pwc-archive/papers-with-abstracts huggingface dataset:
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing
Authors: Dinghan Shen, Qinliang Su, Paidamoyo Chapfuwa, Wenlin Wang, Guoyin Wang, Lawrence Carin, Ricardo Henao
Date: 2018-05-14 00:00:00
Proceeding: ACL 2018 7
Tasks: Information Retrieval, Retrieval, Variational Inference

Semantic hashing has become a powerful paradigm for fast similarity search in
many information retrieval systems. While fairly successful, previous
techniques generally require two-stage training, and the binary constraints are
handled ad-hoc. In this paper, we present an end-to-end Neural Architecture for
Semantic Hashing (NASH), where the binary hashing codes are treated as
Bernoulli latent variables. A neural variational inference framework is
proposed for training, where gradients are directly back-propagated through the
discrete latent variable to optimize the hash function. We also draw
connections between proposed method and rate-distortion theory, which provides
a theoretical foundation for the effectiveness of the proposed framework.
Experimental results on three public datasets demonstrate that our method
significantly outperforms several state-of-the-art models on both unsupervised
and supervised scenarios.
SAVERS: SAR ATR with Verification Support Based on Convolutional Neural Network
Authors: Hidetoshi Furukawa
Date: 2018-05-14 00:00:00
Tasks: Segmentation

We propose a new convolutional neural network (CNN) which performs coarse and
fine segmentation for end-to-end synthetic aperture radar (SAR) automatic
target recognition (ATR) system. In recent years, many CNNs for SAR ATR using
deep learning have been proposed, but most of them classify target classes from
fixed size target chips extracted from SAR imagery. On the other hand, we
proposed the CNN which outputs the score of the multiple target classes and a
background class for each pixel from the SAR imagery of arbitrary size and
multiple targets as fine segmentation. However, it was necessary for humans to
judge the CNN segmentation result. In this report, we propose a CNN called SAR
ATR with verification support (SAVERS), which performs region-wise (i.e.
coarse) segmentation and pixel-wise segmentation. SAVERS discriminates between
target and non-target, and classifies multiple target classes and non-target
class by coarse segmentation. This report describes the evaluation results of
SAVERS using the Moving and Stationary Target Acquisition and Recognition
(MSTAR) dataset.
Conversations Gone Awry: Detecting Early Signs of Conversational Failure
Authors: Justine Zhang, Jonathan P. Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Nithum Thain, Dario Taraborelli
Date: 2018-05-14 00:00:00
Proceeding: ACL 2018 7

One of the main challenges online social systems face is the prevalence of
antisocial behavior, such as harassment and personal attacks. In this work, we
introduce the task of predicting from the very start of a conversation whether
it will get out of hand. As opposed to detecting undesirable behavior after the
fact, this task aims to enable early, actionable prediction at a time when the
conversation might still be salvaged.
  To this end, we develop a framework for capturing pragmatic devices---such as
politeness strategies and rhetorical prompts---used to start a conversation,
and analyze their relation to its future trajectory. Applying this framework in
a controlled setting, we demonstrate the feasibility of detecting early warning
signs of antisocial behavior in online discussions.
Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing
Authors: Deniz Engin, Anıl Genç, Hazim Kemal Ekenel
Date: 2018-05-14 00:00:00
Tasks: Image Dehazing, Single Image Dehazing

