pwc-archive/papers-with-abstracts huggingface dataset:
Ubiquitous Acoustic Sensing on Commodity IoT Devices: A Survey
Authors: Chao Cai, Rong Zheng, Jun Luo
Date: 2019-01-11 00:00:00
Tasks: Survey

With the proliferation of Internet-of-Things devices, acoustic sensing attracts much attention in recent years. It exploits acoustic transceivers such as microphones and speakers beyond their primary functions, namely recording and playing, to enable novel applications and new user experiences. In this paper, we present the first systematic survey of recent advances in active acoustic sensing using commodity hardware with a frequency range below 24~\!kHz. We propose a general framework that categorizes main building blocks of acoustic sensing systems. This framework encompasses three layers, i.e., physical layer, core technique layer, and application layer. The physical layer includes basic hardware components, acoustic platforms as well as the air-borne and structure-borne channel characteristics. The core technique layer encompasses key mechanisms to generate acoustic signals (waveforms) and to extract useful temporal, spatial and spectral information from received signals. The application layer builds upon the functions offered by the core techniques to realize different acoustic sensing applications. We highlight unique challenges due to the limitations of physical devices and acoustic channels and how they are mitigated or overcame by core processing techniques and application-specific solutions. Finally, research opportunities and future directions are discussed to spawn further in-depth investigation on acoustic sensing.
Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture
Authors: Ning Yu, Connelly Barnes, Eli Shechtman, Sohrab Amirghodsi, Michal Lukac
Date: 2019-01-11 00:00:00
Proceeding: CVPR 2019 6

This paper addresses the problem of interpolating visual textures. We
formulate this problem by requiring (1) by-example controllability and (2)
realistic and smooth interpolation among an arbitrary number of texture
samples. To solve it we propose a neural network trained simultaneously on a
reconstruction task and a generation task, which can project texture examples
onto a latent space where they can be linearly interpolated and projected back
onto the image domain, thus ensuring both intuitive control and realistic
results. We show our method outperforms a number of baselines according to a
comprehensive suite of metrics as well as a user study. We further show several
applications based on our technique, which include texture brush, texture
dissolve, and animal hybridization.
Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors
Authors: Tong He, Stefano Soatto
Date: 2019-01-11 00:00:00
Tasks: Sensitivity, vehicle detection

We present a method to infer 3D pose and shape of vehicles from a single
image. To tackle this ill-posed problem, we optimize two-scale projection
consistency between the generated 3D hypotheses and their 2D
pseudo-measurements. Specifically, we use a morphable wireframe model to
generate a fine-scaled representation of vehicle shape and pose. To reduce its
sensitivity to 2D landmarks, we jointly model the 3D bounding box as a coarse
representation which improves robustness. We also integrate three task priors,
including unsupervised monocular depth, a ground plane constraint as well as
vehicle shape priors, with forward projection errors into an overall energy
function.
Undirected Graphical Models as Approximate Posteriors
Authors: Arash Vahdat, Evgeny Andriyash, William G. Macready
Date: 2019-01-11 00:00:00
Proceeding: ICML 2020 1
Tasks: Bayesian Inference

The representation of the approximate posterior is a critical aspect of effective variational autoencoders (VAEs). Poor choices for the approximate posterior have a detrimental impact on the generative performance of VAEs due to the mismatch with the true posterior. We extend the class of posterior models that may be learned by using undirected graphical models. We develop an efficient method to train undirected approximate posteriors by showing that the gradient of the training objective with respect to the parameters of the undirected posterior can be computed by backpropagation through Markov chain Monte Carlo updates. We apply these gradient estimators for training discrete VAEs with Boltzmann machines as approximate posteriors and demonstrate that undirected models outperform previous results obtained using directed graphical models. Our implementation is available at https://github.com/QuadrantAI/dvaess .
Linguistic Analysis of Pretrained Sentence Encoders with Acceptability Judgments
Authors: Alex Warstadt, Samuel R. Bowman
Date: 2019-01-11 00:00:00
Tasks: CoLA, General Classification, Linguistic Acceptability, Sentence

