pwc-archive/papers-with-abstracts huggingface dataset:
Gradient Harmonized Single-stage Detector
Authors: Buyu Li, Yu Liu, Xiaogang Wang
Date: 2018-11-13 00:00:00
Tasks: General Classification, Object Detection, Philosophy

Despite the great success of two-stage detectors, single-stage detector is
still a more elegant and efficient way, yet suffers from the two well-known
disharmonies during training, i.e. the huge difference in quantity between
positive and negative examples as well as between easy and hard examples. In
this work, we first point out that the essential effect of the two disharmonies
can be summarized in term of the gradient. Further, we propose a novel gradient
harmonizing mechanism (GHM) to be a hedging for the disharmonies. The
philosophy behind GHM can be easily embedded into both classification loss
function like cross-entropy (CE) and regression loss function like smooth-$L_1$
($SL_1$) loss. To this end, two novel loss functions called GHM-C and GHM-R are
designed to balancing the gradient flow for anchor classification and bounding
box refinement, respectively. Ablation study on MS COCO demonstrates that
without laborious hyper-parameter tuning, both GHM-C and GHM-R can bring
substantial improvement for single-stage detector. Without any whistles and
bells, our model achieves 41.6 mAP on COCO test-dev set which surpasses the
state-of-the-art method, Focal Loss (FL) + $SL_1$, by 0.8.
Child Gender Determination with Convolutional Neural Networks on Hand Radio-Graphs
Authors: Mumtaz A. Kaloi, Kun He
Date: 2018-11-13 00:00:00

Motivation: In forensic or medico-legal investigation as well as in
anthropology the gender determination of the subject (hit by a disastrous or
any kind of traumatic situation) is mostly the first step. In state-of-the-art
techniques the gender is determined by examining dimensions of the bones of
skull and the pelvis area. In worse situations when there is only a small
portion of the human remains to be investigated and the subject is a child, we
need alternative techniques to determine the gender of the subject. In this
work we propose a technique called GDCNN (Gender Determination with
Convolutional Neural Networks), where the left hand radio-graphs of the
children between a wide range of ages in 1 month to 18 years are examined to
determine the gender. To our knowledge this technique is first of its kind.
Further to identify the area of the attention we used Class Activation Mapping
(CAM). Results: The results suggest the accuracy of the model is as high as
98%, which is very convincing by taking into account the incompletely grown
skeleton of the children. The attention observed with CAM discovers that the
lower part of the hand around carpals (wrist) is more important for child
gender determination.
Vehicle Re-identification Using Quadruple Directional Deep Learning Features
Authors: Jianqing Zhu, Huanqiang Zeng, Jingchang Huang, Shengcai Liao, Zhen Lei, Canhui Cai, Lixin Zheng
Date: 2018-11-13 00:00:00
Tasks: Deep Learning, Vehicle Re-Identification

In order to resist the adverse effect of viewpoint variations for improving
vehicle re-identification performance, we design quadruple directional deep
learning networks to extract quadruple directional deep learning features
(QD-DLF) of vehicle images. The quadruple directional deep learning networks
are with similar overall architecture, including the same basic deep learning
architecture but different directional feature pooling layers. Specifically,
the same basic deep learning architecture is a shortly and densely connected
convolutional neural network to extract basic feature maps of an input square
vehicle image in the first stage. Then, the quadruple directional deep learning
networks utilize different directional pooling layers, i.e., horizontal average
pooling (HAP) layer, vertical average pooling (VAP) layer, diagonal average
pooling (DAP) layer and anti-diagonal average pooling (AAP) layer, to compress
the basic feature maps into horizontal, vertical, diagonal and anti-diagonal
directional feature maps, respectively.
  Finally, these directional feature maps are spatially normalized and
concatenated together as a quadruple directional deep learning feature for
vehicle re-identification. Extensive experiments on both VeRi and VehicleID
databases show that the proposed QD-DLF approach outperforms multiple
state-of-the-art vehicle re-identification methods.
Recurrent Multi-Graph Neural Networks for Travel Cost Prediction
Authors: Jilin Hu, Chenjuan Guo, Bin Yang, Christian S. Jensen, Lu Chen
Date: 2018-11-13 00:00:00
Tasks: Prediction

