pwc-archive/papers-with-abstracts huggingface dataset:
Leveraging Medical Sentiment to Understand Patients Health on Social Media
Authors: Shweta Yadav, Joy Sain, Amit Sheth, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
Date: 2018-07-30 00:00:00

The unprecedented growth of Internet users in recent years has resulted in an
abundance of unstructured information in the form of social media text. A large
percentage of this population is actively engaged in health social networks to
share health-related information. In this paper, we address an important and
timely topic by analyzing the users' sentiments and emotions w.r.t their
medical conditions. Towards this, we examine users on popular medical forums
(Patient.info,dailystrength.org), where they post on important topics such as
asthma, allergy, depression, and anxiety. First, we provide a benchmark setup
for the task by crawling the data, and further define the sentiment specific
fine-grained medical conditions (Recovered, Exist, Deteriorate, and Other). We
propose an effective architecture that uses a Convolutional Neural Network
(CNN) as a data-driven feature extractor and a Support Vector Machine (SVM) as
a classifier. We further develop a sentiment feature which is sensitive to the
medical context. Here, we show that the use of medical sentiment feature along
with extracted features from CNN improves the model performance. In addition to
our dataset, we also evaluate our approach on the benchmark "CLEF eHealth 2014"
corpora and show that our model outperforms the state-of-the-art techniques.
Online Learning with an Almost Perfect Expert
Authors: Simina Brânzei, Yuval Peres
Date: 2018-07-30 00:00:00

We study the multiclass online learning problem where a forecaster makes a
sequence of predictions using the advice of $n$ experts. Our main contribution
is to analyze the regime where the best expert makes at most $b$ mistakes and
to show that when $b = o(\log_4{n})$, the expected number of mistakes made by
the optimal forecaster is at most $\log_4{n} + o(\log_4{n})$. We also describe
an adversary strategy showing that this bound is tight and that the worst case
is attained for binary prediction.
A Group-Theoretic Approach to Computational Abstraction: Symmetry-Driven Hierarchical Clustering
Authors: Haizi Yu, Igor Mineyev, Lav R. Varshney
Date: 2018-07-30 00:00:00
Tasks: Clustering

Abstraction plays a key role in concept learning and knowledge discovery; this paper is concerned with computational abstraction. In particular, we study the nature of abstraction through a group-theoretic approach, formalizing it as symmetry-driven---as opposed to data-driven---hierarchical clustering. Thus, the resulting clustering framework is data-free, feature-free, similarity-free, and globally hierarchical---the four key features that distinguish it from common data clustering models such as $k$-means. Beyond a theoretical foundation for abstraction, we also present a top-down and a bottom-up approach to establish an algorithmic foundation for practical abstraction-generating methods. Lastly, via both a theoretical explanation and a real-world application, we illustrate that further coupling of our abstraction framework with statistics realizes Shannon's information lattice and even further, brings learning into the picture. This not only presents one use case of our proposed computational abstraction, but also gives a first step towards a principled and cognitive way of automatic concept learning and knowledge discovery.
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Authors: Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun
Date: 2018-07-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Classification, Object Detection

Datasets, Transforms and Models specific to Computer Vision
Lead Sheet Generation and Arrangement by Conditional Generative Adversarial Network
Authors: Hao-Min Liu, Yi-Hsuan Yang
Date: 2018-07-30 00:00:00
Tasks: Generative Adversarial Network, Music Generation

Research on automatic music generation has seen great progress due to the
development of deep neural networks. However, the generation of
multi-instrument music of arbitrary genres still remains a challenge. Existing
research either works on lead sheets or multi-track piano-rolls found in MIDIs,
but both musical notations have their limits. In this work, we propose a new
task called lead sheet arrangement to avoid such limits. A new recurrent
convolutional generative model for the task is proposed, along with three new
symbolic-domain harmonic features to facilitate learning from unpaired lead
sheets and MIDIs. Our model can generate lead sheets and their arrangements of
eight-bar long. Audio samples of the generated result can be found at
https://drive.google.com/open?id=1c0FfODTpudmLvuKBbc23VBCgQizY6-Rk
Robust Student Network Learning
Authors: Tianyu Guo, Chang Xu, Shiyi He, Boxin Shi, Chao Xu, DaCheng Tao
Date: 2018-07-30 00:00:00

