pwc-archive/papers-with-abstracts huggingface dataset:
Reconstruction of Electrical Impedance Tomography Using Fish School Search, Non-Blind Search, and Genetic Algorithm
Authors: Valter Augusto de Freitas Barbosa, Reiga Ramalho Ribeiro, Allan Rivalles Souza Feitosa, Victor Luiz Bezerra Araújo da Silva, Arthur Diego Dias Rocha, Rafaela Covello de Freitas, Ricardo Emmanuel de Souza, Wellington Pinheiro dos Santos
Date: 2017-12-03 00:00:00
Tasks: Image Reconstruction

Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that
does not use ionizing radiation, with application both in environmental
sciences and in health. Image reconstruction is performed by solving an inverse
problem and ill-posed. Evolutionary Computation and Swarm Intelligence have
become a source of methods for solving inverse problems. Fish School Search
(FSS) is a promising search and optimization method, based on the dynamics of
schools of fish. In this article the authors present a method for
reconstruction of EIT images based on FSS and Non-Blind Search (NBS). The
method was evaluated using numerical phantoms consisting of electrical
conductivity images with subjects in the center, between the center and the
edge and on the edge of a circular section, with meshes of 415 finite elements.
The authors performed 20 simulations for each configuration. Results showed
that both FSS and FSS-NBS were able to converge faster than genetic algorithms.
Accelerating Kernel Classifiers Through Borders Mapping
Authors: Peter Mills
Date: 2017-08-20 00:00:00
Tasks: General Classification

Support vector machines (SVM) and other kernel techniques represent a family of powerful statistical classification methods with high accuracy and broad applicability. Because they use all or a significant portion of the training data, however, they can be slow, especially for large problems. Piecewise linear classifiers are similarly versatile, yet have the additional advantages of simplicity, ease of interpretation and, if the number of component linear classifiers is not too large, speed. Here we show how a simple, piecewise linear classifier can be trained from a kernel-based classifier in order to improve the classification speed. The method works by finding the root of the difference in conditional probabilities between pairs of opposite classes to build up a representation of the decision boundary. When tested on 17 different datasets, it succeeded in improving the classification speed of a SVM for 12 of them by up to two orders-of-magnitude. Of these, two were less accurate than a simple, linear classifier. The method is best suited to problems with continuum features data and smooth probability functions. Because the component linear classifiers are built up individually from an existing classifier, rather than through a simultaneous optimization procedure, the classifier is also fast to train.
Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary
Authors: Masataro Asai, Alex Fukunaga
Date: 2017-04-29 00:00:00

Current domain-independent, classical planners require symbolic models of the
problem domain and instance as input, resulting in a knowledge acquisition
bottleneck. Meanwhile, although deep learning has achieved significant success
in many fields, the knowledge is encoded in a subsymbolic representation which
is incompatible with symbolic systems such as planners. We propose LatPlan, an
unsupervised architecture combining deep learning and classical planning. Given
only an unlabeled set of image pairs showing a subset of transitions allowed in
the environment (training inputs), and a pair of images representing the
initial and the goal states (planning inputs), LatPlan finds a plan to the goal
state in a symbolic latent space and returns a visualized plan execution. The
contribution of this paper is twofold: (1) State Autoencoder, which finds a
propositional state representation of the environment using a Variational
Autoencoder. It generates a discrete latent vector from the images, based on
which a PDDL model can be constructed and then solved by an off-the-shelf
planner. (2) Action Autoencoder / Discriminator, a neural architecture which
jointly finds the action symbols and the implicit action models
(preconditions/effects), and provides a successor function for the implicit
graph search. We evaluate LatPlan using image-based versions of 3 planning
domains: 8-puzzle, Towers of Hanoi and LightsOut.
Learning Human Motion Models for Long-term Predictions
Authors: Partha Ghosh, Jie Song, Emre Aksan, Otmar Hilliges
Date: 2017-04-10 00:00:00