Origin-destination (OD) matrices are often used in urban planning, where a
city is partitioned into regions and an element (i, j) in an OD matrix records
the cost (e.g., travel time, fuel consumption, or travel speed) from region i
to region j. In this paper, we partition a day into multiple intervals, e.g.,
96 15-min intervals and each interval is associated with an OD matrix which
represents the costs in the interval; and we consider sparse and stochastic OD
matrices, where the elements represent stochastic but not deterministic costs
and some elements are missing due to lack of data between two regions. We solve
the sparse, stochastic OD matrix forecasting problem. Given a sequence of
historical OD matrices that are sparse, we aim at predicting future OD matrices
with no empty elements. We propose a generic learning framework to solve the
problem by dealing with sparse matrices via matrix factorization and two graph
convolutional neural networks and capturing temporal dynamics via recurrent
neural network. Empirical studies using two taxi datasets from different
countries verify the effectiveness of the proposed framework.
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits
Authors: Branislav Kveton, Csaba Szepesvari, Sharan Vaswani, Zheng Wen, Mohammad Ghavamzadeh, Tor Lattimore
Date: 2018-11-13 00:00:00
Tasks: Multi-Armed Bandits

We propose a bandit algorithm that explores by randomizing its history of rewards. Specifically, it pulls the arm with the highest mean reward in a non-parametric bootstrap sample of its history with pseudo rewards. We design the pseudo rewards such that the bootstrap mean is optimistic with a sufficiently high probability. We call our algorithm Giro, which stands for garbage in, reward out. We analyze Giro in a Bernoulli bandit and derive a $O(K \Delta^{-1} \log n)$ bound on its $n$-round regret, where $\Delta$ is the difference in the expected rewards of the optimal and the best suboptimal arms, and $K$ is the number of arms. The main advantage of our exploration design is that it easily generalizes to structured problems. To show this, we propose contextual Giro with an arbitrary reward generalization model. We evaluate Giro and its contextual variant on multiple synthetic and real-world problems, and observe that it performs well.
Application of Faster R-CNN model on Human Running Pattern Recognition
Authors: Kairan Yang, Feng Geng
Date: 2018-11-13 00:00:00
Tasks: Object Recognition

The advance algorithms like Faster Regional Convolutional Neural Network
(Faster R-CNN) models are suitable to identify classified moving objects, due
to the efficiency in learning the training dataset superior than ordinary CNN
algorithms and the higher accuracy of labeling correct classes in the
validation and testing dataset. This research examined and compared the three
R-CNN type algorithms in object recognition to show the superior efficiency and
accuracy of Faster R-CNN model on classifying human running patterns. Then it
described the effect of Faster R-CNN in detecting different types of running
patterns exhibited by a single individual or multiple individuals by conducting
a dataset fitting experiment. In this study, the Faster R-CNN algorithm is
implemented directly from the version released by Ross Girshick.
Hate Speech Detection from Code-mixed Hindi-English Tweets Using Deep Learning Models
Authors: Satyajit Kamble, Aditya Joshi
Date: 2018-11-13 00:00:00
Tasks: Hate Speech Detection

This paper reports an increment to the state-of-the-art in hate speech
detection for English-Hindi code-mixed tweets. We compare three typical deep
learning models using domain-specific embeddings. On experimenting with a
benchmark dataset of English-Hindi code-mixed tweets, we observe that using
domain-specific embeddings results in an improved representation of target
groups, and an improved F-score.
Co-Representation Learning For Classification and Novel Class Detection via Deep Networks
Authors: Zhuoyi Wang, Zelun Kong, Hemeng Tao, Swarup Chandra, Latifur Khan
Date: 2018-11-13 00:00:00
Tasks: General Classification, Multi-Task Learning, Representation Learning

