pwc-archive/papers-with-abstracts huggingface dataset:
An All-Pair Quantum SVM Approach for Big Data Multiclass Classification
Authors: Arit Kumar Bishwas, Ashish Mani, Vasile Palade
Date: 2017-04-25 00:00:00
Tasks: All, Binary Classification, Classification, General Classification

In this paper, we have discussed a quantum approach for the all-pair
multiclass classification problem. We have shown that the multiclass support
vector machine for big data classification with a quantum all-pair approach can
be implemented in logarithm runtime complexity on a quantum computer. In an
all-pair approach, there is one binary classification problem for each pair of
classes, and so there are k (k-1)/2 classifiers for a k-class problem. As
compared to the classical multiclass support vector machine that can be
implemented with polynomial run time complexity, our approach exhibits
exponential speed up in the quantum version. The quantum all-pair algorithm can
be used with other classification algorithms, and a speed up gain can be
achieved as compared to their classical counterparts.
Simplifying Sentences with Sequence to Sequence Models
Authors: Alexander Mathews, Lexing Xie, Xuming He
Date: 2018-05-15 00:00:00
Tasks: Articles, Style Transfer, Text Generation, Word Embeddings

We simplify sentences with an attentive neural network sequence to sequence
model, dubbed S4. The model includes a novel word-copy mechanism and loss
function to exploit linguistic similarities between the original and simplified
sentences. It also jointly uses pre-trained and fine-tuned word embeddings to
capture the semantics of complex sentences and to mitigate the effects of
limited data. When trained and evaluated on pairs of sentences from thousands
of news articles, we observe a 8.8 point improvement in BLEU score over a
sequence to sequence baseline; however, learning word substitutions remains
difficult. Such sequence to sequence models are promising for other text
generation tasks such as style transfer.
ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids
Authors: Dinesh Jayaraman, Ruohan Gao, Kristen Grauman
Date: 2017-09-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decoder, Object, Object Recognition

We introduce an unsupervised feature learning approach that embeds 3D shape
information into a single-view image representation. The main idea is a
self-supervised training objective that, given only a single 2D image, requires
all unseen views of the object to be predictable from learned features. We
implement this idea as an encoder-decoder convolutional neural network. The
network maps an input image of an unknown category and unknown viewpoint to a
latent space, from which a deconvolutional decoder can best "lift" the image to
its complete viewgrid showing the object from all viewing angles. Our
class-agnostic training procedure encourages the representation to capture
fundamental shape primitives and semantic regularities in a data-driven
manner---without manual semantic labels. Our results on two widely-used shape
datasets show 1) our approach successfully learns to perform "mental rotation"
even for objects unseen during training, and 2) the learned latent space is a
powerful representation for object recognition, outperforming several existing
unsupervised feature learning methods.
On Learning Associations of Faces and Voices
Authors: Changil Kim, Hijung Valentina Shin, Tae-Hyun Oh, Alexandre Kaspar, Mohamed Elgharib, Wojciech Matusik
Date: 2018-05-15 00:00:00
Tasks: Speaker Identification

In this paper, we study the associations between human faces and voices.
Audiovisual integration, specifically the integration of facial and vocal
information is a well-researched area in neuroscience. It is shown that the
overlapping information between the two modalities plays a significant role in
perceptual tasks such as speaker identification. Through an online study on a
new dataset we created, we confirm previous findings that people can associate
unseen faces with corresponding voices and vice versa with greater than chance
accuracy. We computationally model the overlapping information between faces
and voices and show that the learned cross-modal representation contains enough
information to identify matching faces and voices with performance similar to
that of humans. Our representation exhibits correlations to certain demographic
attributes and features obtained from either visual or aural modality alone. We
release our dataset of audiovisual recordings and demographic annotations of
people reading out short text used in our studies.
End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions
Authors: Wenpeng Yin, Hinrich Schütze, Dan Roth
Date: 2018-04-24 00:00:00
Proceeding: ACL 2018 7
Tasks: Natural Language Inference, Position, Question Answering, RTE, Sentence