Deep neural networks bring in impressive accuracy in various applications,
but the success often relies on the heavy network architecture. Taking
well-trained heavy networks as teachers, classical teacher-student learning
paradigm aims to learn a student network that is lightweight yet accurate. In
this way, a portable student network with significantly fewer parameters can
achieve a considerable accuracy which is comparable to that of teacher network.
However, beyond accuracy, robustness of the learned student network against
perturbation is also essential for practical uses. Existing teacher-student
learning frameworks mainly focus on accuracy and compression ratios, but ignore
the robustness. In this paper, we make the student network produce more
confident predictions with the help of the teacher network, and analyze the
lower bound of the perturbation that will destroy the confidence of the student
network. Two important objectives regarding prediction scores and gradients of
examples are developed to maximize this lower bound, so as to enhance the
robustness of the student network without sacrificing the performance.
Experiments on benchmark datasets demonstrate the efficiency of the proposed
approach to learn robust student networks which have satisfying accuracy and
compact sizes.
Transformationally Identical and Invariant Convolutional Neural Networks by Combining Symmetric Operations or Input Vectors
Authors: ShihChung B. Lo, Matthew T. Freedman, Seong K. Mun
Date: 2018-07-30 00:00:00

Transformationally invariant processors constructed by transformed input
vectors or operators have been suggested and applied to many applications. In
this study, transformationally identical processing based on combining results
of all sub-processes with corresponding transformations at one of the
processing steps or at the beginning step were found to be equivalent for a
given condition. This property can be applied to most convolutional neural
network (CNN) systems. Specifically, a transformationally identical CNN can be
constructed by arranging internally symmetric operations in parallel with the
same transformation family that includes a flatten layer with weights sharing
among their corresponding transformation elements. Other transformationally
identical CNNs can be constructed by averaging transformed input vectors of the
family at the input layer followed by an ordinary CNN process or by a set of
symmetric operations. Interestingly, we found that both types of
transformationally identical CNN systems are mathematically equivalent by
either applying an averaging operation to corresponding elements of all
sub-channels before the activation function or without using a non-linear
activation function.
Pose Guided Human Video Generation
Authors: Ceyuan Yang, Zhe Wang, Xinge Zhu, Chen Huang, Jianping Shi, Dahua Lin
Date: 2018-07-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Generative Adversarial Network, motion prediction, Video Generation

Due to the emergence of Generative Adversarial Networks, video synthesis has
witnessed exceptional breakthroughs. However, existing methods lack a proper
representation to explicitly control the dynamics in videos. Human pose, on the
other hand, can represent motion patterns intrinsically and interpretably, and
impose the geometric constraints regardless of appearance. In this paper, we
propose a pose guided method to synthesize human videos in a disentangled way:
plausible motion prediction and coherent appearance generation. In the first
stage, a Pose Sequence Generative Adversarial Network (PSGAN) learns in an
adversarial manner to yield pose sequences conditioned on the class label. In
the second stage, a Semantic Consistent Generative Adversarial Network (SCGAN)
generates video frames from the poses while preserving coherent appearances in
the input image. By enforcing semantic consistency between the generated and
ground-truth poses at a high feature level, our SCGAN is robust to noisy or
abnormal poses. Extensive experiments on both human action and human face
datasets manifest the superiority of the proposed method over other
state-of-the-arts.
Occluded Joints Recovery in 3D Human Pose Estimation based on Distance Matrix
Authors: Xiang Guo, Yuchao Dai
Date: 2018-07-30 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

