pwc-archive/papers-with-abstracts huggingface dataset:
Sampling Limits for Electron Tomography with Sparsity-exploiting Reconstructions
Authors: Yi Jiang, Elliot Padgett, Robert Hovden, David A. Muller
Date: 2019-04-04 00:00:00
Tasks: compressed sensing, Electron Tomography

Electron tomography (ET) has become a standard technique for 3D
characterization of materials at the nano-scale. Traditional reconstruction
algorithms such as weighted back projection suffer from disruptive artifacts
with insufficient projections. Popularized by compressed sensing,
sparsity-exploiting algorithms have been applied to experimental ET data and
show promise for improving reconstruction quality or reducing the total beam
dose applied to a specimen. Nevertheless, theoretical bounds for these methods
have been less explored in the context of ET applications. Here, we perform
numerical simulations to investigate performance of l_1-norm and
total-variation (TV) minimization under various imaging conditions. From 36,100
different simulated structures, our results show specimens with more complex
structures generally require more projections for exact reconstruction.
However, once sufficient data is acquired, dividing the beam dose over more
projections provides no improvements - analogous to the traditional
dose-fraction theorem. Moreover, a limited tilt range of +-75 or less can
result in distorting artifacts in sparsity-exploiting reconstructions. The
influence of optimization parameters on reconstructions is also discussed.
3D Face Reconstruction Using Color Photometric Stereo with Uncalibrated Near Point Lights
Authors: Zhang Chen, Yu Ji, Mingyuan Zhou, Sing Bing Kang, Jingyi Yu
Date: 2019-04-04 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction, Semantic Segmentation

We present a new color photometric stereo (CPS) method that recovers high quality, detailed 3D face geometry in a single shot. Our system uses three uncalibrated near point lights of different colors and a single camera. For robust self-calibration of the light sources, we use 3D morphable model (3DMM) and semantic segmentation of facial parts. We address the spectral ambiguity problem by incorporating albedo consensus, albedo similarity, and proxy prior into a unified framework. We avoid the need for spatial constancy of albedo; instead, we use a new measure for albedo similarity that is based on the albedo norm profile. Experiments show that our new approach produces state-of-the-art results from single image with high-fidelity geometry that includes details such as wrinkles.
Dialogue Act Classification with Context-Aware Self-Attention
Authors: Vipul Raheja, Joel Tetreault
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Classification, Dialogue Act Classification, General Classification, Representation Learning

Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. We build on this prior work by leveraging the effectiveness of a context-aware self-attention mechanism coupled with a hierarchical recurrent neural network. We conduct extensive evaluations on standard Dialogue Act classification datasets and show significant improvement over state-of-the-art results on the Switchboard Dialogue Act (SwDA) Corpus. We also investigate the impact of different utterance-level representation learning methods and show that our method is effective at capturing utterance-level semantic text representations while maintaining high accuracy.
Geometry of the Hough transforms with applications to synthetic data
Authors: Mauro C. Beltrametti, Cristina Campi, Anna Maria Massone, Maria-Laura Torrente
Date: 2019-04-04 00:00:00

In the framework of the Hough transform technique to detect curves in images,
we provide a bound for the number of Hough transforms to be considered for a
successful optimization of the accumulator function in the recognition
algorithm. Such a bound is consequence of geometrical arguments. We also show
the robustness of the results when applied to synthetic datasets strongly
perturbed by noise. An algebraic approach, discussed in the appendix, leads to
a better bound of theoretical interest in the exact case.
Online Convex Matrix Factorization with Representative Regions
Authors: Abhishek Agarwal, Jianhao Peng, Olgica Milenkovic
Date: 2019-04-04 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Computational Efficiency, Dictionary Learning, Dimensionality Reduction