This work deals with SciTail, a natural entailment challenge derived from a
multi-choice question answering problem. The premises and hypotheses in SciTail
were generated with no awareness of each other, and did not specifically aim at
the entailment task. This makes it more challenging than other entailment data
sets and more directly useful to the end-task -- question answering. We propose
DEISTE (deep explorations of inter-sentence interactions for textual
entailment) for this entailment task. Given word-to-word interactions between
the premise-hypothesis pair ($P$, $H$), DEISTE consists of: (i) a
parameter-dynamic convolution to make important words in $P$ and $H$ play a
dominant role in learnt representations; and (ii) a position-aware attentive
convolution to encode the representation and position information of the
aligned word pairs. Experiments show that DEISTE gets $\approx$5\% improvement
over prior state of the art and that the pretrained DEISTE on SciTail
generalizes well on RTE-5.
Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students
Authors: Chenglin Yang, Lingxi Xie, Siyuan Qiao, Alan Yuille
Date: 2018-05-15 00:00:00
Tasks: General Classification, image-classification, Image Classification, Knowledge Distillation

We focus on the problem of training a deep neural network in generations. The
flowchart is that, in order to optimize the target network (student), another
network (teacher) with the same architecture is first trained, and used to
provide part of supervision signals in the next stage. While this strategy
leads to a higher accuracy, many aspects (e.g., why teacher-student
optimization helps) still need further explorations.
  This paper studies this problem from a perspective of controlling the
strictness in training the teacher network. Existing approaches mostly used a
hard distribution (e.g., one-hot vectors) in training, leading to a strict
teacher which itself has a high accuracy, but we argue that the teacher needs
to be more tolerant, although this often implies a lower accuracy. The
implementation is very easy, with merely an extra loss term added to the
teacher network, facilitating a few secondary classes to emerge and complement
to the primary class. Consequently, the teacher provides a milder supervision
signal (a less peaked distribution), and makes it possible for the student to
learn from inter-class similarity and potentially lower the risk of
over-fitting. Experiments are performed on standard image classification tasks
(CIFAR100 and ILSVRC2012). Although the teacher network behaves less powerful,
the students show a persistent ability growth and eventually achieve higher
classification accuracies than other competitors. Model ensemble and transfer
feature extraction also verify the effectiveness of our approach.
A Manually Annotated Chinese Corpus for Non-task-oriented Dialogue Systems
Authors: Jing Li, Yan Song, Haisong Zhang, Shuming Shi
Date: 2018-05-15 00:00:00
Tasks: Informativeness, Task-Oriented Dialogue Systems

This paper presents a large-scale corpus for non-task-oriented dialogue
response selection, which contains over 27K distinct prompts more than 82K
responses collected from social media. To annotate this corpus, we define a
5-grade rating scheme: bad, mediocre, acceptable, good, and excellent,
according to the relevance, coherence, informativeness, interestingness, and
the potential to move a conversation forward. To test the validity and
usefulness of the produced corpus, we compare various unsupervised and
supervised models for response selection. Experimental results confirm that the
proposed corpus is helpful in training response selection models.
A Dynamic Neural Network Approach to Generating Robot's Novel Actions: A Simulation Experiment
Authors: Jungsik Hwang, Jun Tani
Date: 2018-05-15 00:00:00

In this study, we investigate how a robot can generate novel and creative
actions from its own experience of learning basic actions. Inspired by a
machine learning approach to computational creativity, we propose a dynamic
neural network model that can learn and generate robot's actions. We conducted
a set of simulation experiments with a humanoid robot. The results showed that
the proposed model was able to learn the basic actions and also to generate
novel actions by modulating and combining those learned actions. The analysis
on the neural activities illustrated that the ability to generate creative
actions emerged from the model's nonlinear memory structure self-organized
during training. The results also showed that the different way of learning the
basic actions induced the self-organization of the memory structure with the
different characteristics, resulting in the generation of different levels of
creative actions. Our approach can be utilized in human-robot interaction in
which a user can interactively explore the robot's memory to control its
behavior and also discover other novel actions.
Advances in Experience Replay
Authors: Tracy Wan, Neil Xu
Date: 2018-05-15 00:00:00
Tasks: OpenAI Gym