Recent work on evaluating grammatical knowledge in pretrained sentence encoders gives a fine-grained view of a small number of phenomena. We introduce a new analysis dataset that also has broad coverage of linguistic phenomena. We annotate the development set of the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018) for the presence of 13 classes of syntactic phenomena including various forms of argument alternations, movement, and modification. We use this analysis set to investigate the grammatical knowledge of three pretrained encoders: BERT (Devlin et al., 2018), GPT (Radford et al., 2018), and the BiLSTM baseline from Warstadt et al. We find that these models have a strong command of complex or non-canonical argument structures like ditransitives (Sue gave Dan a book) and passives (The book was read). Sentences with long distance dependencies like questions (What do you think I ate?) challenge all models, but for these, BERT and GPT have a distinct advantage over the baseline. We conclude that recent sentence encoders, despite showing near-human performance on acceptability classification overall, still fail to make fine-grained grammaticality distinctions for many complex syntactic structures.
On the Turing Completeness of Modern Neural Network Architectures
Authors: Jorge Pérez, Javier Marinković, Pablo Barceló
Date: 2019-01-10 00:00:00
Proceeding: ICLR 2019 5
Tasks: GPU

Alternatives to recurrent neural networks, in particular, architectures based
on attention or convolutions, have been gaining momentum for processing input
sequences. In spite of their relevance, the computational properties of these
alternatives have not yet been fully explored. We study the computational power
of two of the most paradigmatic architectures exemplifying these mechanisms:
the Transformer (Vaswani et al., 2017) and the Neural GPU (Kaiser & Sutskever,
2016). We show both models to be Turing complete exclusively based on their
capacity to compute and access internal dense representations of the data. In
particular, neither the Transformer nor the Neural GPU requires access to an
external memory to become Turing complete. Our study also reveals some minimal
sets of elements needed to obtain these completeness results.
How Can We Make GAN Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach
Authors: Jin Zhu, Guang Yang, Pietro Lio
Date: 2019-01-10 00:00:00
Tasks: Generative Adversarial Network, Image Super-Resolution, Super-Resolution

Single image super-resolution (SISR) is of great importance as a low-level
computer vision task. The fast development of Generative Adversarial Network
(GAN) based deep learning architectures realises an efficient and effective
SISR to boost the spatial resolution of natural images captured by digital
cameras. However, the SISR for medical images is still a very challenging
problem. This is due to (1) compared to natural images, in general, medical
images have lower signal to noise ratios, (2) GAN based models pre-trained on
natural images may synthesise unrealistic patterns in medical images which
could affect the clinical interpretation and diagnosis, and (3) the vanilla GAN
architecture may suffer from unstable training and collapse mode that can also
affect the SISR results. In this paper, we propose a novel lesion focused SR
(LFSR) method, which incorporates GAN to achieve perceptually realistic SISR
results for brain tumour MRI images. More importantly, we test and make
comparison using recently developed GAN variations, e.g., Wasserstein GAN
(WGAN) and WGAN with Gradient Penalty (WGAN-GP), and propose a novel
multi-scale GAN (MS-GAN), to achieve a more stabilised and efficient training
and improved perceptual quality of the super-resolved results. Based on both
quantitative evaluations and our designed mean opinion score, the proposed LFSR
coupled with MS-GAN has performed better in terms of both perceptual quality
and efficiency.
Preventing Posterior Collapse with delta-VAEs
Authors: Ali Razavi, Aäron van den Oord, Ben Poole, Oriol Vinyals
Date: 2019-01-10 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Image Generation, Representation Learning