Matrix factorization (MF) is a versatile learning method that has found wide applications in various data-driven disciplines. Still, many MF algorithms do not adequately scale with the size of available datasets and/or lack interpretability. To improve the computational efficiency of the method, an online (streaming) MF algorithm was proposed in Mairal et al. [2010]. To enable data interpretability, a constrained version of MF, termed convex MF, was introduced in Ding et al. [2010]. In the latter work, the basis vectors are required to lie in the convex hull of the data samples, thereby ensuring that every basis can be interpreted as a weighted combination of data samples. No current algorithmic solutions for online convex MF are known as it is challenging to find adequate convex bases without having access to the complete dataset. We address both problems by proposing the first online convex MF algorithm that maintains a collection of constant-size sets of representative data samples needed for interpreting each of the basis (Ding et al. [2010]) and has the same almost sure convergence guarantees as the online learning algorithm of Mairal et al. [2010]. Our proof techniques combine random coordinate descent algorithms with specialized quasi-martingale convergence analysis. Experiments on synthetic and real world datasets show significant computational savings of the proposed online convex MF method compared to classical convex MF. Since the proposed method maintains small representative sets of data samples needed for convex interpretations, it is related to a body of work in theoretical computer science, pertaining to generating point sets (Blum et al. [2016]), and in computer vision, pertaining to archetypal analysis (Mei et al. [2018]). Nevertheless, it differs from these lines of work both in terms of the objective and algorithmic implementations.
Can a Robot Become a Movie Director? Learning Artistic Principles for Aerial Cinematography
Authors: Mirko Gschwindt, Efe Camci, Rogerio Bonatti, Wenshan Wang, Erdal Kayacan, Sebastian Scherer
Date: 2019-04-04 00:00:00
Tasks: Deep Reinforcement Learning, Motion Planning, Reinforcement Learning

Aerial filming is constantly gaining importance due to the recent advances in drone technology. It invites many intriguing, unsolved problems at the intersection of aesthetical and scientific challenges. In this work, we propose a deep reinforcement learning agent which supervises motion planning of a filming drone by making desirable shot mode selections based on aesthetical values of video shots. Unlike most of the current state-of-the-art approaches that require explicit guidance by a human expert, our drone learns how to make favorable viewpoint selections by experience. We propose a learning scheme that exploits aesthetical features of retrospective shots in order to extract a desirable policy for better prospective shots. We train our agent in realistic AirSim simulations using both a hand-crafted reward function as well as reward from direct human input. We then deploy the same agent on a real DJI M210 drone in order to test the generalization capability of our approach to real world conditions. To evaluate the success of our approach in the end, we conduct a comprehensive user study in which participants rate the shot quality of our methods. Videos of the system in action can be seen at https://youtu.be/qmVw6mfyEmw.
Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic MRI Using Mask-RCNN
Authors: Zhenzhen Dai, Eric Carver, Chang Liu, Joon Lee, Aharon Feldman, Weiwei Zong, Milan Pantelic, Mohamed Elshaikh, Ning Wen
Date: 2019-04-04 00:00:00

Prostate cancer (PCa) is the most common cancer in men in the United States.
Multiparametic magnetic resonance imaging (mp-MRI) has been explored by many
researchers to targeted prostate biopsies and radiation therapy. However,
assessment on mp-MRI can be subjective, development of computer-aided diagnosis
systems to automatically delineate the prostate gland and the intraprostratic
lesions (ILs) becomes important to facilitate with radiologists in clinical
practice. In this paper, we first study the implementation of the Mask-RCNN
model to segment the prostate and ILs. We trained and evaluated models on 120
patients from two different cohorts of patients. We also used 2D U-Net and 3D
U-Net as benchmarks to segment the prostate and compared the model's
performance. The contour variability of ILs using the algorithm was also
benchmarked against the interobserver variability between two different
radiation oncologists on 19 patients. Our results indicate that the Mask-RCNN
model is able to reach state-of-art performance in the prostate segmentation
and outperforms several competitive baselines in ILs segmentation.
Noise-Level Estimation from Single Color Image Using Correlations Between Textures in RGB Channels
Authors: Akihiro Nakamura, Michihiro Kobayashi
Date: 2019-04-04 00:00:00
Tasks: Denoising, Image Denoising, Noise Estimation