This project combines recent advances in experience replay techniques,
namely, Combined Experience Replay (CER), Prioritized Experience Replay (PER),
and Hindsight Experience Replay (HER). We show the results of combinations of
these techniques with DDPG and DQN methods. CER always adds the most recent
experience to the batch. PER chooses which experiences should be replayed based
on how beneficial they will be towards learning. HER learns from failure by
substituting the desired goal with the achieved goal and recomputing the reward
function. The effectiveness of combinations of these experience replay
techniques is tested in a variety of OpenAI gym environments.
Deep Learning in Spiking Neural Networks
Authors: Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh, Timothee Masquelier, Anthony S. Maida
Date: 2018-04-22 00:00:00
Tasks: Deep Learning

In recent years, deep learning has been a revolution in the field of machine
learning, for computer vision in particular. In this approach, a deep
(multilayer) artificial neural network (ANN) is trained in a supervised manner
using backpropagation. Huge amounts of labeled examples are required, but the
resulting classification accuracy is truly impressive, sometimes outperforming
humans. Neurons in an ANN are characterized by a single, static,
continuous-valued activation. Yet biological neurons use discrete spikes to
compute and transmit information, and the spike times, in addition to the spike
rates, matter. Spiking neural networks (SNNs) are thus more biologically
realistic than ANNs, and arguably the only viable option if one wants to
understand how the brain computes. SNNs are also more hardware friendly and
energy-efficient than ANNs, and are thus appealing for technology, especially
for portable devices. However, training deep SNNs remains a challenge. Spiking
neurons' transfer function is usually non-differentiable, which prevents using
backpropagation. Here we review recent supervised and unsupervised methods to
train deep SNNs, and compare them in terms of accuracy, but also computational
cost and hardware friendliness. The emerging picture is that SNNs still lag
behind ANNs in terms of accuracy, but the gap is decreasing, and can even
vanish on some tasks, while the SNNs typically require much fewer operations.
A Non-monotone Alternating Updating Method for A Class of Matrix Factorization Problems
Authors: Lei Yang, Ting Kei Pong, Xiaojun Chen
Date: 2017-05-18 00:00:00
Tasks: Matrix Completion

In this paper we consider a general matrix factorization model which covers a
large class of existing models with many applications in areas such as machine
learning and imaging sciences. To solve this possibly nonconvex, nonsmooth and
non-Lipschitz problem, we develop a non-monotone alternating updating method
based on a potential function. Our method essentially updates two blocks of
variables in turn by inexactly minimizing this potential function, and updates
another auxiliary block of variables using an explicit formula. The special
structure of our potential function allows us to take advantage of efficient
computational strategies for non-negative matrix factorization to perform the
alternating minimization over the two blocks of variables. A suitable line
search criterion is also incorporated to improve the numerical performance.
Under some mild conditions, we show that the line search criterion is well
defined, and establish that the sequence generated is bounded and any cluster
point of the sequence is a stationary point. Finally, we conduct some numerical
experiments using real datasets to compare our method with some existing
efficient methods for non-negative matrix factorization and matrix completion.
The numerical results show that our method can outperform these methods for
these specific applications.
A Revisit of Hashing Algorithms for Approximate Nearest Neighbor Search
Authors: Deng Cai
Date: 2016-12-22 00:00:00

Approximate Nearest Neighbor Search (ANNS) is a fundamental problem in many areas of machine learning and data mining. During the past decade, numerous hashing algorithms are proposed to solve this problem. Every proposed algorithm claims outperform other state-of-the-art hashing methods. However, the evaluation of these hashing papers was not thorough enough, and those claims should be re-examined. The ultimate goal of an ANNS method is returning the most accurate answers (nearest neighbors) in the shortest time. If implemented correctly, almost all the hashing methods will have their performance improved as the code length increases. However, many existing hashing papers only report the performance with the code length shorter than 128. In this paper, we carefully revisit the problem of search with a hash index, and analyze the pros and cons of two popular hash index search procedures. Then we proposed a very simple but effective two level index structures and make a thorough comparison of eleven popular hashing algorithms. Surprisingly, the random-projection-based Locality Sensitive Hashing (LSH) is the best performed algorithm, which is in contradiction to the claims in all the other ten hashing papers. Despite the extreme simplicity of random-projection-based LSH, our results show that the capability of this algorithm has been far underestimated. For the sake of reproducibility, all the codes used in the paper are released on GitHub, which can be used as a testing platform for a fair comparison between various hashing algorithms.
Fully Associative Patch-based 1-to-N Matcher for Face Recognition
Authors: Lingfeng Zhang, Ioannis A. Kakadiaris
Date: 2018-05-15 00:00:00
Tasks: Face Recognition