In this paper, we present an end-to-end network, called Cycle-Dehaze, for
single image dehazing problem, which does not require pairs of hazy and
corresponding ground truth images for training. That is, we train the network
by feeding clean and hazy images in an unpaired manner. Moreover, the proposed
approach does not rely on estimation of the atmospheric scattering model
parameters. Our method enhances CycleGAN formulation by combining
cycle-consistency and perceptual losses in order to improve the quality of
textural information recovery and generate visually better haze-free images.
Typically, deep learning models for dehazing take low resolution images as
input and produce low resolution outputs. However, in the NTIRE 2018 challenge
on single image dehazing, high resolution images were provided. Therefore, we
apply bicubic downscaling. After obtaining low-resolution outputs from the
network, we utilize the Laplacian pyramid to upscale the output images to the
original resolution. We conduct experiments on NYU-Depth, I-HAZE, and O-HAZE
datasets. Extensive experiments demonstrate that the proposed approach improves
CycleGAN method both quantitatively and qualitatively.
A Cost-Effective Framework for Preference Elicitation and Aggregation
Authors: Zhibing Zhao, Haoming Li, Junming Wang, Jeffrey Kephart, Nicholas Mattei, Hui Su, Lirong Xia
Date: 2018-05-14 00:00:00

We propose a cost-effective framework for preference elicitation and
aggregation under the Plackett-Luce model with features. Given a budget, our
framework iteratively computes the most cost-effective elicitation questions in
order to help the agents make a better group decision.
  We illustrate the viability of the framework with experiments on Amazon
Mechanical Turk, which we use to estimate the cost of answering different types
of elicitation questions. We compare the prediction accuracy of our framework
when adopting various information criteria that evaluate the expected
information gain from a question. Our experiments show carefully designed
information criteria are much more efficient, i.e., they arrive at the correct
answer using fewer queries, than randomly asking questions given the budget
constraint.
AMR Parsing as Graph Prediction with Latent Alignment
Authors: Chunchuan Lyu, Ivan Titov
Date: 2018-05-14 00:00:00
Proceeding: ACL 2018 7
Tasks: AMR Parsing, Prediction, Sentence

Abstract meaning representations (AMRs) are broad-coverage sentence-level
semantic representations. AMRs represent sentences as rooted labeled directed
acyclic graphs. AMR parsing is challenging partly due to the lack of annotated
alignments between nodes in the graphs and words in the corresponding
sentences. We introduce a neural parser which treats alignments as latent
variables within a joint probabilistic model of concepts, relations and
alignments. As exact inference requires marginalizing over alignments and is
infeasible, we use the variational auto-encoding framework and a continuous
relaxation of the discrete alignments. We show that joint modeling is
preferable to using a pipeline of align and parse. The parser achieves the best
reported results on the standard benchmark (74.4% on LDC2016E25).
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
Authors: Guokan Shang, Wensi Ding, Zekun Zhang, Antoine Jean-Pierre Tixier, Polykarpos Meladianos, Michalis Vazirgiannis, Jean-Pierre Lorré
Date: 2018-05-14 00:00:00
Proceeding: ACL 2018 7
Tasks: Abstractive Dialogue Summarization, Abstractive Text Summarization, Dialogue Understanding, Diversity, Informativeness, Meeting Summarization, Sentence, Sentence Compression, Word Embeddings

We introduce a novel graph-based framework for abstractive meeting speech
summarization that is fully unsupervised and does not rely on any annotations.
Our work combines the strengths of multiple recent approaches while addressing
their weaknesses. Moreover, we leverage recent advances in word embeddings and
graph degeneracy applied to NLP to take exterior semantic knowledge into
account, and to design custom diversity and informativeness measures.
Experiments on the AMI and ICSI corpus show that our system improves on the
state-of-the-art. Code and data are publicly available, and our system can be
interactively tested.
Normal Similarity Network for Generative Modelling
Authors: Jay Nandy, Wynne Hsu, Mong Li Lee
Date: 2018-05-14 00:00:00
Tasks: Density Estimation, Image Generation