We propose a new architecture for the learning of predictive spatio-temporal
motion models from data alone. Our approach, dubbed the Dropout Autoencoder
LSTM, is capable of synthesizing natural looking motion sequences over long
time horizons without catastrophic drift or motion degradation. The model
consists of two components, a 3-layer recurrent neural network to model
temporal aspects and a novel auto-encoder that is trained to implicitly recover
the spatial structure of the human skeleton via randomly removing information
about joints during training time. This Dropout Autoencoder (D-AE) is then used
to filter each predicted pose of the LSTM, reducing accumulation of error and
hence drift over time. Furthermore, we propose new evaluation protocols to
assess the quality of synthetic motion sequences even for which no ground truth
data exists. The proposed protocols can be used to assess generated sequences
of arbitrary length. Finally, we evaluate our proposed method on two of the
largest motion-capture datasets available to date and show that our model
outperforms the state-of-the-art on a variety of actions, including cyclic and
acyclic motion, and that it can produce natural looking sequences over longer
time horizons than previous methods.
Accelerated Block Coordinate Proximal Gradients with Applications in High Dimensional Statistics
Authors: Tsz Kit Lau, Yuan YAO
Date: 2017-10-15 00:00:00
Tasks: BIG-bench Machine Learning, regression, Vocal Bursts Intensity Prediction

Nonconvex optimization problems arise in different research fields and arouse
lots of attention in signal processing, statistics and machine learning. In
this work, we explore the accelerated proximal gradient method and some of its
variants which have been shown to converge under nonconvex context recently. We
show that a novel variant proposed here, which exploits adaptive momentum and
block coordinate update with specific update rules, further improves the
performance of a broad class of nonconvex problems. In applications to sparse
linear regression with regularizations like Lasso, grouped Lasso, capped
$\ell_1$ and SCAP, the proposed scheme enjoys provable local linear
convergence, with experimental justification.
Thinking Fast and Slow with Deep Learning and Tree Search
Authors: Thomas Anthony, Zheng Tian, David Barber
Date: 2017-05-23 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Decision Making, Deep Learning, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making, Structured Prediction

Sequential decision making problems, such as structured prediction, robotic
control, and game playing, require a combination of planning policies and
generalisation of those plans. In this paper, we present Expert Iteration
(ExIt), a novel reinforcement learning algorithm which decomposes the problem
into separate planning and generalisation tasks. Planning new policies is
performed by tree search, while a deep neural network generalises those plans.
Subsequently, tree search is improved by using the neural network policy to
guide search, increasing the strength of new plans. In contrast, standard deep
Reinforcement Learning algorithms rely on a neural network not only to
generalise plans, but to discover them too. We show that ExIt outperforms
REINFORCE for training a neural network to play the board game Hex, and our
final tree search agent, trained tabula rasa, defeats MoHex 1.0, the most
recent Olympiad Champion player to be publicly released.
Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks
Authors: Guohao Li, Hang Su, Wenwu Zhu
Date: 2017-12-03 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) has attracted much attention since it offers
insight into the relationships between the multi-modal analysis of images and
natural language. Most of the current algorithms are incapable of answering
open-domain questions that require to perform reasoning beyond the image
contents. To address this issue, we propose a novel framework which endows the
model capabilities in answering more complex questions by leveraging massive
external knowledge with dynamic memory networks. Specifically, the questions
along with the corresponding images trigger a process to retrieve the relevant
information in external knowledge bases, which are embedded into a continuous
vector space by preserving the entity-relation structures. Afterwards, we
employ dynamic memory networks to attend to the large body of facts in the
knowledge graph and images, and then perform reasoning over these facts to
generate corresponding answers. Extensive experiments demonstrate that our
model not only achieves the state-of-the-art performance in the visual question
answering task, but can also answer open-domain questions effectively by
leveraging the external knowledge.
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
Authors: Hongwei Wang, Fuzheng Zhang, Min Hou, Xing Xie, Minyi Guo, Qi Liu
Date: 2017-12-03 00:00:00
Tasks: Link Prediction, Network Embedding, Relation, Sentiment Classification