Albeit the recent progress in single image 3D human pose estimation due to
the convolutional neural network, it is still challenging to handle real
scenarios such as highly occluded scenes. In this paper, we propose to address
the problem of single image 3D human pose estimation with occluded measurements
by exploiting the Euclidean distance matrix (EDM). Specifically, we present two
approaches based on EDM, which could effectively handle occluded joints in 2D
images. The first approach is based on 2D-to-2D distance matrix regression
achieved by a simple CNN architecture. The second approach is based on sparse
coding along with a learned over-complete dictionary. Experiments on the
Human3.6M dataset show the excellent performance of these two approaches in
recovering occluded observations and demonstrate the improvements in accuracy
for 3D human pose estimation with occluded joints.
ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks
Authors: Mingzhang Yin, Mingyuan Zhou
Date: 2018-07-30 00:00:00
Proceeding: ICLR 2019 5
Tasks: Data Augmentation, Variational Inference

To backpropagate the gradients through stochastic binary layers, we propose the augment-REINFORCE-merge (ARM) estimator that is unbiased, exhibits low variance, and has low computational complexity. Exploiting variable augmentation, REINFORCE, and reparameterization, the ARM estimator achieves adaptive variance reduction for Monte Carlo integration by merging two expectations via common random numbers. The variance-reduction mechanism of the ARM estimator can also be attributed to either antithetic sampling in an augmented space, or the use of an optimal anti-symmetric "self-control" baseline function together with the REINFORCE estimator in that augmented space. Experimental results show the ARM estimator provides state-of-the-art performance in auto-encoding variational inference and maximum likelihood estimation, for discrete latent variable models with one or multiple stochastic binary layers. Python code for reproducible research is publicly available.
Causal Modeling with Probabilistic Simulation Models
Authors: Duligur Ibeling
Date: 2018-07-30 00:00:00

Recent authors have proposed analyzing conditional reasoning through a notion
of intervention on a simulation program, and have found a sound and complete
axiomatization of the logic of conditionals in this setting. Here we extend
this setting to the case of probabilistic simulation models. We give a natural
definition of probability on formulas of the conditional language, allowing for
the expression of counterfactuals, and prove foundational results about this
definition. We also find an axiomatization for reasoning about linear
inequalities involving probabilities in this setting. We prove soundness,
completeness, and NP-completeness of the satisfiability problem for this logic.
Geo-Supervised Visual Depth Prediction
Authors: Xiaohan Fei, Alex Wong, Stefano Soatto
Date: 2018-07-30 00:00:00
Tasks: 3D Reconstruction, Depth Estimation, Depth Prediction, Prediction

We propose using global orientation from inertial measurements, and the bias it induces on the shape of objects populating the scene, to inform visual 3D reconstruction. We test the effect of using the resulting prior in depth prediction from a single image, where the normal vectors to surfaces of objects of certain classes tend to align with gravity or be orthogonal to it. Adding such a prior to baseline methods for monocular depth prediction yields improvements beyond the state-of-the-art and illustrates the power of gravity as a supervisory signal.
Microsoft Dialogue Challenge: Building End-to-End Task-Completion Dialogue Systems
Authors: Xiujun Li, Yu Wang, Siqi Sun, Sarah Panda, Jingjing Liu, Jianfeng Gao
Date: 2018-07-29 00:00:00

This proposal introduces a Dialogue Challenge for building end-to-end
task-completion dialogue systems, with the goal of encouraging the dialogue
research community to collaborate and benchmark on standard datasets and
unified experimental environment. In this special session, we will release
human-annotated conversational data in three domains (movie-ticket booking,
restaurant reservation, and taxi booking), as well as an experiment platform
with built-in simulators in each domain, for training and evaluation purposes.
The final submitted systems will be evaluated both in simulated setting and by
human judges.
Story Understanding in Video Advertisements
Authors: Keren Ye, Kyle Buettner, Adriana Kovashka
Date: 2018-07-29 00:00:00