Due to the phenomenon of "posterior collapse," current latent variable
generative models pose a challenging design choice that either weakens the
capacity of the decoder or requires augmenting the objective so it does not
only maximize the likelihood of the data. In this paper, we propose an
alternative that utilizes the most powerful generative models as decoders,
whilst optimising the variational lower bound all while ensuring that the
latent variables preserve and encode useful information. Our proposed
$\delta$-VAEs achieve this by constraining the variational family for the
posterior to have a minimum distance to the prior. For sequential latent
variable models, our approach resembles the classic representation learning
approach of slow feature analysis. We demonstrate the efficacy of our approach
at modeling text on LM1B and modeling images: learning representations,
improving sample quality, and achieving state of the art log-likelihood on
CIFAR-10 and ImageNet $32\times 32$.
Context Aware Machine Learning
Authors: Yun Zeng
Date: 2019-01-10 00:00:00
Tasks: BIG-bench Machine Learning, Sentence, Sentence Embedding, Sentence-Embedding

We propose a principle for exploring context in machine learning models.
Starting with a simple assumption that each observation may or may not depend
on its context, a conditional probability distribution is decomposed into two
parts: context-free and context-sensitive. Then by employing the log-linear
word production model for relating random variables to their embedding space
representation and making use of the convexity of natural exponential function,
we show that the embedding of an observation can also be decomposed into a
weighted sum of two vectors, representing its context-free and
context-sensitive parts, respectively. This simple treatment of context
provides a unified view of many existing deep learning models, leading to
revisions of these models able to achieve significant performance boost.
Specifically, our upgraded version of a recent sentence embedding model not
only outperforms the original one by a large margin, but also leads to a new,
principled approach for compositing the embeddings of bag-of-words features, as
well as a new architecture for modeling attention in deep neural networks. More
surprisingly, our new principle provides a novel understanding of the gates and
equations defined by the long short term memory model, which also leads to a
new model that is able to converge significantly faster and achieve much lower
prediction errors. Furthermore, our principle also inspires a new type of
generic neural network layer that better resembles real biological neurons than
the traditional linear mapping plus nonlinear activation based architecture.
Its multi-layer extension provides a new principle for deep neural networks
which subsumes residual network (ResNet) as its special case, and its extension
to convolutional neutral network model accounts for irrelevant input (e.g.,
background in an image) in addition to filtering.
Deep Learning for Anomaly Detection: A Survey
Authors: Raghavendra Chalapathy, Sanjay Chawla
Date: 2019-01-10 00:00:00
Tasks: Anomaly Detection, Deep Learning, Survey

Anomaly detection is an important problem that has been well-studied within
diverse research areas and application domains. The aim of this survey is
two-fold, firstly we present a structured and comprehensive overview of
research methods in deep learning-based anomaly detection. Furthermore, we
review the adoption of these methods for anomaly across various application
domains and assess their effectiveness. We have grouped state-of-the-art
research techniques into different categories based on the underlying
assumptions and approach adopted. Within each category we outline the basic
anomaly detection technique, along with its variants and present key
assumptions, to differentiate between normal and anomalous behavior. For each
category, we present we also present the advantages and limitations and discuss
the computational complexity of the techniques in real application domains.
Finally, we outline open issues in research and challenges faced while adopting
these techniques.
Mean Estimation from One-Bit Measurements
Authors: Alon Kipnis, John C. Duchi
Date: 2019-01-10 00:00:00
Tasks: Quantization

We consider the problem of estimating the mean of a symmetric log-concave distribution under the constraint that only a single bit per sample from this distribution is available to the estimator. We study the mean squared error as a function of the sample size (and hence the number of bits). We consider three settings: first, a centralized setting, where an encoder may release $n$ bits given a sample of size $n$, and for which there is no asymptotic penalty for quantization; second, an adaptive setting in which each bit is a function of the current observation and previously recorded bits, where we show that the optimal relative efficiency compared to the sample mean is precisely the efficiency of the median; lastly, we show that in a distributed setting where each bit is only a function of a local sample, no estimator can achieve optimal efficiency uniformly over the parameter space. We additionally complement our results in the adaptive setting by showing that \emph{one} round of adaptivity is sufficient to achieve optimal mean-square error.
Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification
Authors: Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Date: 2019-01-10 00:00:00
Tasks: BIG-bench Machine Learning, Object Recognition

