pwc-archive/papers-with-abstracts huggingface dataset:
Siamese Networks for Semantic Pattern Similarity
Authors: Yassine Benajiba, Jin Sun, Yong Zhang, Longquan Jiang, Zhiliang Weng, Or Biran
Date: 2018-12-17 00:00:00
Tasks: Question Answering

Semantic Pattern Similarity is an interesting, though not often encountered
NLP task where two sentences are compared not by their specific meaning, but by
their more abstract semantic pattern (e.g., preposition or frame). We utilize
Siamese Networks to model this task, and show its usefulness in determining SQL
patterns for unseen questions in a database-backed question answering scenario.
Our approach achieves high accuracy and contains a built-in proxy for
confidence, which can be used to keep precision arbitrarily high.
Double Deep Q-Learning for Optimal Execution
Authors: Brian Ning, Franco Ho Ting Lin, Sebastian Jaimungal
Date: 2018-12-17 00:00:00
Tasks: Q-Learning

Optimal trade execution is an important problem faced by essentially all traders. Much research into optimal execution uses stringent model assumptions and applies continuous time stochastic control to solve them. Here, we instead take a model free approach and develop a variation of Deep Q-Learning to estimate the optimal actions of a trader. The model is a fully connected Neural Network trained using Experience Replay and Double DQN with input features given by the current state of the limit order book, other trading signals, and available execution actions, while the output is the Q-value function estimating the future rewards under an arbitrary action. We apply our model to nine different stocks and find that it outperforms the standard benchmark approach on most stocks using the measures of (i) mean and median out-performance, (ii) probability of out-performance, and (iii) gain-loss ratios.
Learning Student Networks via Feature Embedding
Authors: Hanting Chen, Yunhe Wang, Chang Xu, Chao Xu, DaCheng Tao
Date: 2018-12-17 00:00:00
Tasks: Knowledge Distillation

Deep convolutional neural networks have been widely used in numerous
applications, but their demanding storage and computational resource
requirements prevent their applications on mobile devices. Knowledge
distillation aims to optimize a portable student network by taking the
knowledge from a well-trained heavy teacher network. Traditional
teacher-student based methods used to rely on additional fully-connected layers
to bridge intermediate layers of teacher and student networks, which brings in
a large number of auxiliary parameters. In contrast, this paper aims to
propagate information from teacher to student without introducing new variables
which need to be optimized. We regard the teacher-student paradigm from a new
perspective of feature embedding. By introducing the locality preserving loss,
the student network is encouraged to generate the low-dimensional features
which could inherit intrinsic properties of their corresponding
high-dimensional features from teacher network. The resulting portable network
thus can naturally maintain the performance as that of the teacher network.
Theoretical analysis is provided to justify the lower computation complexity of
the proposed method. Experiments on benchmark datasets and well-trained
networks suggest that the proposed algorithm is superior to state-of-the-art
teacher-student learning methods in terms of computational and storage
complexity.
Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning
Authors: Hao Zhu, Huaibo Huang, Yi Li, Aihua Zheng, Ran He
Date: 2018-12-17 00:00:00
Tasks: Face Generation, Talking Face Generation

Talking face generation aims to synthesize a face video with precise lip synchronization as well as a smooth transition of facial motion over the entire video via the given speech clip and facial image. Most existing methods mainly focus on either disentangling the information in a single image or learning temporal information between frames. However, cross-modality coherence between audio and video information has not been well addressed during synthesis. In this paper, we propose a novel arbitrary talking face generation framework by discovering the audio-visual coherence via the proposed Asymmetric Mutual Information Estimator (AMIE). In addition, we propose a Dynamic Attention (DA) block by selectively focusing the lip area of the input image during the training stage, to further enhance lip synchronization. Experimental results on benchmark LRW dataset and GRID dataset transcend the state-of-the-art methods on prevalent metrics with robust high-resolution synthesizing on gender and pose variations.
Grounded Video Description
Authors: Luowei Zhou, Yannis Kalantidis, Xinlei Chen, Jason J. Corso, Marcus Rohrbach
Date: 2018-12-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Description, Sentence, Video Description