One of the key challenges of performing label prediction over a data stream
concerns with the emergence of instances belonging to unobserved class labels
over time. Previously, this problem has been addressed by detecting such
instances and using them for appropriate classifier adaptation. The fundamental
aspect of a novel-class detection strategy relies on the ability of comparison
among observed instances to discriminate them into known and unknown classes.
Therefore, studies in the past have proposed various metrics suitable for
comparison over the observed feature space. Unfortunately, these similarity
measures fail to reliably identify distinct regions in observed feature spaces
useful for class discrimination and novel-class detection, especially in
streams containing high-dimensional data instances such as images and texts. In
this paper, we address this key challenge by proposing a semi-supervised
multi-task learning framework called \sysname{} which aims to intrinsically
search for a latent space suitable for detecting labels of instances from both
known and unknown classes. We empirically measure the performance of \sysname{}
over multiple real-world image and text datasets and demonstrate its
superiority by comparing its performance with existing semi-supervised methods.
Community Exploration: From Offline Optimization to Online Learning
Authors: Xiaowei Chen, Weiran Huang, Wei Chen, John C. S. Lui
Date: 2018-11-13 00:00:00
Proceeding: NeurIPS 2018 12

We introduce the community exploration problem that has many real-world
applications such as online advertising. In the problem, an explorer allocates
limited budget to explore communities so as to maximize the number of members
he could meet. We provide a systematic study of the community exploration
problem, from offline optimization to online learning. For the offline setting
where the sizes of communities are known, we prove that the greedy methods for
both of non-adaptive exploration and adaptive exploration are optimal. For the
online setting where the sizes of communities are not known and need to be
learned from the multi-round explorations, we propose an `upper confidence'
like algorithm that achieves the logarithmic regret bounds. By combining the
feedback from different rounds, we can achieve a constant regret bound.
Modeling Local Dependence in Natural Language with Multi-channel Recurrent Neural Networks
Authors: Chang Xu, Weiran Huang, Hongwei Wang, Gang Wang, Tie-Yan Liu
Date: 2018-11-13 00:00:00
Tasks: Abstractive Text Summarization, Language Modeling, Language Modelling, Machine Translation, Sentence

Recurrent Neural Networks (RNNs) have been widely used in processing natural
language tasks and achieve huge success. Traditional RNNs usually treat each
token in a sentence uniformly and equally. However, this may miss the rich
semantic structure information of a sentence, which is useful for understanding
natural languages. Since semantic structures such as word dependence patterns
are not parameterized, it is a challenge to capture and leverage structure
information. In this paper, we propose an improved variant of RNN,
Multi-Channel RNN (MC-RNN), to dynamically capture and leverage local semantic
structure information. Concretely, MC-RNN contains multiple channels, each of
which represents a local dependence pattern at a time. An attention mechanism
is introduced to combine these patterns at each step, according to the semantic
information. Then we parameterize structure information by adaptively selecting
the most appropriate connection structures among channels. In this way, diverse
local structures and dependence patterns in sentences can be well captured by
MC-RNN. To verify the effectiveness of MC-RNN, we conduct extensive experiments
on typical natural language processing tasks, including neural machine
translation, abstractive summarization, and language modeling. Experimental
results on these tasks all show significant improvements of MC-RNN over current
top systems.
Exploiting temporal and depth information for multi-frame face anti-spoofing
Authors: Zezheng Wang, Chenxu Zhao, Yunxiao Qin, Qiusheng Zhou, Guo-Jun Qi, Jun Wan, Zhen Lei
Date: 2018-11-13 00:00:00
Tasks: Face Anti-Spoofing, Face Recognition, Optical Flow Estimation