We propose a simple method for estimating noise level from a single color
image. In most image-denoising algorithms, an accurate noise-level estimate
results in good denoising performance; however, it is difficult to estimate
noise level from a single image because it is an ill-posed problem. We tackle
this problem by using prior knowledge that textures are highly correlated
between RGB channels and noise is uncorrelated to other signals. We also
extended our method for RAW images because they are available in almost all
digital cameras and often used in practical situations. Experiments show the
high noise-estimation performance of our method in synthetic noisy images. We
also applied our method to natural images including RAW images and achieved
better noise-estimation performance than conventional methods.
Generic Multiview Visual Tracking
Authors: Minye Wu, Haibin Ling, Ning Bi, Shenghua Gao, Hao Sheng, Jingyi Yu
Date: 2019-04-04 00:00:00
Tasks: Camera Calibration, Trajectory Prediction, Visual Tracking

Recent progresses in visual tracking have greatly improved the tracking
performance. However, challenges such as occlusion and view change remain
obstacles in real world deployment. A natural solution to these challenges is
to use multiple cameras with multiview inputs, though existing systems are
mostly limited to specific targets (e.g. human), static cameras, and/or camera
calibration. To break through these limitations, we propose a generic multiview
tracking (GMT) framework that allows camera movement, while requiring neither
specific object model nor camera calibration. A key innovation in our framework
is a cross-camera trajectory prediction network (TPN), which implicitly and
dynamically encodes camera geometric relations, and hence addresses missing
target issues such as occlusion. Moreover, during tracking, we assemble
information across different cameras to dynamically update a novel
collaborative correlation filter (CCF), which is shared among cameras to
achieve robustness against view change. The two components are integrated into
a correlation filter tracking framework, where the features are trained offline
using existing single view tracking datasets. For evaluation, we first
contribute a new generic multiview tracking dataset (GMTD) with careful
annotations, and then run experiments on GMTD and the PETS2009 datasets. On
both datasets, the proposed GMT algorithm shows clear advantages over
state-of-the-art ones.
DeCaFA: Deep Convolutional Cascade for Face Alignment In The Wild
Authors: Arnaud Dapogny, Kévin Bailly, Matthieu Cord
Date: 2019-04-04 00:00:00
Proceeding: ICCV 2019 10
Tasks: Face Alignment

Face Alignment is an active computer vision domain, that consists in
localizing a number of facial landmarks that vary across datasets.
State-of-the-art face alignment methods either consist in end-to-end
regression, or in refining the shape in a cascaded manner, starting from an
initial guess. In this paper, we introduce DeCaFA, an end-to-end deep
convolutional cascade architecture for face alignment. DeCaFA uses
fully-convolutional stages to keep full spatial resolution throughout the
cascade. Between each cascade stage, DeCaFA uses multiple chained transfer
layers with spatial softmax to produce landmark-wise attention maps for each of
several landmark alignment tasks. Weighted intermediate supervision, as well as
efficient feature fusion between the stages allow to learn to progressively
refine the attention maps in an end-to-end manner. We show experimentally that
DeCaFA significantly outperforms existing approaches on 300W, CelebA and WFLW
databases. In addition, we show that DeCaFA can learn fine alignment with
reasonable accuracy from very few images using coarsely annotated data.
Explaining versus Describing Human Decisions. Hilbert Space Structures in Decision Theory
Authors: Sandro Sozzo
Date: 2019-04-04 00:00:00
Tasks: Decision Making

Despite the impressive success of quantum structures to model long-standing
human judgement and decision puzzles, the {\it quantum cognition research
programme} still faces challenges about its explanatory power. Indeed, quantum
models introduce new parameters, which may fit empirical data without
necessarily explaining them. Also, one wonders whether more general
non-classical structures are better equipped to model cognitive phenomena. In
this paper, we provide a {\it realistic-operational foundation of decision
processes} using a known decision-making puzzle, the {\it Ellsberg paradox}, as
a case study. Then, we elaborate a novel representation of the Ellsberg
decision situation applying standard quantum correspondence rules which map
realistic-operational entities into quantum mathematical terms. This result
opens the way towards an independent, foundational rather than
phenomenological, motivation for a general use of quantum Hilbert space
structures in human cognition.
Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics
Authors: Cheng Deng, Zhao Li, Xinbo Gao, DaCheng Tao
Date: 2019-04-04 00:00:00
Tasks: General Classification

