pwc-archive/papers-with-abstracts huggingface dataset:
Generative Adversarial Network for Abstractive Text Summarization
Authors: Linqing Liu, Yao Lu, Min Yang, Qiang Qu, Jia Zhu, Hongyan Li
Date: 2017-11-26 00:00:00
Tasks: Abstractive Text Summarization, Generative Adversarial Network, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Text Summarization

In this paper, we propose an adversarial process for abstractive text
summarization, in which we simultaneously train a generative model G and a
discriminative model D. In particular, we build the generator G as an agent of
reinforcement learning, which takes the raw text as input and predicts the
abstractive summarization. We also build a discriminator which attempts to
distinguish the generated summary from the ground truth summary. Extensive
experiments demonstrate that our model achieves competitive ROUGE scores with
the state-of-the-art methods on CNN/Daily Mail dataset. Qualitatively, we show
that our model is able to generate more abstractive, readable and diverse
summaries.
Automatic Color Image Segmentation Using a Square Elemental Region-Based Seeded Region Growing and Merging Method
Authors: Hisashi Shimodaira
Date: 2017-11-26 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

This paper presents an efficient automatic color image segmentation method
using a seeded region growing and merging method based on square elemental
regions. Our segmentation method consists of the three steps: generating seed
regions, merging the regions, and applying a pixel-wise boundary determination
algorithm to the resultant polygonal regions. The major features of our method
are as follows: the use of square elemental regions instead of pixels as the
processing unit, a seed generation method based on enhanced gradient values, a
seed region growing method exploiting local gradient values, a region merging
method using a similarity measure including a homogeneity distance based on
Tsallis entropy, and a termination condition of region merging using an
estimated desired number of regions. Using square regions as the processing
unit substantially reduces the time complexity of the algorithm and makes the
performance stable. The experimental results show that our method exhibits
stable performance for a variety of natural images, including heavily textured
areas, and produces good segmentation results using the same parameter values.
The results of our method are fairly comparable to, and in some respects better
than, those of existing algorithms.
HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrieval
Authors: Xi Zhang, Siyu Zhou, Jiashi Feng, Hanjiang Lai, Bo Li, Yan Pan, Jian Yin, Shuicheng Yan
Date: 2017-11-26 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval

As the rapid growth of multi-modal data, hashing methods for cross-modal
retrieval have received considerable attention. Deep-networks-based cross-modal
hashing methods are appealing as they can integrate feature learning and hash
coding into end-to-end trainable frameworks. However, it is still challenging
to find content similarities between different modalities of data due to the
heterogeneity gap. To further address this problem, we propose an adversarial
hashing network with attention mechanism to enhance the measurement of content
similarities by selectively focusing on informative parts of multi-modal data.
The proposed new adversarial network, HashGAN, consists of three building
blocks: 1) the feature learning module to obtain feature representations, 2)
the generative attention module to generate an attention mask, which is used to
obtain the attended (foreground) and the unattended (background) feature
representations, 3) the discriminative hash coding module to learn hash
functions that preserve the similarities between different modalities. In our
framework, the generative module and the discriminative module are trained in
an adversarial way: the generator is learned to make the discriminator cannot
preserve the similarities of multi-modal data w.r.t. the background feature
representations, while the discriminator aims to preserve the similarities of
multi-modal data w.r.t. both the foreground and the background feature
representations. Extensive evaluations on several benchmark datasets
demonstrate that the proposed HashGAN brings substantial improvements over
other state-of-the-art cross-modal hashing methods.
Semantically Consistent Image Completion with Fine-grained Details
Authors: Pengpeng Liu, Xiaojuan Qi, Pinjia He, Yikang Li, Michael R. Lyu, Irwin King
Date: 2017-11-26 00:00:00
Tasks: Image Inpainting

Image completion has achieved significant progress due to advances in
generative adversarial networks (GANs). Albeit natural-looking, the synthesized
contents still lack details, especially for scenes with complex structures or
images with large holes. This is because there exists a gap between low-level
reconstruction loss and high-level adversarial loss. To address this issue, we
introduce a perceptual network to provide mid-level guidance, which measures
the semantical similarity between the synthesized and original contents in a
similarity-enhanced space. We conduct a detailed analysis on the effects of
different losses and different levels of perceptual features in image
completion, showing that there exist complementarity between adversarial
training and perceptual features. By combining them together, our model can
achieve nearly seamless fusion results in an end-to-end manner. Moreover, we
design an effective lightweight generator architecture, which can achieve
effective image inpainting with far less parameters. Evaluated on CelebA Face
and Paris StreetView dataset, our proposed method significantly outperforms
existing methods.
Smartphone App Usage Prediction Using Points of Interest
Authors: Donghan Yu, Yong Li, Fengli Xu, Pengyu Zhang, Vassilis Kostakos
Date: 2017-11-26 00:00:00
Tasks: Prediction, Transfer Learning