Gaussian distributions are commonly used as a key building block in many
generative models. However, their applicability has not been well explored in
deep networks. In this paper, we propose a novel deep generative model named as
Normal Similarity Network (NSN) where the layers are constructed with
Gaussian-style filters. NSN is trained with a layer-wise non-parametric density
estimation algorithm that iteratively down-samples the training images and
captures the density of the down-sampled training images in the final layer.
Additionally, we propose NSN-Gen for generating new samples from noise vectors
by iteratively reconstructing feature maps in the hidden layers of NSN. Our
experiments suggest encouraging results of the proposed model for a wide range
of computer vision applications including image generation, styling and
reconstruction from occluded images.
Maximizing Expected Impact in an Agent Reputation Network -- Technical Report
Authors: Gavin Rens, Abhaya Nayak, Thomas Meyer
Date: 2018-05-14 00:00:00

Many multi-agent systems (MASs) are situated in stochastic environments. Some
such systems that are based on the partially observable Markov decision process
(POMDP) do not take the benevolence of other agents for granted. We propose a
new POMDP-based framework which is general enough for the specification of a
variety of stochastic MAS domains involving the impact of agents on each
other's reputations. A unique feature of this framework is that actions are
specified as either undirected (regular) or directed (towards a particular
agent), and a new directed transition function is provided for modeling the
effects of reputation in interactions. Assuming that an agent must maintain a
good enough reputation to survive in the network, a planning algorithm is
developed for an agent to select optimal actions in stochastic MASs.
Preliminary evaluation is provided via an example specification and by
determining the algorithm's complexity.
Coulomb Autoencoders
Authors: Emanuele Sansone, Hafiz Tiomoko Ali, Sun Jiacheng
Date: 2018-02-10 00:00:00

Learning the true density in high-dimensional feature spaces is a well-known problem in machine learning. In this work, we consider generative autoencoders based on maximum-mean discrepancy (MMD) and provide theoretical insights. In particular, (i) we prove that MMD coupled with Coulomb kernels has optimal convergence properties, which are similar to convex functionals, thus improving the training of autoencoders, and (ii) we provide a probabilistic bound on the generalization performance, highlighting some fundamental conditions to achieve better generalization. We validate the theory on synthetic examples and on the popular dataset of celebrities' faces, showing that our model, called Coulomb autoencoders, outperform the state-of-the-art.
Practical Block-wise Neural Network Architecture Generation
Authors: Zhao Zhong, Junjie Yan, Wei Wu, Jing Shao, Cheng-Lin Liu
Date: 2017-08-18 00:00:00
Proceeding: CVPR 2018 6
Tasks: image-classification, Image Classification, Q-Learning

Convolutional neural networks have gained a remarkable success in computer
vision. However, most usable network architectures are hand-crafted and usually
require expertise and elaborate design. In this paper, we provide a block-wise
network generation pipeline called BlockQNN which automatically builds
high-performance networks using the Q-Learning paradigm with epsilon-greedy
exploration strategy. The optimal network block is constructed by the learning
agent which is trained sequentially to choose component layers. We stack the
block to construct the whole auto-generated network. To accelerate the
generation process, we also propose a distributed asynchronous framework and an
early stop strategy. The block-wise generation brings unique advantages: (1) it
performs competitive results in comparison to the hand-crafted state-of-the-art
networks on image classification, additionally, the best network generated by
BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing
auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of
the search space in designing networks which only spends 3 days with 32 GPUs,
and (3) moreover, it has strong generalizability that the network built on
CIFAR also performs well on a larger-scale ImageNet dataset.
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning
Authors: Oleksii Zhelo, Jingwei Zhang, Lei Tai, Ming Liu, Wolfram Burgard
Date: 2018-04-02 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper investigates exploration strategies of Deep Reinforcement Learning
(DRL) methods to learn navigation policies for mobile robots. In particular, we
augment the normal external reward for training DRL algorithms with intrinsic
reward signals measured by curiosity. We test our approach in a mapless
navigation setting, where the autonomous agent is required to navigate without
the occupancy map of the environment, to targets whose relative locations can
be easily acquired through low-cost solutions (e.g., visible light
localization, Wi-Fi signal localization). We validate that the intrinsic
motivation is crucial for improving DRL performance in tasks with challenging
exploration requirements. Our experimental results show that our proposed
method is able to more effectively learn navigation policies, and has better
generalization capabilities in previously unseen environments. A video of our
experimental results can be found at https://goo.gl/pWbpcF.
Code Completion with Neural Attention and Pointer Networks
Authors: Jian Li, Yue Wang, Michael R. Lyu, Irwin King
Date: 2017-11-27 00:00:00
Tasks: Code Completion