As JPEG is the most widely used image format, the importance of tampering
detection for JPEG images in blind forensics is self-evident. In this area,
extracting effective statistical characteristics from a JPEG image for
classification remains a challenge. Effective features are designed manually in
traditional methods, suggesting that extensive labor-consuming research and
derivation is required. In this paper, we propose a novel image tampering
detection method based on deep multi-scale discriminative networks (MSD-Nets).
The multi-scale module is designed to automatically extract multiple features
from the discrete cosine transform (DCT) coefficient histograms of the JPEG
image. This module can capture the characteristic information in different
scale spaces. In addition, a discriminative module is also utilized to improve
the detection effect of the networks in those difficult situations when the
first compression quality (QF1) is higher than the second one (QF2). A special
network in this module is designed to distinguish the small statistical
difference between authentic and tampered regions in these cases. Finally, a
probability map can be obtained and the specific tampering area is located
using the last classification results. Extensive experiments demonstrate the
superiority of our proposed method in both quantitative and qualitative metrics
when compared with state-of-the-art approaches.
SMURFF: a High-Performance Framework for Matrix Factorization
Authors: Tom Vander Aa, Imen Chakroun, Thomas J. Ashby, Jaak Simm, Adam Arany, Yves Moreau, Thanh Le Van, José Felipe Golib Dzib, Jörg Wegner, Vladimir Chupakhin, Hugo Ceulemans, Roel Wuyts, Wilfried Verachtert
Date: 2019-04-04 00:00:00
Tasks: Activity Prediction, Recommendation Systems, Vocal Bursts Intensity Prediction

Bayesian Matrix Factorization (BMF) is a powerful technique for recommender systems because it produces good results and is relatively robust against overfitting. Yet BMF is more computationally intensive and thus more challenging to implement for large datasets. In this work we present SMURFF a high-performance feature-rich framework to compose and construct different Bayesian matrix-factorization methods. The framework has been successfully used in to do large scale runs of compound-activity prediction. SMURFF is available as open-source and can be used both on a supercomputer and on a desktop or laptop machine. Documentation and several examples are provided as Jupyter notebooks using SMURFF's high-level Python API.
A deterministic and computable Bernstein-von Mises theorem
Authors: Guillaume P. Dehaene
Date: 2019-04-04 00:00:00

Bernstein-von Mises results (BvM) establish that the Laplace approximation is
asymptotically correct in the large-data limit. However, these results are
inappropriate for computational purposes since they only hold over most, and
not all, datasets and involve hard-to-estimate constants. In this article, I
present a new BvM theorem which bounds the Kullback-Leibler (KL) divergence
between a fixed log-concave density $f\left(\boldsymbol{\theta}\right)$ and its
Laplace approximation. The bound goes to $0$ as the higher-derivatives of
$f\left(\boldsymbol{\theta}\right)$ tend to $0$ and
$f\left(\boldsymbol{\theta}\right)$ becomes increasingly Gaussian. The
classical BvM theorem in the IID large-data asymptote is recovered as a
corollary.
  Critically, this theorem further suggests a number of computable
approximations of the KL divergence with the most promising being: \[
KL\left(g_{LAP},f\right)\approx\frac{1}{2}\text{Var}_{\boldsymbol{\theta}\sim
g\left(\boldsymbol{\theta}\right)}\left(\log\left[f\left(\boldsymbol{\theta}\right)\right]-\log\left[g_{LAP}\left(\boldsymbol{\theta}\right)\right]\right)
\] An empirical investigation of these bounds in the logistic classification
model reveals that these approximations are great surrogates for the KL
divergence. This result, and future results of a similar nature, could provide
a path towards rigorously controlling the error due to the Laplace
approximation and more modern approximation methods.
Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion
Authors: Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Ido Dagan
Date: 2019-04-04 00:00:00
Proceeding: WS 2019 6