The phenomenon of Adversarial Examples is attracting increasing interest from
the Machine Learning community, due to its significant impact to the security
of Machine Learning systems. Adversarial examples are similar (from a
perceptual notion of similarity) to samples from the data distribution, that
"fool" a machine learning classifier. For computer vision applications, these
are images with carefully crafted but almost imperceptible changes, that are
misclassified. In this work, we characterize this phenomenon under an existing
taxonomy of threats to biometric systems, in particular identifying new attacks
for Offline Handwritten Signature Verification systems. We conducted an
extensive set of experiments on four widely used datasets: MCYT-75, CEDAR,
GPDS-160 and the Brazilian PUC-PR, considering both a CNN-based system and a
system using a handcrafted feature extractor (CLBP). We found that attacks that
aim to get a genuine signature rejected are easy to generate, even in a limited
knowledge scenario, where the attacker does not have access to the trained
classifier nor the signatures used for training. Attacks that get a forgery to
be accepted are harder to produce, and often require a higher level of noise -
in most cases, no longer "imperceptible" as previous findings in object
recognition. We also evaluated the impact of two countermeasures on the success
rate of the attacks and the amount of noise required for generating successful
attacks.
Unsupervised Moving Object Detection via Contextual Information Separation
Authors: Yanchao Yang, Antonio Loquercio, Davide Scaramuzza, Stefano Soatto
Date: 2019-01-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Moving Object Detection, Object, object-detection, Object Detection, Optical Flow Estimation, Unsupervised Video Object Segmentation

We propose an adversarial contextual model for detecting moving objects in
images. A deep neural network is trained to predict the optical flow in a
region using information from everywhere else but that region (context), while
another network attempts to make such context as uninformative as possible. The
result is a model where hypotheses naturally compete with no need for explicit
regularization or hyper-parameter tuning. Although our method requires no
supervision whatsoever, it outperforms several methods that are pre-trained on
large annotated datasets. Our model can be thought of as a generalization of
classical variational generative region-based segmentation, but in a way that
avoids explicit regularization or solution of partial differential equations at
run-time.
No-Regret Bayesian Optimization with Unknown Hyperparameters
Authors: Felix Berkenkamp, Angela P. Schoellig, Andreas Krause
Date: 2019-01-10 00:00:00
Tasks: Bayesian Optimization

Bayesian optimization (BO) based on Gaussian process models is a powerful
paradigm to optimize black-box functions that are expensive to evaluate. While
several BO algorithms provably converge to the global optimum of the unknown
function, they assume that the hyperparameters of the kernel are known in
advance. This is not the case in practice and misspecification often causes
these algorithms to converge to poor local optima. In this paper, we present
the first BO algorithm that is provably no-regret and converges to the optimum
without knowledge of the hyperparameters. During optimization we slowly adapt
the hyperparameters of stationary kernels and thereby expand the associated
function class over time, so that the BO algorithm considers more complex
function candidates. Based on the theoretical insights, we propose several
practical algorithms that achieve the empirical sample efficiency of BO with
online hyperparameter estimation, but retain theoretical convergence
guarantees. We evaluate our method on several benchmark problems.
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
Authors: Cheng-Yang Fu, Mykhailo Shvets, Alexander C. Berg
Date: 2019-01-10 00:00:00
Tasks: Object Detection