In order to resonate with the viewers, many video advertisements explore
creative narrative techniques such as "Freytag's pyramid" where a story begins
with exposition, followed by rising action, then climax, concluding with
denouement. In the dramatic structure of ads in particular, climax depends on
changes in sentiment. We dedicate our study to understand the dynamic structure
of video ads automatically. To achieve this, we first crowdsource climax
annotations on 1,149 videos from the Video Ads Dataset, which already provides
sentiment annotations. We then use both unsupervised and supervised methods to
predict the climax. Based on the predicted peak, the low-level visual and audio
cues, and semantically meaningful context features, we build a sentiment
prediction model that outperforms the current state-of-the-art model of
sentiment prediction in video ads by 25%. In our ablation study, we show that
using our context features, and modeling dynamics with an LSTM, are both
crucial factors for improved performance.
Neural Mesh: Introducing a Notion of Space and Conservation of Energy to Neural Networks
Authors: Jacob Beck, Zoe Papakipos
Date: 2018-07-29 00:00:00

Neural networks are based on a simplified model of the brain. In this
project, we wanted to relax the simplifying assumptions of a traditional neural
network by making a model that more closely emulates the low level interactions
of neurons. Like in an RNN, our model has a state that persists between time
steps, so that the energies of neurons persist. However, unlike an RNN, our
state consists of a 2 dimensional matrix, rather than a 1 dimensional vector,
thereby introducing a concept of distance to other neurons within the state. In
our model, neurons can only fire to adjacent neurons, as in the brain. Like in
the brain, we only allow neurons to fire in a time step if they contain enough
energy, or excitement. We also enforce a notion of conservation of energy, so
that a neuron cannot excite its neighbors more than the excitement it already
contained at that time step. Taken together, these two features allow signals
in the form of activations to flow around in our network over time, making our
neural mesh more closely model signals traveling through the brain the brain.
Although our main goal is to design an architecture to more closely emulate the
brain in the hope of having a correct internal representation of information by
the time we know how to properly train a general intelligence, we did benchmark
our neural mash on a specific task. We found that by increasing the runtime of
the mesh, we were able to increase its accuracy without increasing the number
of parameters.
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images
Authors: Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing
Date: 2018-07-29 00:00:00
Tasks: Medical Image Analysis, whole slide images

Convolutional neural networks have led to significant breakthroughs in the
domain of medical image analysis. However, the task of breast cancer
segmentation in whole-slide images (WSIs) is still underexplored. WSIs are
large histopathological images with extremely high resolution. Constrained by
the hardware and field of view, using high-magnification patches can slow down
the inference process and using low-magnification patches can cause the loss of
information. In this paper, we aim to achieve two seemingly conflicting goals
for breast cancer segmentation: accurate and fast prediction. We propose a
simple yet efficient framework Reinforced Auto-Zoom Net (RAZN) to tackle this
task. Motivated by the zoom-in operation of a pathologist using a digital
microscope, RAZN learns a policy network to decide whether zooming is required
in a given region of interest. Because the zoom-in action is selective, RAZN is
robust to unbalanced and noisy ground truth labels and can efficiently reduce
overfitting. We evaluate our method on a public breast cancer dataset. RAZN
outperforms both single-scale and multi-scale baseline approaches, achieving
better accuracy at low inference cost.
Is One Hyperparameter Optimizer Enough?
Authors: Huy Tu, Vivek Nair
Date: 2018-07-29 00:00:00
Tasks: Bayesian Optimization, Hyperparameter Optimization

Hyperparameter tuning is the black art of automatically finding a good
combination of control parameters for a data miner. While widely applied in
empirical Software Engineering, there has not been much discussion on which
hyperparameter tuner is best for software analytics. To address this gap in the
literature, this paper applied a range of hyperparameter optimizers (grid
search, random search, differential evolution, and Bayesian optimization) to
defect prediction problem. Surprisingly, no hyperparameter optimizer was
observed to be `best' and, for one of the two evaluation measures studied here
(F-measure), hyperparameter optimization, in 50\% cases, was no better than
using default configurations.
  We conclude that hyperparameter optimization is more nuanced than previously
believed. While such optimization can certainly lead to large improvements in
the performance of classifiers used in software analytics, it remains to be
seen which specific optimizers should be applied to a new dataset.
StructADMM: A Systematic, High-Efficiency Framework of Structured Weight Pruning for DNNs
Authors: Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Xiaolong Ma, Ning Liu, Linfeng Zhang, Jian Tang, Kaisheng Ma, Xue Lin, Makan Fardad, Yanzhi Wang
Date: 2018-07-29 00:00:00
Tasks: CPU, GPU, Model Compression

