pwc-archive/papers-with-abstracts huggingface dataset:
Conditional BERT Contextual Augmentation
Authors: Xing Wu, Shangwen Lv, Liangjun Zang, Jizhong Han, Songlin Hu
Date: 2018-12-17 00:00:00
Tasks: Data Augmentation, Language Modeling, Language Modelling, Text Classification

We propose a novel data augmentation method for labeled sentences called
conditional BERT contextual augmentation. Data augmentation methods are often
applied to prevent overfitting and improve generalization of deep neural
network models. Recently proposed contextual augmentation augments labeled
sentences by randomly replacing words with more varied substitutions predicted
by language model. BERT demonstrates that a deep bidirectional language model
is more powerful than either an unidirectional language model or the shallow
concatenation of a forward and backward model. We retrofit BERT to conditional
BERT by introducing a new conditional masked language model\footnote{The term
"conditional masked language model" appeared once in original BERT paper, which
indicates context-conditional, is equivalent to term "masked language model".
In our paper, "conditional masked language model" indicates we apply extra
label-conditional constraint to the "masked language model".} task. The well
trained conditional BERT can be applied to enhance contextual augmentation.
Experiments on six various different text classification tasks show that our
method can be easily applied to both convolutional or recurrent neural networks
classifier to obtain obvious improvement.
Hateminers : Detecting Hate speech against Women
Authors: Punyajoy Saha, Binny Mathew, Pawan Goyal, Animesh Mukherjee
Date: 2018-12-17 00:00:00
Tasks: BIG-bench Machine Learning, Hate Speech Detection, Sentence, Sentence Embeddings

With the online proliferation of hate speech, there is an urgent need for
systems that can detect such harmful content. In this paper, We present the
machine learning models developed for the Automatic Misogyny Identification
(AMI) shared task at EVALITA 2018. We generate three types of features:
Sentence Embeddings, TF-IDF Vectors, and BOW Vectors to represent each tweet.
These features are then concatenated and fed into the machine learning models.
Our model came First for the English Subtask A and Fifth for the English
Subtask B. We release our winning model for public use and it's available at
https://github.com/punyajoy/Hateminers-EVALITA.
Adaptive Quantum State Tomography with Neural Networks
Authors: Yihui Quek, Stanislav Fort, Hui Khoon Ng
Date: 2018-12-17 00:00:00
Tasks: Quantum State Tomography

Quantum State Tomography is the task of determining an unknown quantum state
by making measurements on identical copies of the state. Current algorithms are
costly both on the experimental front -- requiring vast numbers of measurements
-- as well as in terms of the computational time to analyze those measurements.
In this paper, we address the problem of analysis speed and flexibility,
introducing \textit{Neural Adaptive Quantum State Tomography} (NA-QST), a
machine learning based algorithm for quantum state tomography that adapts
measurements and provides orders of magnitude faster processing while retaining
state-of-the-art reconstruction accuracy. Our algorithm is inspired by particle
swarm optimization and Bayesian particle-filter based adaptive methods, which
we extend and enhance using neural networks. The resampling step, in which a
bank of candidate solutions -- particles -- is refined, is in our case learned
directly from data, removing the computational bottleneck of standard methods.
We successfully replace the Bayesian calculation that requires computational
time of $O(\mathrm{poly}(n))$ with a learned heuristic whose time complexity
empirically scales as $O(\log(n))$ with the number of copies measured $n$,
while retaining the same reconstruction accuracy. This corresponds to a factor
of a million speedup for $10^7$ copies measured. We demonstrate that our
algorithm learns to work with basis, symmetric informationally complete (SIC),
as well as other types of POVMs. We discuss the value of measurement adaptivity
for each POVM type, demonstrating that its effect is significant only for basis
POVMs. Our algorithm can be retrained within hours on a single laptop for a
two-qubit situation, which suggests a feasible time-cost when extended to
larger systems. It can also adapt to a subset of possible states, a choice of
the type of measurement, and other experimental details.
Sepsis Prediction and Vital Signs Ranking in Intensive Care Unit Patients
Authors: Avijit Mitra, Khalid Ashraf
Date: 2018-12-17 00:00:00
Tasks: Prediction