In online social networks people often express attitudes towards others,
which forms massive sentiment links among users. Predicting the sign of
sentiment links is a fundamental task in many areas such as personal
advertising and public opinion analysis. Previous works mainly focus on textual
sentiment classification, however, text information can only disclose the "tip
of the iceberg" about users' true opinions, of which the most are unobserved
but implied by other sources of information such as social relation and users'
profile. To address this problem, in this paper we investigate how to predict
possibly existing sentiment links in the presence of heterogeneous information.
First, due to the lack of explicit sentiment links in mainstream social
networks, we establish a labeled heterogeneous sentiment dataset which consists
of users' sentiment relation, social relation and profile knowledge by
entity-level sentiment extraction method. Then we propose a novel and flexible
end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework
to extract users' latent representations from heterogeneous networks and
predict the sign of unobserved sentiment links. SHINE utilizes multiple deep
autoencoders to map each user into a low-dimension feature space while
preserving the network structure. We demonstrate the superiority of SHINE over
state-of-the-art baselines on link prediction and node recommendation in two
real-world datasets. The experimental results also prove the efficacy of SHINE
in cold start scenario.
Joint Topic-Semantic-aware Social Recommendation for Online Voting
Authors: Hongwei Wang, Jia Wang, Miao Zhao, Jiannong Cao, Minyi Guo
Date: 2017-12-03 00:00:00

Online voting is an emerging feature in social networks, in which users can
express their attitudes toward various issues and show their unique interest.
Online voting imposes new challenges on recommendation, because the propagation
of votings heavily depends on the structure of social networks as well as the
content of votings. In this paper, we investigate how to utilize these two
factors in a comprehensive manner when doing voting recommendation. First, due
to the fact that existing text mining methods such as topic model and semantic
model cannot well process the content of votings that is typically short and
ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to
learn word and document representation by jointly considering their topics and
semantics. Then we propose our Joint Topic-Semantic-aware social Matrix
Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates
similarity among users and votings by combining their TEWE representation and
structural information of social networks, and preserves this
topic-semantic-social similarity during matrix factorization. To evaluate the
performance of TEWE representation and JTS-MF model, we conduct extensive
experiments on real online voting dataset. The results prove the efficacy of
our approach against several state-of-the-art baselines.
Cascade R-CNN: Delving into High Quality Object Detection
Authors: Zhaowei Cai, Nuno Vasconcelos
Date: 2017-12-03 00:00:00
Proceeding: CVPR 2018 6
Tasks: 2D Object Detection, Object, Object Detection, Vocal Bursts Intensity Prediction

In object detection, an intersection over union (IoU) threshold is required
to define positives and negatives. An object detector, trained with low IoU
threshold, e.g. 0.5, usually produces noisy detections. However, detection
performance tends to degrade with increasing the IoU thresholds. Two main
factors are responsible for this: 1) overfitting during training, due to
exponentially vanishing positive samples, and 2) inference-time mismatch
between the IoUs for which the detector is optimal and those of the input
hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, is
proposed to address these problems. It consists of a sequence of detectors
trained with increasing IoU thresholds, to be sequentially more selective
against close false positives. The detectors are trained stage by stage,
leveraging the observation that the output of a detector is a good distribution
for training the next higher quality detector. The resampling of progressively
improved hypotheses guarantees that all detectors have a positive set of
examples of equivalent size, reducing the overfitting problem. The same cascade
procedure is applied at inference, enabling a closer match between the
hypotheses and the detector quality of each stage. A simple implementation of
the Cascade R-CNN is shown to surpass all single-model object detectors on the
challenging COCO dataset. Experiments also show that the Cascade R-CNN is
widely applicable across detector architectures, achieving consistent gains
independently of the baseline detector strength. The code will be made
available at https://github.com/zhaoweicai/cascade-rcnn.
Sentiment Classification using Images and Label Embeddings
Authors: Laura Graesser, Abhinav Gupta, Lakshay Sharma, Evelina Bakhturina
Date: 2017-12-03 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