Intelligent code completion has become an essential research task to
accelerate modern software development. To facilitate effective code completion
for dynamically-typed programming languages, we apply neural language models by
learning from large codebases, and develop a tailored attention mechanism for
code completion. However, standard neural language models even with attention
mechanism cannot correctly predict the out-of-vocabulary (OoV) words that
restrict the code completion performance. In this paper, inspired by the
prevalence of locally repeated terms in program source code, and the recently
proposed pointer copy mechanism, we propose a pointer mixture network for
better predicting OoV words in code completion. Based on the context, the
pointer mixture network learns to either generate a within-vocabulary word
through an RNN component, or regenerate an OoV word from local context through
a pointer component. Experiments on two benchmarked datasets demonstrate the
effectiveness of our attention mechanism and pointer mixture network on the
code completion task.
A giant with feet of clay: on the validity of the data that feed machine learning in medicine
Authors: Federico Cabitza, Davide Ciucci, Raffaele Rasoini
Date: 2017-06-21 00:00:00

This paper considers the use of Machine Learning (ML) in medicine by focusing
on the main problem that this computational approach has been aimed at solving
or at least minimizing: uncertainty. To this aim, we point out how uncertainty
is so ingrained in medicine that it biases also the representation of clinical
phenomena, that is the very input of ML models, thus undermining the clinical
significance of their output. Recognizing this can motivate both medical
doctors, in taking more responsibility in the development and use of these
decision aids, and the researchers, in pursuing different ways to assess the
value of these systems. In so doing, both designers and users could take this
intrinsic characteristic of medicine more seriously and consider alternative
approaches that do not "sweep uncertainty under the rug" within an objectivist
fiction, which everyone can come up by believing as true.
Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic
Authors: Benjamin J. Radford, Bartley D. Richardson, Shawn E. Davis
Date: 2018-05-09 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

We evaluate methods for applying unsupervised anomaly detection to
cybersecurity applications on computer network traffic data, or flow. We borrow
from the natural language processing literature and conceptualize flow as a
sort of "language" spoken between machines. Five sequence aggregation rules are
evaluated for their efficacy in flagging multiple attack types in a labeled
flow dataset, CICIDS2017. For sequence modeling, we rely on long short-term
memory (LSTM) recurrent neural networks (RNN). Additionally, a simple
frequency-based model is described and its performance with respect to attack
detection is compared to the LSTM models. We conclude that the frequency-based
model tends to perform as well as or better than the LSTM models for the tasks
at hand, with a few notable exceptions.
Generating Synthetic X-ray Images of a Person from the Surface Geometry
Authors: Brian Teixeira, Vivek Singh, Terrence Chen, Kai Ma, Birgi Tamersoy, Yifan Wu, Elena Balashova, Dorin Comaniciu
Date: 2018-05-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Anatomy, Anomaly Detection

We present a novel framework that learns to predict human anatomy from body
surface. Specifically, our approach generates a synthetic X-ray image of a
person only from the person's surface geometry. Furthermore, the synthetic
X-ray image is parametrized and can be manipulated by adjusting a set of body
markers which are also generated during the X-ray image prediction. With the
proposed framework, multiple synthetic X-ray images can easily be generated by
varying surface geometry. By perturbing the parameters, several additional
synthetic X-ray images can be generated from the same surface geometry. As a
result, our approach offers a potential to overcome the training data barrier
in the medical domain. This capability is achieved by learning a pair of
networks - one learns to generate the full image from the partial image and a
set of parameters, and the other learns to estimate the parameters given the
full image. During training, the two networks are trained iteratively such that
they would converge to a solution where the predicted parameters and the full
image are consistent with each other. In addition to medical data enrichment,
our framework can also be used for image completion as well as anomaly
detection.
SHADE: Information-Based Regularization for Deep Learning
Authors: Michael Blot, Thomas Robert, Nicolas Thome, Matthieu Cord
Date: 2018-05-14 00:00:00
Tasks: Deep Learning, General Classification