We study multiple rule-based and machine learning (ML) models for sepsis
detection. We report the first neural network detection and prediction results
on three categories of sepsis. We have used the retrospective Medical
Information Mart for Intensive Care (MIMIC)-III dataset, restricted to
intensive care unit (ICU) patients. Features for prediction were created from
only common vital sign measurements. We show significant improvement of AUC
score using neural network based ensemble model compared to single ML and
rule-based models. For the detection of sepsis, severe sepsis, and septic
shock, our model achieves an AUC of 0.97, 0.96 and 0.91, respectively. Four
hours before the positive hours, it predicts the same three categories with an
AUC of 0.90, 0.91 and 0.90 respectively. Further, we ranked the features and
found that using six vital signs consistently provides higher detection and
prediction AUC for all the models tested. Our novel ensemble model achieves
highest AUC in detecting and predicting sepsis, severe sepsis, and septic shock
in the MIMIC-III ICU patients, and is amenable to deployment in hospital
settings.
Floorplan-Jigsaw: Jointly Estimating Scene Layout and Aligning Partial Scans
Authors: Cheng Lin, Changjian Li, Wenping Wang
Date: 2018-12-17 00:00:00
Proceeding: ICCV 2019 10

We present a novel approach to align partial 3D reconstructions which may not have substantial overlap. Using floorplan priors, our method jointly predicts a room layout and estimates the transformations from a set of partial 3D data. Unlike the existing methods relying on feature descriptors to establish correspondences, we exploit the 3D "box" structure of a typical room layout that meets the Manhattan World property. We first estimate a local layout for each partial scan separately and then combine these local layouts to form a globally aligned layout with loop closure. Without the requirement of feature matching, the proposed method enables some novel applications ranging from large or featureless scene reconstruction and modeling from sparse input. We validate our method quantitatively and qualitatively on real and synthetic scenes of various sizes and complexities. The evaluations and comparisons show superior effectiveness and accuracy of our method.
Robust Graph Learning from Noisy Data
Authors: Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu
Date: 2018-12-17 00:00:00
Tasks: Clustering, General Classification, graph construction, Graph Learning, Image/Document Clustering, Image Shadow Removal, Object Recognition, Shadow Removal, Video Background Subtraction

Learning graphs from data automatically has shown encouraging performance on
clustering and semisupervised learning tasks. However, real data are often
corrupted, which may cause the learned graph to be inexact or unreliable. In
this paper, we propose a novel robust graph learning scheme to learn reliable
graphs from real-world noisy data by adaptively removing noise and errors in
the raw data. We show that our proposed model can also be viewed as a robust
version of manifold regularized robust PCA, where the quality of the graph
plays a critical role. The proposed model is able to boost the performance of
data clustering, semisupervised classification, and data recovery
significantly, primarily due to two key factors: 1) enhanced low-rank recovery
by exploiting the graph smoothness assumption, 2) improved graph construction
by exploiting clean data recovered by robust PCA. Thus, it boosts the
clustering, semi-supervised classification, and data recovery performance
overall. Extensive experiments on image/document clustering, object
recognition, image shadow removal, and video background subtraction reveal that
our model outperforms the previous state-of-the-art methods.
Learning to Generate Music with BachProp
Authors: Florian Colombo, Johanni Brea, Wulfram Gerstner
Date: 2018-12-17 00:00:00

