pwc-archive/papers-with-abstracts huggingface dataset:
Are object detection assessment criteria ready for maritime computer vision?
Authors: Dilip K. Prasad, Huixu Dong, Deepu Rajan, Chai Quek
Date: 2018-09-12 00:00:00
Tasks: object-detection, Object Detection

Maritime vessels equipped with visible and infrared cameras can complement other conventional sensors for object detection. However, application of computer vision techniques in maritime domain received attention only recently. The maritime environment offers its own unique requirements and challenges. Assessment of the quality of detections is a fundamental need in computer vision. However, the conventional assessment metrics suitable for usual object detection are deficient in the maritime setting. Thus, a large body of related work in computer vision appears inapplicable to the maritime setting at the first sight. We discuss the problem of defining assessment metrics suitable for maritime computer vision. We consider new bottom edge proximity metrics as assessment metrics for maritime computer vision. These metrics indicate that existing computer vision approaches are indeed promising for maritime computer vision and can play a foundational role in the emerging field of maritime computer vision.
Semantic WordRank: Generating Finer Single-Document Summarizations
Authors: Hao Zhang, Jie Wang
Date: 2018-09-12 00:00:00
Tasks: Clustering, Diversity

We present Semantic WordRank (SWR), an unsupervised method for generating an
extractive summary of a single document. Built on a weighted word graph with
semantic and co-occurrence edges, SWR scores sentences using an
article-structure-biased PageRank algorithm with a Softplus function
adjustment, and promotes topic diversity using spectral subtopic clustering
under the Word-Movers-Distance metric. We evaluate SWR on the DUC-02 and
SummBank datasets and show that SWR produces better summaries than the
state-of-the-art algorithms over DUC-02 under common ROUGE measures. We then
show that, under the same measures over SummBank, SWR outperforms each of the
three human annotators (aka. judges) and compares favorably with the combined
performance of all judges.
The closed loop between opinion formation and personalised recommendations
Authors: Wilbert Samuel Rossi, Jan Willem Polderman, Paolo Frasca
Date: 2018-09-12 00:00:00
Tasks: Recommendation Systems

In online platforms, recommender systems are responsible for directing users to relevant contents. In order to enhance the users' engagement, recommender systems adapt their output to the reactions of the users, who are in turn affected by the recommended contents. In this work, we study a tractable analytical model of a user that interacts with an online news aggregator, with the purpose of making explicit the feedback loop between the evolution of the user's opinion and the personalised recommendation of contents. More specifically, we assume that the user is endowed with a scalar opinion about a certain issue and seeks news about it on a news aggregator: this opinion is influenced by all received news, which are characterized by a binary position on the issue at hand. The user is affected by a confirmation bias, that is, a preference for news that confirm her current opinion. The news aggregator recommends items with the goal of maximizing the number of user's clicks (as a measure of her engagement): in order to fulfil its goal, the recommender has to compromise between exploring the user's preferences and exploiting what it has learned so far. After defining suitable metrics for the effectiveness of the recommender systems (such as the click-through rate) and for its impact on the opinion, we perform both extensive numerical simulations and a mathematical analysis of the model. We find that personalised recommendations markedly affect the evolution of opinions and favor the emergence of more extreme ones: the intensity of these effects is inherently related to the effectiveness of the recommender. We also show that by tuning the amount of randomness in the recommendation algorithm, one can seek a balance between the effectiveness of the recommendation system and its impact on the opinions.
Deep Spectral Correspondence for Matching Disparate Image Pairs
Authors: Arun CS Kumar, Shefali Srivastava, Anirban Mukhopadhyay, Suchendra M. Bhandarkar
Date: 2018-09-12 00:00:00
Tasks: Matching Disparate Images