This paper focuses on improving face recognition performance by a patch-based
1-to-N signature matcher that learns correlations between different facial
patches. A Fully Associative Patch-based Signature Matcher (FAPSM) is proposed
so that the local matching identity of each patch contributes to the global
matching identities of all the patches. The proposed matcher consists of three
steps. First, based on the signature, the local matching identity and the
corresponding matching score of each patch are computed. Then, a fully
associative weight matrix is learned to obtain the global matching identities
and scores of all the patches. At last, the l1-regularized weighting is applied
to combine the global matching identity of each patch and obtain a final
matching identity. The proposed matcher has been integrated with the UR2D
system for evaluation. The experimental results indicate that the proposed
matcher achieves better performance than the current UR2D system. The Rank-1
accuracy is improved significantly by 3% and 0.55% on the UHDB31 dataset and
the IJB-A dataset, respectively.
On Adaptive Estimation for Dynamic Bernoulli Bandits
Authors: Xue Lu, Niall Adams, Nikolas Kantas
Date: 2017-12-08 00:00:00
Tasks: Thompson Sampling

The multi-armed bandit (MAB) problem is a classic example of the
exploration-exploitation dilemma. It is concerned with maximising the total
rewards for a gambler by sequentially pulling an arm from a multi-armed slot
machine where each arm is associated with a reward distribution. In static
MABs, the reward distributions do not change over time, while in dynamic MABs,
each arm's reward distribution can change, and the optimal arm can switch over
time. Motivated by many real applications where rewards are binary, we focus on
dynamic Bernoulli bandits. Standard methods like $\epsilon$-Greedy and Upper
Confidence Bound (UCB), which rely on the sample mean estimator, often fail to
track changes in the underlying reward for dynamic problems. In this paper, we
overcome the shortcoming of slow response to change by deploying adaptive
estimation in the standard methods and propose a new family of algorithms,
which are adaptive versions of $\epsilon$-Greedy, UCB, and Thompson sampling.
These new methods are simple and easy to implement. Moreover, they do not
require any prior knowledge about the dynamic reward process, which is
important for real applications. We examine the new algorithms numerically in
different scenarios and the results show solid improvements of our algorithms
in dynamic environments.
Formal Modelling of Ontologies : An Event-B based Approach Using the Rodin Platform
Authors: Yamine Ait Ameur, Idir Ait Sadoune, Kahina Hacid, Linda Mohand Oussaid
Date: 2018-05-15 00:00:00

This paper reports on the results of the French ANR IMPEX research project
dealing with making explicit domain knowledge in design models. Ontologies are
formalised as theories with sets, axioms, theorems and reasoning rules. They
are integrated to design models through an annotation mechanism. Event-B has
been chosen as the ground formal modelling technique for all our developments.
In this paper, we particularly describe how ontologies are formalised as
Event-B theories.
A Multilayer Framework for Online Metric Learning
Authors: Wenbin Li, Yanfang Liu, Jing Huo, Yinghuan Shi, Yang Gao, Lei Wang, Jiebo Luo
Date: 2018-05-15 00:00:00
Tasks: Metric Learning, Triplet