In this paper we present the first population-level, city-scale analysis of
application usage on smartphones. Using deep packet inspection at the network
operator level, we obtained a geo-tagged dataset with more than 6 million
unique devices that launched more than 10,000 unique applications across the
city of Shanghai over one week. We develop a technique that leverages transfer
learning to predict which applications are most popular and estimate the whole
usage distribution based on the Point of Interest (POI) information of that
particular location. We demonstrate that our technique has an 83.0% hitrate in
successfully identifying the top five popular applications, and a 0.15 RMSE
when estimating usage with just 10% sampled sparse data. It outperforms by
about 25.7% over the existing state-of-the-art approaches. Our findings pave
the way for predicting which apps are relevant to a user given their current
location, and which applications are popular where. The implications of our
findings are broad: it enables a range of systems to benefit from such timely
predictions, including operating systems, network operators, appstores,
advertisers, and service providers.
In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks
Authors: Pramuditha Perera, Mahdi Abavisani, Vishal M. Patel
Date: 2017-11-26 00:00:00
Tasks: Generative Adversarial Network, Image-to-Image Translation, Multimodal Unsupervised Image-To-Image Translation, Translation, Unsupervised Image-To-Image Translation

In unsupervised image-to-image translation, the goal is to learn the mapping
between an input image and an output image using a set of unpaired training
images. In this paper, we propose an extension of the unsupervised
image-to-image translation problem to multiple input setting. Given a set of
paired images from multiple modalities, a transformation is learned to
translate the input into a specified domain. For this purpose, we introduce a
Generative Adversarial Network (GAN) based framework along with a multi-modal
generator structure and a new loss term, latent consistency loss. Through
various experiments we show that leveraging multiple inputs generally improves
the visual quality of the translated images. Moreover, we show that the
proposed method outperforms current state-of-the-art unsupervised
image-to-image translation methods.
The Vapnik-Chervonenkis dimension of cubes in $\mathbb{R}^d$
Authors: Christian J. J. Despres
Date: 2014-12-20 00:00:00
Tasks: BIG-bench Machine Learning

The Vapnik-Chervonenkis (VC) dimension of a collection of subsets of a set is
an important combinatorial concept in settings such as discrete geometry and
machine learning. In this paper we prove that the VC dimension of the family of
$d$-dimensional cubes in $\mathbb R^d$ is $\lfloor(3d+1)/2\rfloor$.
Unsupervised 3D Reconstruction from a Single Image via Adversarial Learning
Authors: Lingjing Wang, Yi Fang
Date: 2017-11-26 00:00:00
Tasks: 3D Reconstruction

Recent advancements in deep learning opened new opportunities for learning a
high-quality 3D model from a single 2D image given sufficient training on
large-scale data sets. However, the significant imbalance between available
amount of images and 3D models, and the limited availability of labeled 2D
image data (i.e. manually annotated pairs between images and their
corresponding 3D models), severely impacts the training of most supervised deep
learning methods in practice. In this paper, driven by a novel design of
adversarial networks, we have developed an unsupervised learning paradigm to
reconstruct 3D models from a single 2D image, which is free of manually
annotated pairwise input image and its associated 3D model. Particularly, the
paradigm begins with training an adaption network via autoencoder with
adversarial loss, which embeds unpaired 2D synthesized image domain with real
world image domain to a shared latent vector space. Then, we jointly train a 3D
deconvolutional network to transform the latent vector space to the 3D object
space together with the embedding process. Our experiments verify our network's
robust and superior performance in handling 3D volumetric object generation
from a single 2D image.
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Authors: Evan Racah, Christopher Beckham, Tegan Maharaj, Samira Ebrahimi Kahou, Prabhat, Christopher Pal
Date: 2016-12-07 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: BIG-bench Machine Learning, Blocking, Management