As deep learning advances, algorithms of music composition increase in performance. However, most of the successful models are designed for specific musical structures. Here, we present BachProp, an algorithmic composer that can generate music scores in many styles given sufficient training data. To adapt BachProp to a broad range of musical styles, we propose a novel representation of music and train a deep network to predict the note transition probabilities of a given music corpus. In this paper, new music scores generated by BachProp are compared with the original corpora as well as with different network architectures and other related models. We show that BachProp captures important features of the original datasets better than other models and invite the reader to a qualitative comparison on a large collection of generated songs.
Representation Learning for Spatial Graphs
Authors: Zheng Wang, Ce Ju, Gao Cong, Cheng Long
Date: 2018-12-17 00:00:00
Tasks: Clustering, Denoising, Graph Representation Learning, Representation Learning

Recently, the topic of graph representation learning has received plenty of
attention. Existing approaches usually focus on structural properties only and
thus they are not sufficient for those spatial graphs where the nodes are
associated with some spatial information. In this paper, we present the first
deep learning approach called s2vec for learning spatial graph representations,
which is based on denoising autoencoders framework (DAF). We evaluate the
learned representations on real datasets and the results verified the
effectiveness of s2vec when used for spatial clustering.
Attending Category Disentangled Global Context for Image Classification
Authors: Keke Tang, Guodong Wei, Runnan Chen, Jie Zhu, Zhaoquan Gu, Wenping Wang
Date: 2018-12-17 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

In this paper, we propose a general framework for image classification using the attention mechanism and global context, which could incorporate with various network architectures to improve their performance. To investigate the capability of the global context, we compare four mathematical models and observe the global context encoded in the category disentangled conditional generative model could give more guidance as "know what is task irrelevant will also know what is relevant". Based on this observation, we define a novel Category Disentangled Global Context (CDGC) and devise a deep network to obtain it. By attending CDGC, the baseline networks could identify the objects of interest more accurately, thus improving the performance. We apply the framework to many different network architectures and compare with the state-of-the-art on four publicly available datasets. Extensive results validate the effectiveness and superiority of our approach. Code will be made public upon paper acceptance.
Interpretable Matrix Completion: A Discrete Optimization Approach
Authors: Dimitris Bertsimas, Michael Lingzhi Li
Date: 2018-12-17 00:00:00
Tasks: Matrix Completion

We consider the problem of matrix completion on an $n \times m$ matrix. We introduce the problem of Interpretable Matrix Completion that aims to provide meaningful insights for the low-rank matrix using side information. We show that the problem can be reformulated as a binary convex optimization problem. We design OptComplete, based on a novel concept of stochastic cutting planes to enable efficient scaling of the algorithm up to matrices of sizes $n=10^6$ and $m=10^6$. We report experiments on both synthetic and real-world datasets that show that OptComplete has favorable scaling behavior and accuracy when compared with state-of-the-art methods for other types of matrix completion, while providing insight on the factors that affect the matrix.
AI-Aided Online Adaptive OFDM Receiver: Design and Experimental Results
Authors: Peiwen Jiang, Tianqi Wang, Bin Han, Xuanxuan Gao, Jing Zhang, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li
Date: 2018-12-17 00:00:00

Orthogonal frequency division multiplexing (OFDM) has been widely applied in current communication systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this study, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to real environments and promising for future communication systems. We discuss potential challenges and future research inspired by our initial study in this paper.
Stable safe screening and structured dictionaries for faster L1 regularization
Authors: Cassio Fraga Dantas, Rémi Gribonval
Date: 2018-12-17 00:00:00

In this paper, we propose a way to combine two acceleration techniques for the $\ell\_{1}$-regularized least squares problem: safe screening tests, which allow to eliminate useless dictionary atoms; and the use of fast structured approximations of the dictionary matrix. To do so, we introduce a new family of screening tests, termed stable screening, which can cope with approximation errors on the dictionary atoms while keeping the safety of the test (i.e. zero risk of rejecting atoms belonging to the solution support). Some of the main existing screening tests are extended to this new framework. The proposed algorithm consists in using a coarser (but faster) approximation of the dictionary at the initial iterations and then switching to better approximations until eventually adopting the original dictionary. A systematic switching criterion based on the duality gap saturation and the screening ratio is derived.Simulation results show significant reductions in both computational complexity and execution times for a wide range of tested scenarios.
Designing Adversarially Resilient Classifiers using Resilient Feature Engineering
Authors: Kevin Eykholt, Atul Prakash
Date: 2018-12-17 00:00:00
Tasks: Feature Engineering, General Classification