A novel, non-learning-based, saliency-aware, shape-cognizant correspondence
determination technique is proposed for matching image pairs that are
significantly disparate in nature. Images in the real world often exhibit high
degrees of variation in scale, orientation, viewpoint, illumination and affine
projection parameters, and are often accompanied by the presence of textureless
regions and complete or partial occlusion of scene objects. The above
conditions confound most correspondence determination techniques by rendering
impractical the use of global contour-based descriptors or local pixel-level
features for establishing correspondence. The proposed deep spectral
correspondence (DSC) determination scheme harnesses the representational power
of local feature descriptors to derive a complex high-level global shape
representation for matching disparate images. The proposed scheme reasons about
correspondence between disparate images using high-level global shape cues
derived from low-level local feature descriptors. Consequently, the proposed
scheme enjoys the best of both worlds, i.e., a high degree of invariance to
affine parameters such as scale, orientation, viewpoint, illumination afforded
by the global shape cues and robustness to occlusion provided by the low-level
feature descriptors. While the shape-based component within the proposed scheme
infers what to look for, an additional saliency-based component dictates where
to look at thereby tackling the noisy correspondences arising from the presence
of textureless regions and complex backgrounds. In the proposed scheme, a joint
image graph is constructed using distances computed between interest points in
the appearance (i.e., image) space. Eigenspectral decomposition of the joint
image graph allows for reasoning about shape similarity to be performed
jointly, in the appearance space and eigenspace.
Jump to better conclusions: SCAN both left and right
Authors: Jasmijn Bastings, Marco Baroni, Jason Weston, Kyunghyun Cho, Douwe Kiela
Date: 2018-09-12 00:00:00
Proceeding: WS 2018 11

Lake and Baroni (2018) recently introduced the SCAN data set, which consists of simple commands paired with action sequences and is intended to test the strong generalization abilities of recurrent sequence-to-sequence models. Their initial experiments suggested that such models may fail because they lack the ability to extract systematic rules. Here, we take a closer look at SCAN and show that it does not always capture the kind of generalization that it was designed for. To mitigate this we propose a complementary dataset, which requires mapping actions back to the original commands, called NACS. We show that models that do well on SCAN do not necessarily do well on NACS, and that NACS exhibits properties more closely aligned with realistic use-cases for sequence-to-sequence models.
Visual-Quality-Driven Learning for Underwater Vision Enhancement
Authors: Walysson Vital Barbosa, Henrique Grandinetti Barbosa Amaral, Thiago Lages Rocha, Erickson Rangel Nascimento
Date: 2018-09-12 00:00:00

The image processing community has witnessed remarkable advances in enhancing
and restoring images. Nevertheless, restoring the visual quality of underwater
images remains a great challenge. End-to-end frameworks might fail to enhance
the visual quality of underwater images since in several scenarios it is not
feasible to provide the ground truth of the scene radiance. In this work, we
propose a CNN-based approach that does not require ground truth data since it
uses a set of image quality metrics to guide the restoration learning process.
The experiments showed that our method improved the visual quality of
underwater images preserving their edges and also performed well considering
the UCIQE metric.
A Two-Step Learning Method For Detecting Landmarks on Faces From Different Domains
Authors: Bruna Vieira Frade, Erickson R. Nascimento
Date: 2018-09-12 00:00:00
Tasks: Domain Adaptation

The detection of fiducial points on faces has significantly been favored by
the rapid progress in the field of machine learning, in particular in the
convolution networks. However, the accuracy of most of the detectors strongly
depends on an enormous amount of annotated data. In this work, we present a
domain adaptation approach based on a two-step learning to detect fiducial
points on human and animal faces. We evaluate our method on three different
datasets composed of different animal faces (cats, dogs, and horses). The
experiments show that our method performs better than state of the art and can
use few annotated data to leverage the detection of landmarks reducing the
demand for large volume of annotated data.
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration
Authors: Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu
Date: 2018-09-12 00:00:00
Tasks: Stochastic Optimization