In this paper, we present a novel algorithm that combines multi-context term
embeddings using a neural classifier and we test this approach on the use case
of corpus-based term set expansion. In addition, we present a novel and unique
dataset for intrinsic evaluation of corpus-based term set expansion algorithms.
We show that, over this dataset, our algorithm provides up to 5 mean average
precision points over the best baseline.
A Categorisation of Post-hoc Explanations for Predictive Models
Authors: John Mitros, Brian Mac Namee
Date: 2019-04-04 00:00:00

The ubiquity of machine learning based predictive models in modern society
naturally leads people to ask how trustworthy those models are? In predictive
modeling, it is quite common to induce a trade-off between accuracy and
interpretability. For instance, doctors would like to know how effective some
treatment will be for a patient or why the model suggested a particular
medication for a patient exhibiting those symptoms? We acknowledge that the
necessity for interpretability is a consequence of an incomplete formalisation
of the problem, or more precisely of multiple meanings adhered to a particular
concept. For certain problems, it is not enough to get the answer (what), the
model also has to provide an explanation of how it came to that conclusion
(why), because a correct prediction, only partially solves the original
problem. In this article we extend existing categorisation of techniques to aid
model interpretability and test this categorisation.
Learning Numeracy: Binary Arithmetic with Neural Turing Machines
Authors: Jacopo Castellini
Date: 2019-04-04 00:00:00

One of the main problems encountered so far with recurrent neural networks is that they struggle to retain long-time information dependencies in their recurrent connections. Neural Turing Machines (NTMs) attempt to mitigate this issue by providing the neural network with an external portion of memory, in which information can be stored and manipulated later on. The whole mechanism is differentiable end-to-end, allowing the network to learn how to utilise this long-term memory via stochastic gradient descent. This allows NTMs to infer simple algorithms directly from data sequences. Nonetheless, the model can be hard to train due to a large number of parameters and interacting components and little related work is present. In this work we use NTMs to learn and generalise two arithmetical tasks: binary addition and multiplication. These tasks are two fundamental algorithmic examples in computer science, and are a lot more challenging than the previously explored ones, with which we aim to shed some light on the real capabilities on this neural model.
Composition of Sentence Embeddings:Lessons from Statistical Relational Learning
Authors: Damien Sileo, Tim Van-De-Cruys, Camille Pradel, Philippe Muller
Date: 2019-04-04 00:00:00
Tasks: Natural Language Inference, Prediction, Relation, Relational Reasoning, Relation Prediction, Representation Learning, Sentence, Sentence Similarity

Various NLP problems -- such as the prediction of sentence similarity,
entailment, and discourse relations -- are all instances of the same general
task: the modeling of semantic relations between a pair of textual elements. A
popular model for such problems is to embed sentences into fixed size vectors,
and use composition functions (e.g. concatenation or sum) of those vectors as
features for the prediction. At the same time, composition of embeddings has
been a main focus within the field of Statistical Relational Learning (SRL)
whose goal is to predict relations between entities (typically from knowledge
base triples). In this article, we show that previous work on relation
prediction between texts implicitly uses compositions from baseline SRL models.
We show that such compositions are not expressive enough for several tasks
(e.g. natural language inference). We build on recent SRL models to address
textual relational problems, showing that they are more expressive, and can
alleviate issues from simpler compositions. The resulting models significantly
improve the state of the art in both transferable sentence representation
learning and relation prediction.
ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems
Authors: Inigo Jauregi Unanue, Ehsan Zare Borzeshi, Nazanin Esmaili, Massimo Piccardi
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, Translation, Word Embeddings

Regularization of neural machine translation is still a significant problem,
especially in low-resource settings. To mollify this problem, we propose
regressing word embeddings (ReWE) as a new regularization technique in a system
that is jointly trained to predict the next word in the translation
(categorical value) and its word embedding (continuous value). Such a joint
training allows the proposed system to learn the distributional properties
represented by the word embeddings, empirically improving the generalization to
unseen sentences. Experiments over three translation datasets have showed a
consistent improvement over a strong baseline, ranging between 0.91 and 2.54
BLEU points, and also a marked improvement over a state-of-the-art system.