Video description is one of the most challenging problems in vision and language understanding due to the large variability both on the video and language side. Models, hence, typically shortcut the difficulty in recognition and generate plausible sentences that are based on priors but are not necessarily grounded in the video. In this work, we explicitly link the sentence to the evidence in the video by annotating each noun phrase in a sentence with the corresponding bounding box in one of the frames of a video. Our dataset, ActivityNet-Entities, augments the challenging ActivityNet Captions dataset with 158k bounding box annotations, each grounding a noun phrase. This allows training video description models with this data, and importantly, evaluate how grounded or "true" such model are to the video they describe. To generate grounded captions, we propose a novel video description model which is able to exploit these bounding box annotations. We demonstrate the effectiveness of our model on our dataset, but also show how it can be applied to image description on the Flickr30k Entities dataset. We achieve state-of-the-art performance on video description, video paragraph description, and image description and demonstrate our generated sentences are better grounded in the video.
Generalizable Meta-Heuristic based on Temporal Estimation of Rewards for Large Scale Blackbox Optimization
Authors: Mingde Zhao, Hongwei Ge, Yi Lian, Kai Zhang
Date: 2018-12-17 00:00:00
Tasks: Multi-Armed Bandits

The generalization abilities of heuristic optimizers may deteriorate with the increment of the search space dimensionality. To achieve generalized performance across Large Scale Blackbox Optimization (LSBO) tasks, it ispossible to ensemble several heuristics and devise a meta-heuristic to control their initiation. This paper first proposes a methodology of transforming LSBO problems into online decision processes to maximize efficiency of resource utilization. Then, using the perspective of multi-armed bandits with non-stationary reward distributions, we propose a meta-heuristic based on Temporal Estimation of Rewards (TER) to address such decision process. TER uses a window for temporal credit assignment and Boltzmann exploration to balance the exploration-exploitation tradeoff. The prior-free TER generalizes across LSBO tasks with flexibility for different types of limited computational resources (e.g. time, money, etc.) and is easy to be adapted to new tasks for its simplicity and easy interface for heuristic articulation. Tests on the benchmarks validate the problem formulation and suggest significant effectiveness: when TER is articulated with three heuristics, competitive performance is reported across different sets of benchmark problems with search dimensions up to 10000.
$\ell_0$-Motivated Low-Rank Sparse Subspace Clustering
Authors: Maria Brbić, Ivica Kopriva
Date: 2018-12-17 00:00:00
Tasks: Clustering

In many applications, high-dimensional data points can be well represented by
low-dimensional subspaces. To identify the subspaces, it is important to
capture a global and local structure of the data which is achieved by imposing
low-rank and sparseness constraints on the data representation matrix. In
low-rank sparse subspace clustering (LRSSC), nuclear and $\ell_1$ norms are
used to measure rank and sparsity. However, the use of nuclear and $\ell_1$
norms leads to an overpenalized problem and only approximates the original
problem. In this paper, we propose two $\ell_0$ quasi-norm based
regularizations. First, the paper presents regularization based on multivariate
generalization of minimax-concave penalty (GMC-LRSSC), which contains the
global minimizers of $\ell_0$ quasi-norm regularized objective. Afterward, we
introduce the Schatten-0 ($S_0$) and $\ell_0$ regularized objective and
approximate the proximal map of the joint solution using a proximal average
method ($S_0/\ell_0$-LRSSC). The resulting nonconvex optimization problems are
solved using alternating direction method of multipliers with established
convergence conditions of both algorithms. Results obtained on synthetic and
four real-world datasets show the effectiveness of GMC-LRSSC and
$S_0/\ell_0$-LRSSC when compared to state-of-the-art methods.
Learning Incremental Triplet Margin for Person Re-identification
Authors: Yingying Zhang, Qiaoyong Zhong, Liang Ma, Di Xie, ShiLiang Pu
Date: 2018-12-17 00:00:00
Tasks: Metric Learning, Person Re-Identification, Triplet