Face anti-spoofing is significant to the security of face recognition
systems. Previous works on depth supervised learning have proved the
effectiveness for face anti-spoofing. Nevertheless, they only considered the
depth as an auxiliary supervision in the single frame. Different from these
methods, we develop a new method to estimate depth information from multiple
RGB frames and propose a depth-supervised architecture which can efficiently
encodes spatiotemporal information for presentation attack detection. It
includes two novel modules: optical flow guided feature block (OFFB) and
convolution gated recurrent units (ConvGRU) module, which are designed to
extract short-term and long-term motion to discriminate living and spoofing
faces. Extensive experiments demonstrate that the proposed approach achieves
state-of-the-art results on four benchmark datasets, namely OULU-NPU, SiW,
CASIA-MFSD, and Replay-Attack.
Interpreting Models by Allowing to Ask
Authors: Sungmin Kang, David Keetae Park, Jaehyuk Chang, Jaegul Choo
Date: 2018-11-13 00:00:00
Tasks: Colorization

Questions convey information about the questioner, namely what one does not
know. In this paper, we propose a novel approach to allow a learning agent to
ask what it considers as tricky to predict, in the course of producing a final
output. By analyzing when and what it asks, we can make our model more
transparent and interpretable. We first develop this idea to propose a general
framework of deep neural networks that can ask questions, which we call asking
networks. A specific architecture and training process for an asking network is
proposed for the task of colorization, which is an exemplar one-to-many task
and thus a task where asking questions is helpful in performing the task
accurately. Our results show that the model learns to generate meaningful
questions, asks difficult questions first, and utilizes the provided hint more
efficiently than baseline models. We conclude that the proposed asking
framework makes the learning agent reveal its weaknesses, which poses a
promising new direction in developing interpretable and interactive models.
Neuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks
Authors: Arjun Punjabi, Adam Martersteck, Yanran Wang, Todd B. Parrish, Aggelos K. Katsaggelos, the Alzheimer's Disease Neuroimaging Initiative
Date: 2018-11-13 00:00:00
Tasks: Classification, General Classification

Automated methods for Alzheimer's disease (AD) classification have the
potential for great clinical benefits and may provide insight for combating the
disease. Machine learning, and more specifically deep neural networks, have
been shown to have great efficacy in this domain. These algorithms often use
neurological imaging data such as MRI and PET, but a comprehensive and balanced
comparison of these modalities has not been performed. In order to accurately
determine the relative strength of each imaging variant, this work performs a
comparison study in the context of Alzheimer's dementia classification using
the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Furthermore,
this work analyzes the benefits of using both modalities in a fusion setting
and discusses how these data types may be leveraged in future AD studies using
deep learning.
Exploring RNN-Transducer for Chinese Speech Recognition
Authors: Senmao Wang, Pan Zhou, Wei Chen, Jia Jia, Lei Xie
Date: 2018-11-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), GPU, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

End-to-end approaches have drawn much attention recently for significantly
simplifying the construction of an automatic speech recognition (ASR) system.
RNN transducer (RNN-T) is one of the popular end-to-end methods. Previous
studies have shown that RNN-T is difficult to train and a very complex training
process is needed for a reasonable performance. In this paper, we explore RNN-T
for a Chinese large vocabulary continuous speech recognition (LVCSR) task and
aim to simplify the training process while maintaining performance. First, a
new strategy of learning rate decay is proposed to accelerate the model
convergence. Second, we find that adding convolutional layers at the beginning
of the network and using ordered data can discard the pre-training process of
the encoder without loss of performance. Besides, we design experiments to find
a balance among the usage of GPU memory, training circle and model performance.
Finally, we achieve 16.9% character error rate (CER) on our test set which is
2% absolute improvement from a strong BLSTM CE system with language model
trained on the same text corpus.
A Local Regret in Nonconvex Online Learning
Authors: Sergul Aydore, Lee Dicker, Dean Foster
Date: 2018-11-13 00:00:00

We consider an online learning process to forecast a sequence of outcomes for
nonconvex models. A typical measure to evaluate online learning algorithms is
regret but such standard definition of regret is intractable for nonconvex
models even in offline settings. Hence, gradient based definition of regrets
are common for both offline and online nonconvex problems. Recently, a notion
of local gradient based regret was introduced. Inspired by the concept of
calibration and a local gradient based regret, we introduce another definition
of regret and we discuss why our definition is more interpretable for
forecasting problems. We also provide bound analysis for our regret under
certain assumptions.
A General Method for Amortizing Variational Filtering
Authors: Joseph Marino, Milan Cvitkovic, Yisong Yue
Date: 2018-11-13 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Inference Optimization, Variational Inference