We provide a methodology, resilient feature engineering, for creating
adversarially resilient classifiers. According to existing work, adversarial
attacks identify weakly correlated or non-predictive features learned by the
classifier during training and design the adversarial noise to utilize these
features. Therefore, highly predictive features should be used first during
classification in order to determine the set of possible output labels. Our
methodology focuses the problem of designing resilient classifiers into a
problem of designing resilient feature extractors for these highly predictive
features. We provide two theorems, which support our methodology. The Serial
Composition Resilience and Parallel Composition Resilience theorems show that
the output of adversarially resilient feature extractors can be combined to
create an equally resilient classifier. Based on our theoretical results, we
outline the design of an adversarially resilient classifier.
Semi-supervised mp-MRI Data Synthesis with StitchLayer and Auxiliary Distance Maximization
Authors: Zhiwei Wang, Yi Lin, Kwang-Ting Cheng, Xin Yang
Date: 2018-12-17 00:00:00
Tasks: Synthesizing Multi-Parameter Magnetic Resonance Imaging (Mp-Mri) Data

In this paper, we address the problem of synthesizing multi-parameter
magnetic resonance imaging (mp-MRI) data, i.e. Apparent Diffusion Coefficients
(ADC) and T2-weighted (T2w), containing clinically significant (CS) prostate
cancer (PCa) via semi-supervised adversarial learning. Specifically, our
synthesizer generates mp-MRI data in a sequential manner: first generating ADC
maps from 128-d latent vectors, followed by translating them to the T2w images.
The synthesizer is trained in a semisupervised manner. In the supervised
training process, a limited amount of paired ADC-T2w images and the
corresponding ADC encodings are provided and the synthesizer learns the paired
relationship by explicitly minimizing the reconstruction losses between
synthetic and real images. To avoid overfitting limited ADC encodings, an
unlimited amount of random latent vectors and unpaired ADC-T2w Images are
utilized in the unsupervised training process for learning the marginal image
distributions of real images. To improve the robustness of synthesizing, we
decompose the difficult task of generating full-size images into several
simpler tasks which generate sub-images only. A StitchLayer is then employed to
fuse sub-images together in an interlaced manner into a full-size image. To
enforce the synthetic images to indeed contain distinguishable CS PCa lesions,
we propose to also maximize an auxiliary distance of Jensen-Shannon divergence
(JSD) between CS and nonCS images. Experimental results show that our method
can effectively synthesize a large variety of mpMRI images which contain
meaningful CS PCa lesions, display a good visual quality and have the correct
paired relationship. Compared to the state-of-the-art synthesis methods, our
method achieves a significant improvement in terms of both visual and
quantitative evaluation metrics.
Feature Fusion Effects of Tensor Product Representation on (De)Compositional Network for Caption Generation for Images
Authors: Chiranjib Sur
Date: 2018-12-17 00:00:00
Tasks: Caption Generation, Image Captioning, Language Modeling, Language Modelling, Sentence

Progress in image captioning is gradually getting complex as researchers try
to generalized the model and define the representation between visual features
and natural language processing. This work tried to define such kind of
relationship in the form of representation called Tensor Product Representation
(TPR) which generalized the scheme of language modeling and structuring the
linguistic attributes (related to grammar and parts of speech of language)
which will provide a much better structure and grammatically correct sentence.
TPR enables better and unique representation and structuring of the feature
space and will enable better sentence composition from these representations. A
large part of the different ways of defining and improving these TPR are
discussed and their performance with respect to the traditional procedures and
feature representations are evaluated for image captioning application. The new
models achieved considerable improvement than the corresponding previous
architectures.
Voiceprint recognition of Parkinson patients based on deep learning
Authors: Zhijing Xu, Juan Wang, Ying Zhang, Xiangjian He
Date: 2018-12-17 00:00:00
Tasks: Deep Learning, General Classification