Person re-identification (ReID) aims to match people across multiple
non-overlapping video cameras deployed at different locations. To address this
challenging problem, many metric learning approaches have been proposed, among
which triplet loss is one of the state-of-the-arts. In this work, we explore
the margin between positive and negative pairs of triplets and prove that large
margin is beneficial. In particular, we propose a novel multi-stage training
strategy which learns incremental triplet margin and improves triplet loss
effectively. Multiple levels of feature maps are exploited to make the learned
features more discriminative. Besides, we introduce global hard identity
searching method to sample hard identities when generating a training batch.
Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our
approach yields a performance boost and outperforms most existing
state-of-the-art methods.
A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule
Authors: Yunzhe Hao, Xuhui Huang, Meng Dong, Bo Xu
Date: 2018-12-17 00:00:00

Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally categorized into two basic classes, i.e., backpropagation-like training methods and plasticity-based learning methods. The former methods are dependent on energy-inefficient real-valued computation and non-local transmission, as also required in artificial neural networks (ANNs), whereas the latter are either considered to be biologically implausible or exhibit poor performance. Hence, biologically plausible (bio-plausible) high-performance supervised learning (SL) methods for SNNs remain deficient. In this paper, we proposed a novel bio-plausible SNN model for SL based on the symmetric spike-timing dependent plasticity (sym-STDP) rule found in neuroscience. By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST dataset). To reveal the underlying mechanism of our SL model, we visualized both layer-based activities and synaptic weights using the t-distributed stochastic neighbor embedding (t-SNE) method after training and found that they were well clustered, thereby demonstrating excellent classification ability. Furthermore, to verify the robustness of our model, we trained it on another more realistic dataset (Fashion-MNIST), which also showed good performance. As the learning rules were bio-plausible and based purely on local spike events, our model could be easily applied to neuromorphic hardware for online training and may be helpful for understanding SL information processing at the synaptic level in biological neural systems.
Latent Dirichlet Allocation in Generative Adversarial Networks
Authors: Lili Pan, Shen Cheng, Jian Liu, Yazhou Ren, Zenglin Xu
Date: 2018-12-17 00:00:00
Tasks: Image Generation, multimodal generation, Stochastic Optimization

We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs). Despite the success of existing methods, they often ignore the underlying structure of vision data or its multimodal generation characteristics. To address this problem, we introduce the Dirichlet prior for multimodal image generation, which leads to a new Latent Dirichlet Allocation based GAN (LDAGAN). In detail, for the generative process modelling, LDAGAN defines a generative mode for each sample, determining which generative sub-process it belongs to. For the adversarial training, LDAGAN derives a variational expectation-maximization (VEM) algorithm to estimate model parameters. Experimental results on real-world datasets have demonstrated the outstanding performance of LDAGAN over other existing GANs.
Defense-VAE: A Fast and Accurate Defense against Adversarial Attacks
Authors: Xiang Li, Shihao Ji
Date: 2018-12-17 00:00:00