In this project we analysed how much semantic information images carry, and
how much value image data can add to sentiment analysis of the text associated
with the images. To better understand the contribution from images, we compared
models which only made use of image data, models which only made use of text
data, and models which combined both data types. We also analysed if this
approach could help sentiment classifiers generalize to unknown sentiments.
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Authors: Bichen Wu, Alvin Wan, Xiangyu Yue, Peter Jin, Sicheng Zhao, Noah Golmant, Amir Gholaminejad, Joseph Gonzalez, Kurt Keutzer
Date: 2017-11-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Face Verification, Image Classification, Style Transfer

Neural networks rely on convolutions to aggregate spatial information.
However, spatial convolutions are expensive in terms of model size and
computation, both of which grow quadratically with respect to kernel size. In
this paper, we present a parameter-free, FLOP-free "shift" operation as an
alternative to spatial convolutions. We fuse shifts and point-wise convolutions
to construct end-to-end trainable shift-based modules, with a hyperparameter
characterizing the tradeoff between accuracy and efficiency. To demonstrate the
operation's efficacy, we replace ResNet's 3x3 convolutions with shift-based
modules for improved CIFAR10 and CIFAR100 accuracy using 60% fewer parameters;
we additionally demonstrate the operation's resilience to parameter reduction
on ImageNet, outperforming ResNet family members. We finally show the shift
operation's applicability across domains, achieving strong performance with
fewer parameters on classification, face verification and style transfer.
Feature Agglomeration Networks for Single Stage Face Detection
Authors: Jialiang Zhang, Xiongwei Wu, Jianke Zhu, Steven C. H. Hoi
Date: 2017-12-03 00:00:00
Tasks: Face Detection, GPU

Recent years have witnessed promising results of face detection using deep
learning. Despite making remarkable progresses, face detection in the wild
remains an open research challenge especially when detecting faces at vastly
different scales and characteristics. In this paper, we propose a novel simple
yet effective framework of "Feature Agglomeration Networks" (FANet) to build a
new single stage face detector, which not only achieves state-of-the-art
performance but also runs efficiently. As inspired by Feature Pyramid Networks
(FPN), the key idea of our framework is to exploit inherent multi-scale
features of a single convolutional neural network by aggregating higher-level
semantic feature maps of different scales as contextual cues to augment
lower-level feature maps via a hierarchical agglomeration manner at marginal
extra computation cost. We further propose a Hierarchical Loss to effectively
train the FANet model. We evaluate the proposed FANet detector on several
public face detection benchmarks, including PASCAL face, FDDB and WIDER FACE
datasets and achieved state-of-the-art results. Our detector can run in real
time for VGA-resolution images on GPU.
Automatic Recognition of Coal and Gangue based on Convolution Neural Network
Authors: Huichao Hong, Lixin Zheng, Jianqing Zhu, Shuwan Pan, Kaiting Zhou
Date: 2017-12-03 00:00:00
Tasks: object-detection, Object Detection, Transfer Learning

We designed a gangue sorting system,and built a convolutional neural network
model based on AlexNet. Data enhancement and transfer learning are used to
solve the problem which the convolution neural network has insufficient
training data in the training stage. An object detection and region clipping
algorithm is proposed to adjust the training image data to the optimum size.
Compared with traditional neural network and SVM algorithm, this algorithm has
higher recognition rate for coal and coal gangue, and provides important
reference for identification and separation of coal and gangue.
Objective Classes for Micro-Facial Expression Recognition
Authors: Adrian K. Davison, Walied Merghani, Moi Hoon Yap
Date: 2017-08-24 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), General Classification, Micro Expression Recognition, Micro-Expression Recognition

