducible. We support our claims using experiments on the Criteo Display Ad Challenge dataset, ImageNet, and the largest to-date dataset used for neural language modeling, containing $6\times 10^{11}$ tokens and based on the Common Crawl repository of web data.
A Neural Multi-sequence Alignment TeCHnique (NeuMATCH)
Authors: Pelin Dogan, Boyang Li, Leonid Sigal, Markus Gross
Date: 2018-02-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: Dynamic Time Warping

The alignment of heterogeneous sequential data (video to text) is an
important and challenging problem. Standard techniques for this task, including
Dynamic Time Warping (DTW) and Conditional Random Fields (CRFs), suffer from
inherent drawbacks. Mainly, the Markov assumption implies that, given the
immediate past, future alignment decisions are independent of further history.
The separation between similarity computation and alignment decision also
prevents end-to-end training. In this paper, we propose an end-to-end neural
architecture where alignment actions are implemented as moving data between
stacks of Long Short-term Memory (LSTM) blocks. This flexible architecture
supports a large variety of alignment tasks, including one-to-one, one-to-many,
skipping unmatched elements, and (with extensions) non-monotonic alignment.
Extensive experiments on semi-synthetic and real datasets show that our
algorithm outperforms state-of-the-art baselines.
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications
Authors: Tien-Ju Yang, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, Hartwig Adam
Date: 2018-04-09 00:00:00
Proceeding: ECCV 2018 9
Tasks: CPU, GPU, image-classification, Image Classification

This work proposes an algorithm, called NetAdapt, that automatically adapts a
pre-trained deep neural network to a mobile platform given a resource budget.
While many existing algorithms simplify networks based on the number of MACs or
weights, optimizing those indirect metrics may not necessarily reduce the
direct metrics, such as latency and energy consumption. To solve this problem,
NetAdapt incorporates direct metrics into its adaptation algorithm. These
direct metrics are evaluated using empirical measurements, so that detailed
knowledge of the platform and toolchain is not required. NetAdapt automatically
and progressively simplifies a pre-trained network until the resource budget is
met while maximizing the accuracy. Experiment results show that NetAdapt
achieves better accuracy versus latency trade-offs on both mobile CPU and
mobile GPU, compared with the state-of-the-art automated network simplification
algorithms. For image classification on the ImageNet dataset, NetAdapt achieves
up to a 1.7$\times$ speedup in measured inference latency with equal or higher
accuracy on MobileNets (V1&V2).
Learning from partial correction
Authors: Sanjoy Dasgupta, Michael Luby
Date: 2017-05-23 00:00:00
Tasks: Generalization Bounds

We introduce a new model of interactive learning in which an expert examines
the predictions of a learner and partially fixes them if they are wrong.
Although this kind of feedback is not i.i.d., we show statistical
generalization bounds on the quality of the learned model.
Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions
Authors: Javad Fotouhi, Mathias Unberath, Giacomo Taylor, Arash Ghaani Farashahi, Bastian Bier, Russell H. Taylor, Greg M. Osgood, M. D., Mehran Armand, Nassir Navab
Date: 2018-04-09 00:00:00
Tasks: Anatomy, Image Augmentation

In unilateral pelvic fracture reductions, surgeons attempt to reconstruct the
bone fragments such that bilateral symmetry in the bony anatomy is restored. We
propose to exploit this "structurally symmetric" nature of the pelvic bone, and
provide intra-operative image augmentation to assist the surgeon in repairing
dislocated fragments. The main challenge is to automatically estimate the
desired plane of symmetry within the patient's pre-operative CT. We propose to
estimate this plane using a non-linear optimization strategy, by minimizing
Tukey's biweight robust estimator, relying on the partial symmetry of the
anatomy. Moreover, a regularization term is designed to enforce the similarity
of bone density histograms on both sides of this plane, relying on the
biological fact that, even if injured, the dislocated bone segments remain
within the body. The experimental results demonstrate the performance of the
proposed method in estimating this "plane of partial symmetry" using CT images
of both healthy and injured anatomy. Examples of unilateral pelvic fractures
are used to show how intra-operative X-ray images could be augmented with the
forward-projections of the mirrored anatomy, acting as objective road-map for
fracture reduction procedures.
Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
Authors: Pete Warden
Date: 2018-04-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Keyword Spotting, Speech Recognition