We introduce the variational filtering EM algorithm, a simple,
general-purpose method for performing variational inference in dynamical latent
variable models using information from only past and present variables, i.e.
filtering. The algorithm is derived from the variational objective in the
filtering setting and consists of an optimization procedure at each time step.
By performing each inference optimization procedure with an iterative amortized
inference model, we obtain a computationally efficient implementation of the
algorithm, which we call amortized variational filtering. We present
experiments demonstrating that this general-purpose method improves performance
across several deep dynamical latent variable models.
Domain Agnostic Real-Valued Specificity Prediction
Authors: Wei-Jen Ko, Greg Durrett, Junyi Jessy Li
Date: 2018-11-13 00:00:00
Tasks: Dialogue Generation, Domain Adaptation, Informativeness, Prediction, Sentence, Specificity, Unsupervised Domain Adaptation

Sentence specificity quantifies the level of detail in a sentence,
characterizing the organization of information in discourse. While this
information is useful for many downstream applications, specificity prediction
systems predict very coarse labels (binary or ternary) and are trained on and
tailored toward specific domains (e.g., news). The goal of this work is to
generalize specificity prediction to domains where no labeled data is available
and output more nuanced real-valued specificity ratings.
  We present an unsupervised domain adaptation system for sentence specificity
prediction, specifically designed to output real-valued estimates from binary
training labels. To calibrate the values of these predictions appropriately, we
regularize the posterior distribution of the labels towards a reference
distribution. We show that our framework generalizes well to three different
domains with 50%~68% mean absolute error reduction than the current
state-of-the-art system trained for news sentence specificity. We also
demonstrate the potential of our work in improving the quality and
informativeness of dialogue generation systems.
A Unified Model for Opinion Target Extraction and Target Sentiment Prediction
Authors: Xin Li, Lidong Bing, Piji Li, Wai Lam
Date: 2018-11-13 00:00:00
Tasks: Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Sentiment Classification

Target-based sentiment analysis involves opinion target extraction and target
sentiment classification. However, most of the existing works usually studied
one of these two sub-tasks alone, which hinders their practical use. This paper
aims to solve the complete task of target-based sentiment analysis in an
end-to-end fashion, and presents a novel unified model which applies a unified
tagging scheme. Our framework involves two stacked recurrent neural networks:
The upper one predicts the unified tags to produce the final output results of
the primary target-based sentiment analysis; The lower one performs an
auxiliary target boundary prediction aiming at guiding the upper network to
improve the performance of the primary task. To explore the inter-task
dependency, we propose to explicitly model the constrained transitions from
target boundaries to target sentiment polarities. We also propose to maintain
the sentiment consistency within an opinion target via a gate mechanism which
models the relation between the features for the current word and the previous
word. We conduct extensive experiments on three benchmark datasets and our
framework achieves consistently superior results.
Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality
Authors: Miaoyan Wang, Lexin Li
Date: 2018-11-13 00:00:00
Tasks: Clustering, Tensor Decomposition

We consider the problem of decomposing a higher-order tensor with binary entries. Such data problems arise frequently in applications such as neuroimaging, recommendation system, topic modeling, and sensor network localization. We propose a multilinear Bernoulli model, develop a rank-constrained likelihood-based estimation method, and obtain the theoretical accuracy guarantees. In contrast to continuous-valued problems, the binary tensor problem exhibits an interesting phase transition phenomenon according to the signal-to-noise ratio. The error bound for the parameter tensor estimation is established, and we show that the obtained rate is minimax optimal under the considered model. Furthermore, we develop an alternating optimization algorithm with convergence guarantees. The efficacy of our approach is demonstrated through both simulations and analyses of multiple data sets on the tasks of tensor completion and clustering.