Then detection and identification of extreme weather events in large-scale
climate simulations is an important problem for risk management, informing
governmental policy decisions and advancing our basic understanding of the
climate system. Recent work has shown that fully supervised convolutional
neural networks (CNNs) can yield acceptable accuracy for classifying well-known
types of extreme weather events when large amounts of labeled data are
available. However, many different types of spatially localized climate
patterns are of interest including hurricanes, extra-tropical cyclones, weather
fronts, and blocking events among others. Existing labeled data for these
patterns can be incomplete in various ways, such as covering only certain years
or geographic areas and having false negatives. This type of climate data
therefore poses a number of interesting machine learning challenges. We present
a multichannel spatiotemporal CNN architecture for semi-supervised bounding box
prediction and exploratory data analysis. We demonstrate that our approach is
able to leverage temporal information and unlabeled data to improve the
localization of extreme weather events. Further, we explore the representations
learned by our model in order to better understand this important data. We
present a dataset, ExtremeWeather, to encourage machine learning research in
this area and to help facilitate further work in understanding and mitigating
the effects of climate change. The dataset is available at
extremeweatherdataset.github.io and the code is available at
https://github.com/eracah/hur-detect.
Inference of Spatio-Temporal Functions over Graphs via Multi-Kernel Kriged Kalman Filtering
Authors: Vassilis N. Ioannidis, Daniel Romero, Georgios B. Giannakis
Date: 2017-11-25 00:00:00

Inference of space-time varying signals on graphs emerges naturally in a
plethora of network science related applications. A frequently encountered
challenge pertains to reconstructing such dynamic processes, given their values
over a subset of vertices and time instants. The present paper develops a
graph-aware kernel-based kriged Kalman filter that accounts for the
spatio-temporal variations, and offers efficient online reconstruction, even
for dynamically evolving network topologies. The kernel-based learning
framework bypasses the need for statistical information by capitalizing on the
smoothness that graph signals exhibit with respect to the underlying graph. To
address the challenge of selecting the appropriate kernel, the proposed filter
is combined with a multi-kernel selection module. Such a data-driven method
selects a kernel attuned to the signal dynamics on-the-fly within the linear
span of a pre-selected dictionary. The novel multi-kernel learning algorithm
exploits the eigenstructure of Laplacian kernel matrices to reduce
computational complexity. Numerical tests with synthetic and real data
demonstrate the superior reconstruction performance of the novel approach
relative to state-of-the-art alternatives.
Learning Less-Overlapping Representations
Authors: Pengtao Xie, Hongbao Zhang, Eric P. Xing
Date: 2017-11-25 00:00:00
Proceeding: ICLR 2018 1
Tasks: Representation Learning

In representation learning (RL), how to make the learned representations easy
to interpret and less overfitted to training data are two important but
challenging issues. To address these problems, we study a new type of
regulariza- tion approach that encourages the supports of weight vectors in RL
models to have small overlap, by simultaneously promoting near-orthogonality
among vectors and sparsity of each vector. We apply the proposed regularizer to
two models: neural networks (NNs) and sparse coding (SC), and develop an
efficient ADMM-based algorithm for regu- larized SC. Experiments on various
datasets demonstrate that weight vectors learned under our regularizer are more
interpretable and have better generalization performance.
On the Automatic Generation of Medical Imaging Reports
Authors: Baoyu Jing, Pengtao Xie, Eric Xing
Date: 2017-11-22 00:00:00
Proceeding: ACL 2018 7
Tasks: Medical Report Generation, Multi-Task Learning

Medical imaging is widely used in clinical practice for diagnosis and
treatment. Report-writing can be error-prone for unexperienced physicians, and
time- consuming and tedious for experienced physicians. To address these
issues, we study the automatic generation of medical imaging reports. This task
presents several challenges. First, a complete report contains multiple
heterogeneous forms of information, including findings and tags. Second,
abnormal regions in medical images are difficult to identify. Third, the re-
ports are typically long, containing multiple sentences. To cope with these
challenges, we (1) build a multi-task learning framework which jointly performs
the pre- diction of tags and the generation of para- graphs, (2) propose a
co-attention mechanism to localize regions containing abnormalities and
generate narrations for them, (3) develop a hierarchical LSTM model to generate
long paragraphs. We demonstrate the effectiveness of the proposed methods on
two publicly available datasets.
An Adaptive Strategy for Active Learning with Smooth Decision Boundary
Authors: Andrea Locatelli, Alexandra Carpentier, Samory Kpotufe
Date: 2017-11-25 00:00:00
Tasks: Active Learning, General Classification

