pwc-archive/papers-with-abstracts huggingface dataset:
Machine Translation: A Literature Review
Authors: Ankush Garg, Mayank Agarwal
Date: 2018-12-28 00:00:00
Tasks: Machine Translation, Translation

Machine translation (MT) plays an important role in benefiting linguists,
sociologists, computer scientists, etc. by processing natural language to
translate it into some other natural language. And this demand has grown
exponentially over past couple of years, considering the enormous exchange of
information between different regions with different regional languages.
Machine Translation poses numerous challenges, some of which are: a) Not all
words in one language has equivalent word in another language b) Two given
languages may have completely different structures c) Words can have more than
one meaning. Owing to these challenges, along with many others, MT has been
active area of research for more than five decades. Numerous methods have been
proposed in the past which either aim at improving the quality of the
translations generated by them, or study the robustness of these systems by
measuring their performance on many different languages. In this literature
review, we discuss statistical approaches (in particular word-based and
phrase-based) and neural approaches which have gained widespread prominence
owing to their state-of-the-art results across multiple major languages.
Car Detection using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3
Authors: Bilel Benjdira, Taha Khursheed, Anis Koubaa, Adel Ammar, Kais Ouni
Date: 2018-12-28 00:00:00

Unmanned Aerial Vehicles are increasingly being used in surveillance and
traffic monitoring thanks to their high mobility and ability to cover areas at
different altitudes and locations. One of the major challenges is to use aerial
images to accurately detect cars and count them in real-time for traffic
monitoring purposes. Several deep learning techniques were recently proposed
based on convolution neural network (CNN) for real-time classification and
recognition in computer vision. However, their performance depends on the
scenarios where they are used. In this paper, we investigate the performance of
two state-of-the-art CNN algorithms, namely Faster R-CNN and YOLOv3, in the
context of car detection from aerial images. We trained and tested these two
models on a large car dataset taken from UAVs. We demonstrated in this paper
that YOLOv3 outperforms Faster R-CNN in sensitivity and processing time,
although they are comparable in the precision metric.
Dealing with Limited Backhaul Capacity in Millimeter Wave Systems: A Deep Reinforcement Learning Approach
Authors: Mingjie Feng, Shiwen Mao
Date: 2018-12-27 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

Millimeter Wave (MmWave) communication is one of the key technology of the
fifth generation (5G) wireless systems to achieve the expected 1000x data rate.
With large bandwidth at mmWave band, the link capacity between users and base
stations (BS) can be much higher compared to sub-6GHz wireless systems.
Meanwhile, due to the high cost of infrastructure upgrade, it would be
difficult for operators to drastically enhance the capacity of backhaul links
between mmWave BSs and the core network. As a result, the data rate provided by
backhaul may not be sufficient to support all mmWave links, the backhaul
connection becomes the new bottleneck that limits the system performance. On
the other hand, as mmWave channels are subject to random blockage, the data
rates of mmWave users significantly vary over time. With limited backhaul
capacity and highly dynamic data rates of users, how to allocate backhaul
resource to each user remains a challenge for mmWave systems. In this article,
we present a deep reinforcement learning (DRL) approach to address this
challenge. By learning the blockage pattern, the system dynamics can be
captured and predicted, resulting in efficient utilization of backhaul
resource. We begin with a discussion on DRL and its application in wireless
systems. We then investigate the problem backhaul resource allocation and
present the DRL based solution. Finally, we discuss open problems for future
research and conclude this article.
Learning a nonlinear dynamical system model of gene regulation: A perturbed steady-state approach
Authors: Arwen Vanice Bradley, Ye Henry Li, Bokyung Choi, Wing Hung Wong
Date: 2012-07-13 00:00:00
Tasks: Experimental Design