Micro-expressions are brief spontaneous facial expressions that appear on a
face when a person conceals an emotion, making them different to normal facial
expressions in subtlety and duration. Currently, emotion classes within the
CASME II dataset are based on Action Units and self-reports, creating conflicts
during machine learning training. We will show that classifying expressions
using Action Units, instead of predicted emotion, removes the potential bias of
human reporting. The proposed classes are tested using LBP-TOP, HOOF and HOG 3D
feature descriptors. The experiments are evaluated on two benchmark FACS coded
datasets: CASME II and SAMM. The best result achieves 86.35\% accuracy when
classifying the proposed 5 classes on CASME II using HOG 3D, outperforming the
result of the state-of-the-art 5-class emotional-based classification in CASME
II. Results indicate that classification based on Action Units provides an
objective method to improve micro-expression recognition.
Convolutional Phase Retrieval via Gradient Descent
Authors: Qing Qu, Yuqian Zhang, Yonina C. Eldar, John Wright
Date: 2017-12-03 00:00:00
Tasks: Retrieval

We study the convolutional phase retrieval problem, of recovering an unknown signal $\mathbf x \in \mathbb C^n $ from $m$ measurements consisting of the magnitude of its cyclic convolution with a given kernel $\mathbf a \in \mathbb C^m $. This model is motivated by applications such as channel estimation, optics, and underwater acoustic communication, where the signal of interest is acted on by a given channel/filter, and phase information is difficult or impossible to acquire. We show that when $\mathbf a$ is random and the number of observations $m$ is sufficiently large, with high probability $\mathbf x$ can be efficiently recovered up to a global phase shift using a combination of spectral initialization and generalized gradient descent. The main challenge is coping with dependencies in the measurement operator. We overcome this challenge by using ideas from decoupling theory, suprema of chaos processes and the restricted isometry property of random circulant matrices, and recent analysis of alternating minimization methods.
Spatial PixelCNN: Generating Images from Patches
Authors: Nader Akoury, Anh Nguyen
Date: 2017-12-03 00:00:00

In this paper we propose Spatial PixelCNN, a conditional autoregressive model
that generates images from small patches. By conditioning on a grid of pixel
coordinates and global features extracted from a Variational Autoencoder (VAE),
we are able to train on patches of images, and reproduce the full-sized image.
We show that it not only allows for generating high quality samples at the same
resolution as the underlying dataset, but is also capable of upscaling images
to arbitrary resolutions (tested at resolutions up to $50\times$) on the MNIST
dataset. Compared to a PixelCNN++ baseline, Spatial PixelCNN quantitatively and
qualitatively achieves similar performance on the MNIST dataset.
Evaluation of Alzheimer's Disease by Analysis of MR Images using Multilayer Perceptrons and Kohonen SOM Classifiers as an Alternative to the ADC Maps
Authors: Wellington Pinheiro dos Santos, Ricardo Emmanuel de Souza, Plínio B. dos Santos Filho
Date: 2017-12-03 00:00:00
Tasks: Classification, General Classification

Alzheimer's disease is the most common cause of dementia, yet hard to
diagnose precisely without invasive techniques, particularly at the onset of
the disease. This work approaches image analysis and classification of
synthetic multispectral images composed by diffusion-weighted magnetic
resonance (MR) cerebral images for the evaluation of cerebrospinal fluid area
and measuring the advance of Alzheimer's disease. A clinical 1.5 T MR imaging
system was used to acquire all images presented. The classification methods are
based on multilayer perceptrons and Kohonen Self-Organized Map classifiers. We
assume the classes of interest can be separated by hyperquadrics. Therefore, a
2-degree polynomial network is used to classify the original image, generating
the ground truth image. The classification results are used to improve the
usual analysis of the apparent diffusion coefficient map.