Deep neural networks (DNNs) have been enormously successful across a variety of prediction tasks. However, recent research shows that DNNs are particularly vulnerable to adversarial attacks, which poses a serious threat to their applications in security-sensitive systems. In this paper, we propose a simple yet effective defense algorithm Defense-VAE that uses variational autoencoder (VAE) to purge adversarial perturbations from contaminated images. The proposed method is generic and can defend white-box and black-box attacks without the need of retraining the original CNN classifiers, and can further strengthen the defense by retraining CNN or end-to-end finetuning the whole pipeline. In addition, the proposed method is very efficient compared to the optimization-based alternatives, such as Defense-GAN, since no iterative optimization is needed for online prediction. Extensive experiments on MNIST, Fashion-MNIST, CelebA and CIFAR-10 demonstrate the superior defense accuracy of Defense-VAE compared to Defense-GAN, while being 50x faster than the latter. This makes Defense-VAE widely deployable in real-time security-sensitive systems. Our source code can be found at https://github.com/lxuniverse/defense-vae.
A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures
Authors: X. Yao, X. Li, Q. Ye, Y. Huang, Q. Cheng, G. -Q. Zhang
Date: 2018-12-17 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, Seizure Detection, Specificity

Identifying epileptic seizures through analysis of the electroencephalography (EEG) signal becomes a standard method for the diagnosis of epilepsy. Manual seizure identification on EEG by trained neurologists is time-consuming, labor-intensive and error-prone, and a reliable automatic seizure/non-seizure classification method is needed. One of the challenges in automatic seizure/non-seizure classification is that seizure morphologies exhibit considerable variabilities. In order to capture essential seizure patterns, this paper leverages an attention mechanism and a bidirectional long short-term memory (BiLSTM) to exploit both spatial and temporal discriminating features and overcome seizure variabilities. The attention mechanism is to capture spatial features according to the contributions of different brain regions to seizures. The BiLSTM is to extract discriminating temporal features in the forward and the backward directions. Cross-validation experiments and cross-patient experiments over the noisy data of CHB-MIT are performed to evaluate our proposed approach. The obtained average sensitivity of 87.00%, specificity of 88.60% and precision of 88.63% in cross-validation experiments are higher than using the current state-of-the-art methods, and the standard deviations of our approach are lower. The evaluation results of cross-patient experiments indicate that, our approach has better performance compared with the current state-of-the-art methods and is more robust across patients.
Towards Robust Human Activity Recognition from RGB Video Stream with Limited Labeled Data
Authors: Krishanu Sarker, Mohamed Masoud, Saeid Belkasim, Shihao Ji
Date: 2018-12-16 00:00:00
Tasks: Activity Recognition, Data Augmentation, Human Activity Recognition

Human activity recognition based on video streams has received numerous
attentions in recent years. Due to lack of depth information, RGB video based
activity recognition performs poorly compared to RGB-D video based solutions.
On the other hand, acquiring depth information, inertia etc. is costly and
requires special equipment, whereas RGB video streams are available in ordinary
cameras. Hence, our goal is to investigate whether similar or even higher
accuracy can be achieved with RGB-only modality. In this regard, we propose a
novel framework that couples skeleton data extracted from RGB video and deep
Bidirectional Long Short Term Memory (BLSTM) model for activity recognition. A
big challenge of training such a deep network is the limited training data, and
exploring RGB-only stream significantly exaggerates the difficulty. We
therefore propose a set of algorithmic techniques to train this model
effectively, e.g., data augmentation, dynamic frame dropout and gradient
injection. The experiments demonstrate that our RGB-only solution surpasses the
state-of-the-art approaches that all exploit RGB-D video streams by a notable
margin. This makes our solution widely deployable with ordinary cameras.
Deep Clustering Based on a Mixture of Autoencoders
Authors: Shlomo E. Chazan, Sharon Gannot, Jacob Goldberger
Date: 2018-12-16 00:00:00
Tasks: Clustering, Deep Clustering

In this paper we propose a Deep Autoencoder MIxture Clustering (DAMIC)
algorithm based on a mixture of deep autoencoders where each cluster is
represented by an autoencoder. A clustering network transforms the data into
another space and then selects one of the clusters. Next, the autoencoder
associated with this cluster is used to reconstruct the data-point. The
clustering algorithm jointly learns the nonlinear data representation and the
set of autoencoders. The optimal clustering is found by minimizing the
reconstruction loss of the mixture of autoencoder network. Unlike other deep
clustering algorithms, no regularization term is needed to avoid data
collapsing to a single point. Our experimental evaluations on image and text
corpora show significant improvement over state-of-the-art methods.
Higher-Order Spectral Clustering under Superimposed Stochastic Block Model
Authors: Subhadeep Paul, Olgica Milenkovic, Yuguo Chen
Date: 2018-12-16 00:00:00
Tasks: Clustering, Community Detection, Stochastic Block Model