Stochastic gradient Hamiltonian Monte Carlo (SGHMC) is a variant of stochastic gradient with momentum where a controlled and properly scaled Gaussian noise is added to the stochastic gradients to steer the iterates towards a global minimum. Many works reported its empirical success in practice for solving stochastic non-convex optimization problems, in particular it has been observed to outperform overdamped Langevin Monte Carlo-based methods such as stochastic gradient Langevin dynamics (SGLD) in many applications. Although asymptotic global convergence properties of SGHMC are well known, its finite-time performance is not well-understood. In this work, we study two variants of SGHMC based on two alternative discretizations of the underdamped Langevin diffusion. We provide finite-time performance bounds for the global convergence of both SGHMC variants for solving stochastic non-convex optimization problems with explicit constants. Our results lead to non-asymptotic guarantees for both population and empirical risk minimization problems. For a fixed target accuracy level, on a class of non-convex problems, we obtain complexity bounds for SGHMC that can be tighter than those for SGLD. These results show that acceleration with momentum is possible in the context of global non-convex optimization.
Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learning
Authors: Edward Higson, Will Handley, Michael Hobson, Anthony Lasenby
Date: 2018-09-12 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency, Model Selection

We present a principled Bayesian framework for signal reconstruction, in
which the signal is modelled by basis functions whose number (and form, if
required) is determined by the data themselves. This approach is based on a
Bayesian interpretation of conventional sparse reconstruction and
regularisation techniques, in which sparsity is imposed through priors via
Bayesian model selection. We demonstrate our method for noisy 1- and
2-dimensional signals, including astronomical images. Furthermore, by using a
product-space approach, the number and type of basis functions can be treated
as integer parameters and their posterior distributions sampled directly. We
show that order-of-magnitude increases in computational efficiency are possible
from this technique compared to calculating the Bayesian evidences separately,
and that further computational gains are possible using it in combination with
dynamic nested sampling. Our approach can also be readily applied to neural
networks, where it allows the network architecture to be determined by the data
in a principled Bayesian manner by treating the number of nodes and hidden
layers as parameters.
Coordinated Heterogeneous Distributed Perception based on Latent Space Representation
Authors: Timo Korthals, Jürgen Leitner, Ulrich Rückert
Date: 2018-09-12 00:00:00
Tasks: Sensor Fusion

We investigate a reinforcement approach for distributed sensing based on the
latent space derived from multi-modal deep generative models. Our contribution
provides insights to the following benefits: Detections can be exchanged
effectively between robots equipped with uni-modal sensors due to a shared
latent representation of information that is trained by a Variational Auto
Encoder (VAE). Sensor-fusion can be applied asynchronously due to the
generative feature of the VAE. Deep Q-Networks (DQNs) are trained to minimize
uncertainty in latent space by coordinating robots to a Point-of-Interest (PoI)
where their sensor modality can provide beneficial information about the PoI.
Additionally, we show that the decrease in uncertainty can be defined as the
direct reward signal for training the DQN.
Detector monitoring with artificial neural networks at the CMS experiment at the CERN Large Hadron Collider
Authors: Adrian Alan Pol, Gianluca Cerminara, Cecile Germain, Maurizio Pierini, Agrima Seth
Date: 2018-07-27 00:00:00
Tasks: Anomaly Detection

Reliable data quality monitoring is a key asset in delivering collision data
suitable for physics analysis in any modern large-scale High Energy Physics
experiment. This paper focuses on the use of artificial neural networks for
supervised and semi-supervised problems related to the identification of
anomalies in the data collected by the CMS muon detectors. We use deep neural
networks to analyze LHC collision data, represented as images organized
geographically. We train a classifier capable of detecting the known anomalous
behaviors with unprecedented efficiency and explore the usage of convolutional
autoencoders to extend anomaly detection capabilities to unforeseen failure
modes. A generalization of this strategy could pave the way to the automation
of the data quality assessment process for present and future high-energy
physics experiments.
BART: Bayesian additive regression trees
Authors: Hugh A. Chipman, Edward I. George, Robert E. McCulloch
Date: 2008-06-19 00:00:00
Tasks: Causal Inference, Drug Discovery, regression, Variable Selection