Describes an audio dataset of spoken words designed to help train and
evaluate keyword spotting systems. Discusses why this task is an interesting
challenge, and why it requires a specialized dataset that is different from
conventional datasets used for automatic speech recognition of full sentences.
Suggests a methodology for reproducible and comparable accuracy metrics for
this task. Describes how the data was collected and verified, what it contains,
previous versions and properties. Concludes by reporting baseline results of
models trained on this dataset.
Assessment of Breast Cancer Histology using Densely Connected Convolutional Networks
Authors: Matthias Kohl, Christoph Walz, Florian Ludwig, Stefan Braunewell, Maximilian Baust
Date: 2018-04-09 00:00:00
Tasks: General Classification, image-classification, Image Classification, Image Segmentation, Semantic Segmentation, Transfer Learning

Breast cancer is the most frequently diagnosed cancer and leading cause of
cancer-related death among females worldwide. In this article, we investigate
the applicability of densely connected convolutional neural networks to the
problems of histology image classification and whole slide image segmentation
in the area of computer-aided diagnoses for breast cancer. To this end, we
study various approaches for transfer learning and apply them to the data set
from the 2018 grand challenge on breast cancer histology images (BACH).
Deep Learning Classification of Polygenic Obesity using Genome Wide Association Study SNPs
Authors: Casimiro Adays Curbelo Montañez, Paul Fergus, Almudena Curbelo Montañez, Carl Chalmers
Date: 2018-04-09 00:00:00
Tasks: Deep Learning, Disease Prediction, General Classification

In this paper, association results from genome-wide association studies
(GWAS) are combined with a deep learning framework to test the predictive
capacity of statistically significant single nucleotide polymorphism (SNPs)
associated with obesity phenotype. Our approach demonstrates the potential of
deep learning as a powerful framework for GWAS analysis that can capture
information about SNPs and the important interactions between them. Basic
statistical methods and techniques for the analysis of genetic SNP data from
population-based genome-wide studies have been considered. Statistical
association testing between individual SNPs and obesity was conducted under an
additive model using logistic regression. Four subsets of loci after
quality-control (QC) and association analysis were selected: P-values lower
than 1x10-5 (5 SNPs), 1x10-4 (32 SNPs), 1x10-3 (248 SNPs) and 1x10-2 (2465
SNPs). A deep learning classifier is initialised using these sets of SNPs and
fine-tuned to classify obese and non-obese observations. Using a deep learning
classifier model and genetic variants with P-value < 1x10-2 (2465 SNPs) it was
possible to obtain results (SE=0.9604, SP=0.9712, Gini=0.9817, LogLoss=0.1150,
AUC=0.9908 and MSE=0.0300). As the P-value increased, an evident deterioration
in performance was observed. Results demonstrate that single SNP analysis fails
to capture the cumulative effect of less significant variants and their overall
contribution to the outcome in disease prediction, which is captured using a
deep learning framework.
Human-Guided Data Exploration
Authors: Andreas Henelius, Emilia Oikarinen, Kai Puolamäki
Date: 2018-04-09 00:00:00

The outcome of the explorative data analysis (EDA) phase is vital for
successful data analysis. EDA is more effective when the user interacts with
the system used to carry out the exploration. In the recently proposed paradigm
of iterative data mining the user controls the exploration by inputting
knowledge in the form of patterns observed during the process. The system then
shows the user views of the data that are maximally informative given the
user's current knowledge. Although this scheme is good at showing surprising
views of the data to the user, there is a clear shortcoming: the user cannot
steer the process. In many real cases we want to focus on investigating
specific questions concerning the data. This paper presents the Human Guided
Data Exploration framework, generalising previous research. This framework
allows the user to incorporate existing knowledge into the exploration process,
focus on exploring a subset of the data, and compare different complex
hypotheses concerning relations in the data. The framework utilises a
computationally efficient constrained randomisation scheme. To showcase the
framework, we developed a free open-source tool, using which the empirical
evaluation on real-world datasets was carried out. Our evaluation shows that
the ability to focus on particular subsets and being able to compare hypotheses
are important additions to the interactive iterative data mining process.
An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks
Authors: Pu Zhao, Sijia Liu, Yanzhi Wang, Xue Lin
Date: 2018-04-09 00:00:00
Tasks: Adversarial Attack

Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That
is, adversarial examples, obtained by adding delicately crafted distortions
onto original legal inputs, can mislead a DNN to classify them as any target
labels. In a successful adversarial attack, the targeted mis-classification
should be achieved with the minimal distortion added. In the literature, the
added distortions are usually measured by L0, L1, L2, and L infinity norms,
namely, L0, L1, L2, and L infinity attacks, respectively. However, there lacks
a versatile framework for all types of adversarial attacks.
  This work for the first time unifies the methods of generating adversarial
examples by leveraging ADMM (Alternating Direction Method of Multipliers), an
operator splitting optimization approach, such that L0, L1, L2, and L infinity
attacks can be effectively implemented by this general framework with little
modifications. Comparing with the state-of-the-art attacks in each category,
our ADMM-based attacks are so far the strongest, achieving both the 100% attack
success rate and the minimal distortion.
Studying the Effects of Deep Brain Stimulation and Medication on the Dynamics of STN-LFP Signals for Human Behavior Analysis
Authors: Hosein M. Golshan, Adam O. Hebb, Joshua Nedrud, Mohammad H. Mahoor
Date: 2018-04-09 00:00:00

This paper presents the results of our recent work on studying the effects of
deep brain stimulation (DBS) and medication on the dynamics of brain local
field potential (LFP) signals used for behavior analysis of patients with
Parkinson s disease (PD). DBS is a technique used to alleviate the severe
symptoms of PD when pharmacotherapy is not very effective. Behavior recognition
from the LFP signals recorded from the subthalamic nucleus (STN) has
application in developing closed-loop DBS systems, where the stimulation pulse
is adaptively generated according to subjects performing behavior. Most of the
existing studies on behavior recognition that use STN-LFPs are based on the DBS
being off. This paper discovers how the performance and accuracy of automated
behavior recognition from the LFP signals are affected under different
paradigms of stimulation on/off. We first study the notion of beta power
suppression in LFP signals under different scenarios (stimulation on/off and
medication on/off). Afterward, we explore the accuracy of support vector
machines in predicting human actions (button press and reach) using the
spectrogram of STN-LFP signals. Our experiments on the recorded LFP signals of
three subjects confirm that the beta power is suppressed significantly when the
patients take medication (p-value<0.002) or stimulation (p-value<0.0003). The
results also show that we can classify different behaviors with a reasonable
accuracy of 85% even when the high-amplitude stimulation is applied.
CIoTA: Collaborative IoT Anomaly Detection via Blockchain
Authors: Tomer Golomb, Yisroel Mirsky, Yuval Elovici
Date: 2018-03-10 00:00:00
Tasks: Anomaly Detection

Due to their rapid growth and deployment, Internet of things (IoT) devices
have become a central aspect of our daily lives. However, they tend to have
many vulnerabilities which can be exploited by an attacker. Unsupervised
techniques, such as anomaly detection, can help us secure the IoT devices.
However, an anomaly detection model must be trained for a long time in order to
capture all benign behaviors. This approach is vulnerable to adversarial
attacks since all observations are assumed to be benign while training the
anomaly detection model.
  In this paper, we propose CIoTA, a lightweight framework that utilizes the
blockchain concept to perform distributed and collaborative anomaly detection
for devices with limited resources. CIoTA uses blockchain to incrementally
update a trusted anomaly detection model via self-attestation and consensus
among IoT devices. We evaluate CIoTA on our own distributed IoT simulation
platform, which consists of 48 Raspberry Pis, to demonstrate CIoTA's ability to
enhance the security of each device and the security of the network as a whole.
Smart, Sparse Contours to Represent and Edit Images
Authors: Tali Dekel, Chuang Gan, Dilip Krishnan, Ce Liu, William T. Freeman
Date: 2017-12-21 00:00:00
Tasks: Face Recognition, Image Manipulation

We study the problem of reconstructing an image from information stored at
contour locations. We show that high-quality reconstructions with high fidelity
to the source image can be obtained from sparse input, e.g., comprising less
than $6\%$ of image pixels. This is a significant improvement over existing
contour-based reconstruction methods that require much denser input to capture
subtle texture information and to ensure image quality. Our model, based on
generative adversarial networks, synthesizes texture and details in regions
where no input information is provided. The semantic knowledge encoded into our
model and the sparsity of the input allows to use contours as an intuitive
interface for semantically-aware image manipulation: local edits in contour
domain translate to long-range and coherent changes in pixel space. We can
perform complex structural changes such as changing facial expression by simple
edits of contours. Our experiments demonstrate that humans as well as a face
recognition system mostly cannot distinguish between our reconstructions and
the source images.