Biological structure and function depend on complex regulatory interactions
between many genes. A wealth of gene expression data is available from
high-throughput genome-wide measurement technologies, but effective gene
regulatory network inference methods are still needed. Model-based methods
founded on quantitative descriptions of gene regulation are among the most
promising, but many such methods rely on simple, local models or on ad hoc
inference approaches lacking experimental interpretability. We propose an
experimental design and develop an associated statistical method for inferring
a gene network by learning a standard quantitative, interpretable, predictive,
biophysics-based ordinary differential equation model of gene regulation. We
fit the model parameters using gene expression measurements from perturbed
steady-states of the system, like those following overexpression or knockdown
experiments. Although the original model is nonlinear, our design allows us to
transform it into a convex optimization problem by restricting attention to
steady-states and using the lasso for parameter selection. Here, we describe
the model and inference algorithm and apply them to a synthetic six-gene
system, demonstrating that the model is detailed and flexible enough to account
for activation and repression as well as synergistic and self-regulation, and
the algorithm can efficiently and accurately recover the parameters used to
generate the data.
Learning Independent Object Motion from Unlabelled Stereoscopic Videos
Authors: Zhe Cao, Abhishek Kar, Christian Haene, Jitendra Malik
Date: 2019-01-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D geometry, Object, Optical Flow Estimation

We present a system for learning motion of independently moving objects from
stereo videos. The only human annotation used in our system are 2D object
bounding boxes which introduce the notion of objects to our system. Unlike
prior learning based work which has focused on predicting dense pixel-wise
optical flow field and/or a depth map for each image, we propose to predict
object instance specific 3D scene flow maps and instance masks from which we
are able to derive the motion direction and speed for each object instance. Our
network takes the 3D geometry of the problem into account which allows it to
correlate the input images. We present experiments evaluating the accuracy of
our 3D flow vectors, as well as depth maps and projected 2D optical flow where
our jointly learned system outperforms earlier approaches trained for each task
independently.
Decision-making and Fuzzy Temporal Logic
Authors: José Cláudio do Nascimento
Date: 2019-01-07 00:00:00
Tasks: Decision Making

This paper shows that the fuzzy temporal logic can model figures of thought
to describe decision-making behaviors. In order to exemplify, some economic
behaviors observed experimentally were modeled from problems of choice
containing time, uncertainty and fuzziness. Related to time preference, it is
noted that the subadditive discounting is mandatory in positive rewards
situations and, consequently, results in the magnitude effect and time effect,
where the last has a stronger discounting for earlier delay periods (as in, one
hour, one day), but a weaker discounting for longer delay periods (for
instance, six months, one year, ten years). In addition, it is possible to
explain the preference reversal (change of preference when two rewards proposed
on different dates are shifted in the time). Related to the Prospect Theory, it
is shown that the risk seeking and the risk aversion are magnitude dependents,
where the risk seeking may disappear when the values to be lost are very high.
Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography
Authors: Wenqi Lu, Jinming Duan, David Orive-Miguel, Lionel Herve, Iain B. Styles
Date: 2019-01-07 00:00:00
Tasks: Image Reconstruction

Total variation (TV) is a powerful regularization method that has been widely
applied in different imaging applications, but is difficult to apply to diffuse
optical tomography (DOT) image reconstruction (inverse problem) due to complex
and unstructured geometries, non-linearity of the data fitting and
regularization terms, and non-differentiability of the regularization term. We
develop several approaches to overcome these difficulties by: i) defining
discrete differential operators for unstructured geometries using both finite
element and graph representations; ii) developing an optimization algorithm
based on the alternating direction method of multipliers (ADMM) for the
non-differentiable and non-linear minimization problem; iii) investigating
isotropic and anisotropic variants of TV regularization, and comparing their
finite element- and graph-based implementations. These approaches are evaluated
on experiments on simulated data and real data acquired from a tissue phantom.
Our results show that both FEM and graph-based TV regularization is able to
accurately reconstruct both sparse and non-sparse distributions without the
over-smoothing effect of Tikhonov regularization and the over-sparsifying
effect of L$_1$ regularization. The graph representation was found to
out-perform the FEM method for low-resolution meshes, and the FEM method was
found to be more accurate for high-resolution meshes.
Efficient Winograd Convolution via Integer Arithmetic
Authors: Lingchuan Meng, John Brothers
Date: 2019-01-07 00:00:00