We develop a Bayesian "sum-of-trees" model where each tree is constrained by
a regularization prior to be a weak learner, and fitting and inference are
accomplished via an iterative Bayesian backfitting MCMC algorithm that
generates samples from a posterior. Effectively, BART is a nonparametric
Bayesian regression approach which uses dimensionally adaptive random basis
elements. Motivated by ensemble methods in general, and boosting algorithms in
particular, BART is defined by a statistical model: a prior and a likelihood.
This approach enables full posterior inference including point and interval
estimates of the unknown regression function as well as the marginal effects of
potential predictors. By keeping track of predictor inclusion frequencies, BART
can also be used for model-free variable selection. BART's many features are
illustrated with a bake-off against competing methods on 42 different data
sets, with a simulation experiment and on a drug discovery classification
problem.
High-Accuracy Inference in Neuromorphic Circuits using Hardware-Aware Training
Authors: Borna Obradovic, Titash Rakshit, Ryan Hatcher, Jorge A. Kittl, Mark S. Rodder
Date: 2018-09-13 00:00:00
Tasks: Quantization, Vocal Bursts Intensity Prediction

Neuromorphic Multiply-And-Accumulate (MAC) circuits utilizing synaptic weight
elements based on SRAM or novel Non-Volatile Memories (NVMs) provide a
promising approach for highly efficient hardware representations of neural
networks. NVM density and robustness requirements suggest that off-line
training is the right choice for "edge" devices, since the requirements for
synapse precision are much less stringent. However, off-line training using
ideal mathematical weights and activations can result in significant loss of
inference accuracy when applied to non-ideal hardware. Non-idealities such as
multi-bit quantization of weights and activations, non-linearity of weights,
finite max/min ratios of NVM elements, and asymmetry of positive and negative
weight components all result in degraded inference accuracy. In this work, it
is demonstrated that non-ideal Multi-Layer Perceptron (MLP) architectures using
low bitwidth weights and activations can be trained with negligible loss of
inference accuracy relative to their Floating Point-trained counterparts using
a proposed off-line, continuously differentiable HW-aware training algorithm.
The proposed algorithm is applicable to a wide range of hardware models, and
uses only standard neural network training methods. The algorithm is
demonstrated on the MNIST and EMNIST datasets, using standard MLPs.
Semantic Analysis of (Reflectional) Visual Symmetry: A Human-Centred Computational Model for Declarative Explainability
Authors: Jakob Suchan, Mehul Bhatt, Srikrishna Vardarajan, Seyed Ali Amirshahi, Stella Yu
Date: 2018-05-31 00:00:00
Tasks: Question Answering

We present a computational model for the semantic interpretation of symmetry
in naturalistic scenes. Key features include a human-centred representation,
and a declarative, explainable interpretation model supporting deep semantic
question-answering founded on an integration of methods in knowledge
representation and deep learning based computer vision. In the backdrop of the
visual arts, we showcase the framework's capability to generate human-centred,
queryable, relational structures, also evaluating the framework with an
empirical study on the human perception of visual symmetry. Our framework
represents and is driven by the application of foundational, integrated Vision
and Knowledge Representation and Reasoning methods for applications in the
arts, and the psychological and social sciences.
Gradient descent in higher codimension
Authors: Y. Cooper
Date: 2018-09-14 00:00:00

We consider the behavior of gradient flow and of discrete and noisy gradient
descent. It is commonly noted that the addition of noise to the process of
discrete gradient descent can affect the trajectory of gradient descent. In
previous work, we observed such effects. There, we considered the case where
the minima had codimension 1. In this note, we do some computer experiments and
observe the behavior of noisy gradient descent in the more complex setting of
minima of higher codimension.
Robustness of Quantum-Enhanced Adaptive Phase Estimation
Authors: Pantita Palittapongarnpim, Barry C. Sanders
Date: 2018-09-14 00:00:00

As all physical adaptive quantum-enhanced metrology schemes operate under
noisy conditions with only partially understood noise characteristics, so a
practical control policy must be robust even for unknown noise. We aim to
devise a test to evaluate the robustness of AQEM policies and assess the
resource used by the policies. The robustness test is performed on QEAPE by
simulating the scheme under four phase-noise models corresponding to
normal-distribution noise, random-telegraph noise, skew-normal-distribution
noise, and log-normal-distribution noise. Control policies are devised either
by an evolutionary algorithm under the same noisy conditions, albeit ignorant
of its properties, or a Bayesian-based feedback method that assumes no noise.
Our robustness test and resource comparison method can be used to determining
the efficacy and selecting a suitable policy.
Extending Neural Generative Conversational Model using External Knowledge Sources
Authors: Prasanna Parthasarathi, Joelle Pineau
Date: 2018-09-14 00:00:00
Proceeding: EMNLP 2018 10