Online metric learning has been widely applied in classification and retrieval. It can automatically learn a suitable metric from data by restricting similar instances to be separated from dissimilar instances with a given margin. However, the existing online metric learning algorithms have limited performance in real-world classifications, especially when data distributions are complex. To this end, this paper proposes a multilayer framework for online metric learning to capture the nonlinear similarities among instances. Different from the traditional online metric learning, which can only learn one metric space, the proposed Multi-Layer Online Metric Learning (MLOML) takes an online metric learning algorithm as a metric layer and learns multiple hierarchical metric spaces, where each metric layer follows a nonlinear layers for the complicated data distribution. Moreover, the forward propagation (FP) strategy and backward propagation (BP) strategy are employed to train the hierarchical metric layers. To build a metric layer of the proposed MLOML, a new Mahalanobis-based Online Metric Learning (MOML) algorithm is presented based on the passive-aggressive strategy and one-pass triplet construction strategy. Furthermore, in a progressively and nonlinearly learning way, MLOML has a stronger learning ability than traditional online metric learning in the case of limited available training data. To make the learning process more explainable and theoretically guaranteed, theoretical analysis is provided. The proposed MLOML enjoys several nice properties, indeed learns a metric progressively, and performs better on the benchmark datasets. Extensive experiments with different settings have been conducted to verify these properties of the proposed MLOML.
Semi-supervised User Geolocation via Graph Convolutional Networks
Authors: Afshin Rahimi, Trevor Cohn, Timothy Baldwin
Date: 2018-04-22 00:00:00
Proceeding: ACL 2018 7
Tasks: Event Detection

Social media user geolocation is vital to many applications such as event
detection. In this paper, we propose GCN, a multiview geolocation model based
on Graph Convolutional Networks, that uses both text and network context. We
compare GCN to the state-of-the-art, and to two baselines we propose, and show
that our model achieves or is competitive with the state- of-the-art over three
benchmark geolocation datasets when sufficient supervision is available. We
also evaluate GCN under a minimal supervision scenario, and show it outperforms
baselines. We find that highway network gates are essential for controlling the
amount of useful neighbourhood expansion in GCN.
Learning to Deblur Images with Exemplars
Authors: Jinshan Pan, Wenqi Ren, Zhe Hu, Ming-Hsuan Yang
Date: 2018-05-15 00:00:00
Tasks: Deblurring, Image Deblurring

Human faces are one interesting object class with numerous applications.
While significant progress has been made in the generic deblurring problem,
existing methods are less effective for blurry face images. The success of the
state-of-the-art image deblurring algorithms stems mainly from implicit or
explicit restoration of salient edges for kernel estimation. However, existing
methods are less effective as only few edges can be restored from blurry face
images for kernel estimation. In this paper, we address the problem of
deblurring face images by exploiting facial structures. We propose a deblurring
algorithm based on an exemplar dataset without using coarse-to-fine strategies
or heuristic edge selections. In addition, we develop a convolutional neural
network to restore sharp edges from blurry images for deblurring. Extensive
experiments against the state-of-the-art methods demonstrate the effectiveness
of the proposed algorithms for deblurring face images. In addition, we show the
proposed algorithms can be applied to image deblurring for other object
classes.
Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets
Authors: Jia Chen, Gang Wang, Georgios B. Giannakis
Date: 2018-05-15 00:00:00
Tasks: Dimensionality Reduction

Principal component analysis (PCA) is widely used for feature extraction and
dimensionality reduction, with documented merits in diverse tasks involving
high-dimensional data. Standard PCA copes with one dataset at a time, but it is
challenged when it comes to analyzing multiple datasets jointly. In certain
data science settings however, one is often interested in extracting the most
discriminative information from one dataset of particular interest (a.k.a.
target data) relative to the other(s) (a.k.a. background data). To this end,
this paper puts forth a novel approach, termed discriminative (d) PCA, for such
discriminative analytics of multiple datasets. Under certain conditions, dPCA
is proved to be least-squares optimal in recovering the component vector unique
to the target data relative to background data. To account for nonlinear data
correlations, (linear) dPCA models for one or multiple background datasets are
generalized through kernel-based learning. Interestingly, all dPCA variants
admit an analytical solution obtainable with a single (generalized) eigenvalue
decomposition. Finally, corroborating dimensionality reduction tests using both
synthetic and real datasets are provided to validate the effectiveness of the
proposed methods.