Convolution is the core operation for many deep neural networks. The Winograd
convolution algorithms have been shown to accelerate the widely-used small
convolution sizes. Quantized neural networks can effectively reduce model sizes
and improve inference speed, which leads to a wide variety of kernels and
hardware accelerators that work with integer data. The state-of-the-art
Winograd algorithms pose challenges for efficient implementation and execution
by the integer kernels and accelerators. We introduce a new class of Winograd
algorithms by extending the construction to the field of complex and propose
optimizations that reduce the number of general multiplications. The new
algorithm achieves an arithmetic complexity reduction of $3.13$x over the
direct method and an efficiency gain up to $17.37\%$ over the rational
algorithms. Furthermore, we design and implement an integer-based filter
scaling scheme to effectively reduce the filter bit width by $30.77\%$ without
any significant accuracy loss.
Machine learning topological phases in real space
Authors: N. L. Holanda, M. A. R. Griffith
Date: 2019-01-07 00:00:00
Tasks: BIG-bench Machine Learning

We develop a supervised machine learning algorithm that is able to learn topological phases of finite condensed matter systems from bulk data in real lattice space. The algorithm employs diagonalization in real space together with any supervised learning algorithm to learn topological phases through an eigenvector ensembling procedure. We combine our algorithm with decision trees and random forests to successfully recover topological phase diagrams of Su-Schrieffer-Heeger (SSH) models from bulk lattice data in real space and show how the Shannon information entropy of ensembles of lattice eigenvectors can be used to retrieve a signal detailing how topological information is distributed in the bulk. We further use insights obtained from these information entropy signatures to engineer global topological features from real space lattice data that still carry most of the topological information in the lattice, while greatly diminishing the size of feature space, thus effectively amounting to a topological lattice compression. Finally, we explore the theoretical possibility of interpreting the information entropy topological signatures in terms of emergent information entropy wave functions, which lead us to Heisenberg and Hirschman uncertainty relations for topological phase transitions. The discovery of Shannon information entropy signals associated with topological phase transitions from the analysis of data from several thousand SSH systems illustrates how model explainability in machine learning can advance the research of exotic quantum materials with properties that may power future technological applications such as qubit engineering for quantum computing.
Learning-based Optimization of the Under-sampling Pattern in MRI
Authors: Cagla Deniz Bahadir, Adrian V. Dalca, Mert R. Sabuncu
Date: 2019-01-07 00:00:00

Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated by
under-sampling in k-space (i.e., the Fourier domain). In this paper, we
consider the problem of optimizing the sub-sampling pattern in a data-driven
fashion. Since the reconstruction model's performance depends on the
sub-sampling pattern, we combine the two problems. For a given sparsity
constraint, our method optimizes the sub-sampling pattern and reconstruction
model, using an end-to-end learning strategy. Our algorithm learns from
full-resolution data that are under-sampled retrospectively, yielding a
sub-sampling pattern and reconstruction model that are customized to the type
of images represented in the training data. The proposed method, which we call
LOUPE (Learning-based Optimization of the Under-sampling PattErn), was
implemented by modifying a U-Net, a widely-used convolutional neural network
architecture, that we append with the forward model that encodes the
under-sampling process. Our experiments with T1-weighted structural brain MRI
scans show that the optimized sub-sampling pattern can yield significantly more
accurate reconstructions compared to standard random uniform, variable density
or equispaced under-sampling schemes. The code is made available at:
https://github.com/cagladbahadir/LOUPE .
GASL: Guided Attention for Sparsity Learning in Deep Neural Networks
Authors: Amirsina Torfi, Rouzbeh A. Shirvani, Sobhan Soleymani, Naser M. Nasrabadi
Date: 2019-01-07 00:00:00
Tasks: Model Compression, Network Pruning