The use of connectionist approaches in conversational agents has been
progressing rapidly due to the availability of large corpora. However current
generative dialogue models often lack coherence and are content poor. This work
proposes an architecture to incorporate unstructured knowledge sources to
enhance the next utterance prediction in chit-chat type of generative dialogue
models. We focus on Sequence-to-Sequence (Seq2Seq) conversational agents
trained with the Reddit News dataset, and consider incorporating external
knowledge from Wikipedia summaries as well as from the NELL knowledge base. Our
experiments show faster training time and improved perplexity when leveraging
external knowledge.
Deep Compressive Autoencoder for Action Potential Compression in Large-Scale Neural Recording
Authors: Tong Wu, Wenfeng Zhao, Edward Keefer, Zhi Yang
Date: 2018-09-14 00:00:00
Tasks: Quantization, Spike Sorting

Understanding the coordinated activity underlying brain computations requires
large-scale, simultaneous recordings from distributed neuronal structures at a
cellular-level resolution. One major hurdle to design high-bandwidth,
high-precision, large-scale neural interfaces lies in the formidable data
streams that are generated by the recorder chip and need to be online
transferred to a remote computer. The data rates can require hundreds to
thousands of I/O pads on the recorder chip and power consumption on the order
of Watts for data streaming alone. We developed a deep learning-based
compression model to reduce the data rate of multichannel action potentials.
The proposed model is built upon a deep compressive autoencoder (CAE) with
discrete latent embeddings. The encoder is equipped with residual
transformations to extract representative features from spikes, which are
mapped into the latent embedding space and updated via vector quantization
(VQ). The decoder network reconstructs spike waveforms from the quantized
latent embeddings. Experimental results show that the proposed model
consistently outperforms conventional methods by achieving much higher
compression ratios (20-500x) and better or comparable reconstruction
accuracies. Testing results also indicate that CAE is robust against a diverse
range of imperfections, such as waveform variation and spike misalignment, and
has minor influence on spike sorting accuracy. Furthermore, we have estimated
the hardware cost and real-time performance of CAE and shown that it could
support thousands of recording channels simultaneously without excessive
power/heat dissipation. The proposed model can reduce the required data
transmission bandwidth in large-scale recording experiments and maintain good
signal qualities. The code of this work has been made available at
https://github.com/tong-wu-umn/spike-compression-autoencoder
BPE and computer-extracted parenchymal enhancement for breast cancer risk, response monitoring, and prognosis
Authors: Bas H. M. van der Velden
Date: 2018-09-14 00:00:00
Tasks: Prognosis

Functional behavior of breast cancer - representing underlying biology - can
be analyzed using MRI. The most widely used breast MR imaging protocol is
dynamic contrast-enhanced T1-weighted imaging. The cancer enhances on dynamic
contrast-enhanced MR imaging because the contrast agent leaks from the leaky
vessels into the interstitial space. The contrast agent subsequently leaks back
into the vascular space, creating a washout effect. The normal parenchymal
tissue of the breast can also enhance after contrast injection. This
enhancement generally increases over time. Typically, a radiologist assesses
this background parenchymal enhancement (BPE) using the Breast Imaging
Reporting and Data System (BI-RADS). According to the BI-RADS, BPE refers to
the volume of enhancement and the intensity of enhancement and is divided in
four incremental categories: minimal, mild, moderate, and marked.
  Researchers have developed semi-automatic and automatic methods to extract
properties of BPE from MR images. For clarity, in this syllabus the BI-RADS
definition will be referred to as BPE, whereas the computer-extracted
properties will not. Both BPE and computer-extracted parenchymal enhancement
properties have been linked to screening and diagnosis, hormone status and age,
risk of development of breast cancer, response monitoring, and prognosis.