Regularization is a big issue for training deep neural networks. In this
paper, we propose a new information-theory-based regularization scheme named
SHADE for SHAnnon DEcay. The originality of the approach is to define a prior
based on conditional entropy, which explicitly decouples the learning of
invariant representations in the regularizer and the learning of correlations
between inputs and labels in the data fitting term. Our second contribution is
to derive a stochastic version of the regularizer compatible with deep
learning, resulting in a tractable training scheme. We empirically validate the
efficiency of our approach to improve classification performances compared to
standard regularization schemes on several standard architectures.
Generative Adversarial Forests for Better Conditioned Adversarial Learning
Authors: Yan Zuo, Gil Avraham, Tom Drummond
Date: 2018-05-14 00:00:00

In recent times, many of the breakthroughs in various vision-related tasks
have revolved around improving learning of deep models; these methods have
ranged from network architectural improvements such as Residual Networks, to
various forms of regularisation such as Batch Normalisation. In essence, many
of these techniques revolve around better conditioning, allowing for deeper and
deeper models to be successfully learned. In this paper, we look towards better
conditioning Generative Adversarial Networks (GANs) in an unsupervised learning
setting. Our method embeds the powerful discriminating capabilities of a
decision forest into the discriminator of a GAN. This results in a better
conditioned model which learns in an extremely stable way. We demonstrate
empirical results which show both clear qualitative and quantitative evidence
of the effectiveness of our approach, gaining significant performance
improvements over several popular GAN-based approaches on the Oxford Flowers
and Aligned Celebrity Faces datasets.
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
Authors: Jingjing Xu, Xu sun, Qi Zeng, Xuancheng Ren, Xiaodong Zhang, Houfeng Wang, Wenjie Li
Date: 2018-05-14 00:00:00
Proceeding: ACL 2018 7
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, Text Style Transfer, Translation

The goal of sentiment-to-sentiment "translation" is to change the underlying
sentiment of a sentence while keeping its content. The main challenge is the
lack of parallel data. To solve this problem, we propose a cycled reinforcement
learning method that enables training on unpaired data by collaboration between
a neutralization module and an emotionalization module. We evaluate our
approach on two review datasets, Yelp and Amazon. Experimental results show
that our approach significantly outperforms the state-of-the-art systems.
Especially, the proposed method substantially improves the content preservation
performance. The BLEU score is improved from 1.64 to 22.46 and from 0.56 to
14.06 on the two datasets, respectively.
Full 3D Reconstruction of Transparent Objects
Authors: Bojian Wu, Yang Zhou, Yiming Qian, Minglun Gong, Hui Huang
Date: 2018-05-09 00:00:00
Tasks: 3D Reconstruction, Transparent objects

Numerous techniques have been proposed for reconstructing 3D models for
opaque objects in past decades. However, none of them can be directly applied
to transparent objects. This paper presents a fully automatic approach for
reconstructing complete 3D shapes of transparent objects. Through positioning
an object on a turntable, its silhouettes and light refraction paths under
different viewing directions are captured. Then, starting from an initial rough
model generated from space carving, our algorithm progressively optimizes the
model under three constraints: surface and refraction normal consistency,
surface projection and silhouette consistency, and surface smoothness.
Experimental results on both synthetic and real objects demonstrate that our
method can successfully recover the complex shapes of transparent objects and
faithfully reproduce their light refraction properties.