Weight pruning methods of DNNs have been demonstrated to achieve a good model
pruning rate without loss of accuracy, thereby alleviating the significant
computation/storage requirements of large-scale DNNs. Structured weight pruning
methods have been proposed to overcome the limitation of irregular network
structure and demonstrated actual GPU acceleration. However, in prior work the
pruning rate (degree of sparsity) and GPU acceleration are limited (to less
than 50%) when accuracy needs to be maintained. In this work,we overcome these
limitations by proposing a unified, systematic framework of structured weight
pruning for DNNs. It is a framework that can be used to induce different types
of structured sparsity, such as filter-wise, channel-wise, and shape-wise
sparsity, as well non-structured sparsity. The proposed framework incorporates
stochastic gradient descent with ADMM, and can be understood as a dynamic
regularization method in which the regularization target is analytically
updated in each iteration. Without loss of accuracy on the AlexNet model, we
achieve 2.58X and 3.65X average measured speedup on two GPUs, clearly
outperforming the prior work. The average speedups reach 3.15X and 8.52X when
allowing a moderate ac-curacy loss of 2%. In this case the model compression
for convolutional layers is 15.0X, corresponding to 11.93X measured CPU
speedup. Our experiments on ResNet model and on other data sets like UCF101 and
CIFAR-10 demonstrate the consistently higher performance of our framework.
Towards Automatic Speech Identification from Vocal Tract Shape Dynamics in Real-time MRI
Authors: Pramit Saha, Praneeth Srungarapu, Sidney Fels
Date: 2018-07-29 00:00:00
Tasks: Action Recognition, Classification, General Classification, Temporal Action Localization, Video Classification

Vocal tract configurations play a vital role in generating distinguishable
speech sounds, by modulating the airflow and creating different resonant
cavities in speech production. They contain abundant information that can be
utilized to better understand the underlying speech production mechanism. As a
step towards automatic mapping of vocal tract shape geometry to acoustics, this
paper employs effective video action recognition techniques, like Long-term
Recurrent Convolutional Networks (LRCN) models, to identify different
vowel-consonant-vowel (VCV) sequences from dynamic shaping of the vocal tract.
Such a model typically combines a CNN based deep hierarchical visual feature
extractor with Recurrent Networks, that ideally makes the network
spatio-temporally deep enough to learn the sequential dynamics of a short video
clip for video classification tasks. We use a database consisting of 2D
real-time MRI of vocal tract shaping during VCV utterances by 17 speakers. The
comparative performances of this class of algorithms under various parameter
settings and for various classification tasks are discussed. Interestingly, the
results show a marked difference in the model performance in the context of
speech classification with respect to generic sequence or video classification
tasks.
Convolutional Gated Recurrent Units for Medical Relation Classification
Authors: Bin He, Yi Guan, Rui Dai
Date: 2018-07-29 00:00:00
Tasks: Classification, General Classification, Relation, Relation Classification

Convolutional neural network (CNN) and recurrent neural network (RNN) models
have become the mainstream methods for relation classification. We propose a
unified architecture, which exploits the advantages of CNN and RNN
simultaneously, to identify medical relations in clinical records, with only
word embedding features. Our model learns phrase-level features through a CNN
layer, and these feature representations are directly fed into a bidirectional
gated recurrent unit (GRU) layer to capture long-term feature dependencies. We
evaluate our model on two clinical datasets, and experiments demonstrate that
our model performs significantly better than previous single-model methods on
both datasets.