Recently two-stage detectors have surged ahead of single-shot detectors in
the accuracy-vs-speed trade-off. Nevertheless single-shot detectors are
immensely popular in embedded vision applications. This paper brings
single-shot detectors up to the same level as current two-stage techniques. We
do this by improving training for the state-of-the-art single-shot detector,
RetinaNet, in three ways: integrating instance mask prediction for the first
time, making the loss function adaptive and more stable, and including
additional hard examples in training. We call the resulting augmented network
RetinaMask. The detection component of RetinaMask has the same computational
cost as the original RetinaNet, but is more accurate. COCO test-dev results are
up to 41.4 mAP for RetinaMask-101 vs 39.1mAP for RetinaNet-101, while the
runtime is the same during evaluation. Adding Group Normalization increases the
performance of RetinaMask-101 to 41.7 mAP. Code is
at:https://github.com/chengyangfu/retinamask
A New Tensioning Method using Deep Reinforcement Learning for Surgical Pattern Cutting
Authors: Thanh Thi Nguyen, Ngoc Duy Nguyen, Fernando Bello, Saeid Nahavandi
Date: 2019-01-10 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Surgeons normally need surgical scissors and tissue grippers to cut through a
deformable surgical tissue. The cutting accuracy depends on the skills to
manipulate these two tools. Such skills are part of basic surgical skills
training as in the Fundamentals of Laparoscopic Surgery. The gripper is used to
pinch a point on the surgical sheet and pull the tissue to a certain direction
to maintain the tension while the scissors cut through a trajectory. As the
surgical materials are deformable, it requires a comprehensive tensioning
policy to yield appropriate tensioning direction at each step of the cutting
process. Automating a tensioning policy for a given cutting trajectory will
support not only the human surgeons but also the surgical robots to improve the
cutting accuracy and reliability. This paper presents a multiple pinch point
approach to modelling an autonomous tensioning planner based on a deep
reinforcement learning algorithm. Experiments on a simulator show that the
proposed method is superior to existing methods in terms of both performance
and robustness.
High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort
Authors: Rahman Attar, Marco Pereanez, Ali Gooya, Xenia Alba, Le Zhang, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi
Date: 2019-01-10 00:00:00
Tasks: Image Quality Assessment, Segmentation

The exploitation of large-scale population data has the potential to improve
healthcare by discovering and understanding patterns and trends within this
data. To enable high throughput analysis of cardiac imaging data automatically,
a pipeline should comprise quality monitoring of the input images, segmentation
of the cardiac structures, assessment of the segmentation quality, and parsing
of cardiac functional indexes. We present a fully automatic, high throughput
image parsing workflow for the analysis of cardiac MR images, and test its
performance on the UK Biobank (UKB) cardiac dataset. The proposed pipeline is
capable of performing end-to-end image processing including: data organisation,
image quality assessment, shape model initialisation, segmentation,
segmentation quality assessment, and functional parameter computation; all
without any user interaction. To the best of our knowledge,this is the first
paper tackling the fully automatic 3D analysis of the UKB population study,
providing reference ranges for all key cardiovascular functional indexes, from
both left and right ventricles of the heart. We tested our workflow on a
reference cohort of 800 healthy subjects for which manual delineations, and
reference functional indexes exist. Our results show statistically significant
agreement between the manually obtained reference indexes, and those
automatically computed using our framework.
Detecting Overfitting of Deep Generative Networks via Latent Recovery
Authors: Ryan Webster, Julien Rabin, Loic Simon, Frederic Jurie
Date: 2019-01-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Facial Inpainting, Memorization, Super-Resolution

State of the art deep generative networks are capable of producing images
with such incredible realism that they can be suspected of memorizing training
images. It is why it is not uncommon to include visualizations of training set
nearest neighbors, to suggest generated images are not simply memorized. We
demonstrate this is not sufficient and motivates the need to study
memorization/overfitting of deep generators with more scrutiny. This paper
addresses this question by i) showing how simple losses are highly effective at
reconstructing images for deep generators ii) analyzing the statistics of
reconstruction errors when reconstructing training and validation images, which
is the standard way to analyze overfitting in machine learning. Using this
methodology, this paper shows that overfitting is not detectable in the pure
GAN models proposed in the literature, in contrast with those using hybrid
adversarial losses, which are amongst the most widely applied generative
methods. The paper also shows that standard GAN evaluation metrics fail to
capture memorization for some deep generators. Finally, the paper also shows
how off-the-shelf GAN generators can be successfully applied to face inpainting
and face super-resolution using the proposed reconstruction method, without
hybrid adversarial losses.