Higher-order motif structures and multi-vertex interactions are becoming
increasingly important in studies that aim to improve our understanding of
functionalities and evolution patterns of networks. To elucidate the role of
higher-order structures in community detection problems over complex networks,
we introduce the notion of a Superimposed Stochastic Block Model (SupSBM). The
model is based on a random graph framework in which certain higher-order
structures or subgraphs are generated through an independent hyperedge
generation process, and are then replaced with graphs that are superimposed
with directed or undirected edges generated by an inhomogeneous random graph
model. Consequently, the model introduces controlled dependencies between edges
which allow for capturing more realistic network phenomena, namely strong local
clustering in a sparse network, short average path length, and community
structure. We proceed to rigorously analyze the performance of a number of
recently proposed higher-order spectral clustering methods on the SupSBM. In
particular, we prove non-asymptotic upper bounds on the misclustering error of
spectral community detection for a SupSBM setting in which triangles or
3-uniform hyperedges are superimposed with undirected edges. As part of our
analysis, we also derive new bounds on the misclustering error of higher-order
spectral clustering methods for the standard SBM and the 3-uniform hypergraph
SBM. Furthermore, for a non-uniform hypergraph SBM model in which one directly
observes both edges and 3-uniform hyperedges, we obtain a criterion that
describes when to perform spectral clustering based on edges and when on
hyperedges, based on a function of hyperedge density and observation quality.
The limit of artificial intelligence: Can machines be rational?
Authors: Tshilidzi Marwala
Date: 2018-12-16 00:00:00

This paper studies the question on whether machines can be rational. It
observes the existing reasons why humans are not rational which is due to
imperfect and limited information, limited and inconsistent processing power
through the brain and the inability to optimize decisions and achieve maximum
utility. It studies whether these limitations of humans are transferred to the
limitations of machines. The conclusion reached is that even though machines
are not rational advances in technological developments make these machines
more rational. It also concludes that machines can be more rational than
humans.
Non-invasive measuring method of skin temperature based on skin sensitivity index and deep learning
Authors: Xiaogang Cheng, Bin Yang, Kaige Tan, Erik Isaksson, Liren Li, Anders Hedman, Thomas Olofsson, Hai-Bo Li
Date: 2018-12-16 00:00:00
Tasks: Deep Learning, Sensitivity

In human-centered intelligent building, real-time measurements of human
thermal comfort play critical roles and supply feedback control signals for
building heating, ventilation, and air conditioning (HVAC) systems. Due to the
challenges of intra- and inter-individual differences and skin subtleness
variations, there is no satisfactory solution for thermal comfort measurements
until now. In this paper, a non-invasive measuring method based on skin
sensitivity index and deep learning (NISDL) was proposed to measure real-time
skin temperature. A new evaluating index, named skin sensitivity index (SSI),
was defined to overcome individual differences and skin subtleness variations.
To illustrate the effectiveness of SSI proposed, two multi-layers deep learning
framework (NISDL method I and II) was designed and the DenseNet201 was used for
extracting features from skin images. The partly personal saturation
temperature (NIPST) algorithm was use for algorithm comparisons. Another deep
learning algorithm without SSI (DL) was also generated for algorithm
comparisons. Finally, a total of 1.44 million image data was used for algorithm
validation. The results show that 55.6180% and 52.2472% error values (NISDL
method I, II) are scattered at [0, 0.25), and the same error intervals
distribution of NIPST is 35.3933%.