The main goal of network pruning is imposing sparsity on the neural network
by increasing the number of parameters with zero value in order to reduce the
architecture size and the computational speedup. In most of the previous
research works, sparsity is imposed stochastically without considering any
prior knowledge of the weights distribution or other internal network
characteristics. Enforcing too much sparsity may induce accuracy drop due to
the fact that a lot of important elements might have been eliminated. In this
paper, we propose Guided Attention for Sparsity Learning (GASL) to achieve (1)
model compression by having less number of elements and speed-up; (2) prevent
the accuracy drop by supervising the sparsity operation via a guided attention
mechanism and (3) introduce a generic mechanism that can be adapted for any
type of architecture; Our work is aimed at providing a framework based on
interpretable attention mechanisms for imposing structured and non-structured
sparsity in deep neural networks. For Cifar-100 experiments, we achieved the
state-of-the-art sparsity level and 2.91x speedup with competitive accuracy
compared to the best method. For MNIST and LeNet architecture we also achieved
the highest sparsity and speedup level.
DSConv: Efficient Convolution Operator
Authors: Marcelo Gennari, Roger Fawcett, Victor Adrian Prisacariu
Date: 2019-01-07 00:00:00
Tasks: Quantization

Quantization is a popular way of increasing the speed and lowering the memory usage of Convolution Neural Networks (CNNs). When labelled training data is available, network weights and activations have successfully been quantized down to 1-bit. The same cannot be said about the scenario when labelled training data is not available, e.g. when quantizing a pre-trained model, where current approaches show, at best, no loss of accuracy at 8-bit quantizations. We introduce DSConv, a flexible quantized convolution operator that replaces single-precision operations with their far less expensive integer counterparts, while maintaining the probability distributions over both the kernel weights and the outputs. We test our model as a plug-and-play replacement for standard convolution on most popular neural network architectures, ResNet, DenseNet, GoogLeNet, AlexNet and VGG-Net and demonstrate state-of-the-art results, with less than 1% loss of accuracy, without retraining, using only 4-bit quantization. We also show how a distillation-based adaptation stage with unlabelled data can improve results even further.
On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution
Authors: Yuqian Zhang, Yenson Lau, Han-Wen Kuo, Sky Cheung, Abhay Pasupathy, John Wright
Date: 2019-01-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Deblurring, Dictionary Learning, Image Deblurring

Blind deconvolution is the problem of recovering a convolutional kernel
$\boldsymbol a_0$ and an activation signal $\boldsymbol x_0$ from their
convolution $\boldsymbol y = \boldsymbol a_0 \circledast \boldsymbol x_0$. This
problem is ill-posed without further constraints or priors. This paper studies
the situation where the nonzero entries in the activation signal are sparsely
and randomly populated. We normalize the convolution kernel to have unit
Frobenius norm and cast the sparse blind deconvolution problem as a nonconvex
optimization problem over the sphere. With this spherical constraint, every
spurious local minimum turns out to be close to some signed shift truncation of
the ground truth, under certain hypotheses. This benign property motivates an
effective two stage algorithm that recovers the ground truth from the partial
information offered by a suboptimal local minimum. This geometry-inspired
algorithm recovers the ground truth for certain microscopy problems, also
exhibits promising performance in the more challenging image deblurring
problem. Our insights into the global geometry and the two stage algorithm
extend to the convolutional dictionary learning problem, where a superposition
of multiple convolution signals is observed.
Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure
Authors: Endang Wahyu Pamungkas, Valerio Basile, Viviana Patti
Date: 2019-01-07 00:00:00
Tasks: Classification, General Classification, Misinformation, Rumour Detection, Stance Classification