More than 90% of the Parkinson Disease (PD) patients suffer from vocal
disorders. Speech impairment is already indicator of PD. This study focuses on
PD diagnosis through voiceprint features. In this paper, a method based on Deep
Neural Network (DNN) recognition and classification combined with Mini-Batch
Gradient Descent (MBGD) is proposed to distinguish PD patients from healthy
people using voiceprint features. In order to exact the voiceprint features
from patients, Weighted Mel Frequency Cepstrum Coefficients (WMFCC) is applied.
The proposed method is tested on experimental data obtained by the voice
recordings of three sustained vowels /a/, /o/ and /u/ from participants (48 PD
and 20 healthy people). The results show that the proposed method achieves a
high accuracy of diagnosis of PD patients from healthy people, than the
conventional methods like Support Vector Machine (SVM) and other mentioned in
this paper. The accuracy achieved is 89.5%. WMFCC approach can solve the
problem that the high-order cepstrum coefficients are small and the features
component's representation ability to the audio is weak. MBGD reduces the
computational loads of the loss function, and increases the training speed of
the system. DNN classifier enhances the classification ability of voiceprint
features. Therefore, the above approaches can provide a solid solution for the
quick auxiliary diagnosis of PD in early stage.
A Layer Decomposition-Recomposition Framework for Neuron Pruning towards Accurate Lightweight Networks
Authors: Wei-Jie Chen, Yuan Zhang, Di Xie, ShiLiang Pu
Date: 2018-12-17 00:00:00

Neuron pruning is an efficient method to compress the network into a slimmer
one for reducing the computational cost and storage overhead. Most of
state-of-the-art results are obtained in a layer-by-layer optimization mode. It
discards the unimportant input neurons and uses the survived ones to
reconstruct the output neurons approaching to the original ones in a
layer-by-layer manner. However, an unnoticed problem arises that the
information loss is accumulated as layer increases since the survived neurons
still do not encode the entire information as before. A better alternative is
to propagate the entire useful information to reconstruct the pruned layer
instead of directly discarding the less important neurons. To this end, we
propose a novel Layer Decomposition-Recomposition Framework (LDRF) for neuron
pruning, by which each layer's output information is recovered in an embedding
space and then propagated to reconstruct the following pruned layers with
useful information preserved. We mainly conduct our experiments on ILSVRC-12
benchmark with VGG-16 and ResNet-50. What should be emphasized is that our
results before end-to-end fine-tuning are significantly superior owing to the
information-preserving property of our proposed framework.With end-to-end
fine-tuning, we achieve state-of-the-art results of 5.13x and 3x speed-up with
only 0.5% and 0.65% top-5 accuracy drop respectively, which outperform the
existing neuron pruning methods.
Deep Heterogeneous Autoencoders for Collaborative Filtering
Authors: Tianyu Li, Yukun Ma, Jiu Xu, Bjorn Stenger, Chen Liu, Yu Hirate
Date: 2018-12-17 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

This paper leverages heterogeneous auxiliary information to address the data
sparsity problem of recommender systems. We propose a model that learns a
shared feature space from heterogeneous data, such as item descriptions,
product tags and online purchase history, to obtain better predictions. Our
model consists of autoencoders, not only for numerical and categorical data,
but also for sequential data, which enables capturing user tastes, item
characteristics and the recent dynamics of user preference. We learn the
autoencoder architecture for each data source independently in order to better
model their statistical properties. Our evaluation on two MovieLens datasets
and an e-commerce dataset shows that mean average precision and recall improve
over state-of-the-art methods.