Analysing how people react to rumours associated with news in social media is
an important task to prevent the spreading of misinformation, which is nowadays
widely recognized as a dangerous tendency. In social media conversations, users
show different stances and attitudes towards rumourous stories. Some users take
a definite stance, supporting or denying the rumour at issue, while others just
comment it, or ask for additional evidence related to the veracity of the
rumour. On this line, a new shared task has been proposed at SemEval-2017 (Task
8, SubTask A), which is focused on rumour stance classification in English
tweets. The goal is predicting user stance towards emerging rumours in Twitter,
in terms of supporting, denying, querying, or commenting the original rumour,
looking at the conversation threads originated by the rumour. This paper
describes a new approach to this task, where the use of conversation-based and
affective-based features, covering different facets of affect, has been
explored. Our classification model outperforms the best-performing systems for
stance classification at SemEval-2017 Task 8, showing the effectiveness of the
feature set proposed.
Scale-Aware Trident Networks for Object Detection
Authors: Yanghao Li, Yuntao Chen, Naiyan Wang, Zhao-Xiang Zhang
Date: 2019-01-07 00:00:00
Proceeding: ICCV 2019 10
Tasks: Object, object-detection, Object Detection

Scale variation is one of the key challenges in object detection. In this work, we first present a controlled experiment to investigate the effect of receptive fields for scale variation in object detection. Based on the findings from the exploration experiments, we propose a novel Trident Network (TridentNet) aiming to generate scale-specific feature maps with a uniform representational power. We construct a parallel multi-branch architecture in which each branch shares the same transformation parameters but with different receptive fields. Then, we adopt a scale-aware training scheme to specialize each branch by sampling object instances of proper scales for training. As a bonus, a fast approximation version of TridentNet could achieve significant improvements without any additional parameters and computational cost compared with the vanilla detector. On the COCO dataset, our TridentNet with ResNet-101 backbone achieves state-of-the-art single-model results of 48.4 mAP. Codes are available at https://git.io/fj5vR.
Monocular Neural Image Based Rendering with Continuous View Control
Authors: Xu Chen, Jie Song, Otmar Hilliges
Date: 2019-01-07 00:00:00
Proceeding: ICCV 2019 10
Tasks: Novel View Synthesis

We present an approach that learns to synthesize high-quality, novel views of 3D objects or scenes, while providing fine-grained and precise control over the 6-DOF viewpoint. The approach is self-supervised and only requires 2D images and associated view transforms for training. Our main contribution is a network architecture that leverages a transforming auto-encoder in combination with a depth-guided warping procedure to predict geometrically accurate unseen views. Leveraging geometric constraints renders direct supervision via depth or flow maps unnecessary. If large parts of the object are occluded in the source view, a purely learning based prior is used to predict the values for dis-occluded pixels. Our network furthermore predicts a per-pixel mask, used to fuse depth-guided and pixel-based predictions. The resulting images reflect the desired 6-DOF transformation and details are preserved. We thoroughly evaluate our architecture on synthetic and real scenes and under fine-grained and fixed-view settings. Finally, we demonstrate that the approach generalizes to entirely unseen images such as product images downloaded from the internet.
Mutual Context Network for Jointly Estimating Egocentric Gaze and Actions
Authors: Yifei Huang, Zhenqiang Li, Minjie Cai, Yoichi Sato
Date: 2019-01-07 00:00:00
Tasks: Action Recognition, Gaze Prediction, Prediction, Temporal Action Localization

In this work, we address two coupled tasks of gaze prediction and action recognition in egocentric videos by exploring their mutual context. Our assumption is that in the procedure of performing a manipulation task, what a person is doing determines where the person is looking at, and the gaze point reveals gaze and non-gaze regions which contain important and complementary information about the undergoing action. We propose a novel mutual context network (MCN) that jointly learns action-dependent gaze prediction and gaze-guided action recognition in an end-to-end manner. Experiments on public egocentric video datasets demonstrate that our MCN achieves state-of-the-art performance of both gaze prediction and action recognition.