We present the first adaptive strategy for active learning in the setting of
classification with smooth decision boundary. The problem of adaptivity (to
unknown distributional parameters) has remained opened since the seminal work
of Castro and Nowak (2007), which first established (active learning) rates for
this setting. While some recent advances on this problem establish adaptive
rates in the case of univariate data, adaptivity in the more practical setting
of multivariate data has so far remained elusive. Combining insights from
various recent works, we show that, for the multivariate case, a careful
reduction to univariate-adaptive strategies yield near-optimal rates without
prior knowledge of distributional parameters.
Multiresolution and Hierarchical Analysis of Astronomical Spectroscopic Cubes using 3D Discrete Wavelet Transform
Authors: Martín Villanueva, Mauricio Araya
Date: 2017-11-17 00:00:00

The intrinsically hierarchical and blended structure of interstellar
molecular clouds, plus the always increasing resolution of astronomical
instruments, demand advanced and automated pattern recognition techniques for
identifying and connecting source components in spectroscopic cubes. We extend
the work done in multiresolution analysis using Wavelets for astronomical 2D
images to 3D spectroscopic cubes, combining the results with the Dendrograms
approach to offer a hierarchical representation of connections between sources
at different scale levels. We test our approach in real data from the ALMA
observatory, exploring different Wavelet families and assessing the main
parameter for source identification (i.e., RMS) at each level. Our approach
shows that is feasible to perform multiresolution analysis for the spatial and
frequency domains simultaneously rather than analyzing each spectral channel
independently.
Experiential, Distributional and Dependency-based Word Embeddings have Complementary Roles in Decoding Brain Activity
Authors: Samira Abnar, Rasyan Ahmed, Max Mijnheer, Willem Zuidema
Date: 2017-11-25 00:00:00
Proceeding: WS 2018 1
Tasks: Word Embeddings

We evaluate 8 different word embedding models on their usefulness for
predicting the neural activation patterns associated with concrete nouns. The
models we consider include an experiential model, based on crowd-sourced
association data, several popular neural and distributional models, and a model
that reflects the syntactic context of words (based on dependency parses). Our
goal is to assess the cognitive plausibility of these various embedding models,
and understand how we can further improve our methods for interpreting brain
imaging data.
  We show that neural word embedding models exhibit superior performance on the
tasks we consider, beating experiential word representation model. The
syntactically informed model gives the overall best performance when predicting
brain activation patterns from word embeddings; whereas the GloVe
distributional method gives the overall best performance when predicting in the
reverse direction (words vectors from brain images). Interestingly, however,
the error patterns of these different models are markedly different. This may
support the idea that the brain uses different systems for processing different
kinds of words. Moreover, we suggest that taking the relative strengths of
different embedding models into account will lead to better models of the brain
activity associated with words.
A Big Data Analysis Framework Using Apache Spark and Deep Learning
Authors: Anand Gupta, Hardeo Thakur, Ritvik Shrivastava, Pulkit Kumar, Sreyashi Nag
Date: 2017-11-25 00:00:00
Tasks: BIG-bench Machine Learning

With the spreading prevalence of Big Data, many advances have recently been
made in this field. Frameworks such as Apache Hadoop and Apache Spark have
gained a lot of traction over the past decades and have become massively
popular, especially in industries. It is becoming increasingly evident that
effective big data analysis is key to solving artificial intelligence problems.
Thus, a multi-algorithm library was implemented in the Spark framework, called
MLlib. While this library supports multiple machine learning algorithms, there
is still scope to use the Spark setup efficiently for highly time-intensive and
computationally expensive procedures like deep learning. In this paper, we
propose a novel framework that combines the distributive computational
abilities of Apache Spark and the advanced machine learning architecture of a
deep multi-layer perceptron (MLP), using the popular concept of Cascade
Learning. We conduct empirical analysis of our framework on two real world
datasets. The results are encouraging and corroborate our proposed framework,
in turn proving that it is an improvement over traditional big data analysis
methods that use either Spark or Deep learning as individual elements.
Few-Shot Image Recognition by Predicting Parameters from Activations
Authors: Siyuan Qiao, Chenxi Liu, Wei Shen, Alan Yuille
Date: 2017-06-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Few-Shot Image Classification, Few-Shot Learning

In this paper, we are interested in the few-shot learning problem. In
particular, we focus on a challenging scenario where the number of categories
is large and the number of examples per novel category is very limited, e.g. 1,
2, or 3. Motivated by the close relationship between the parameters and the
activations in a neural network associated with the same category, we propose a
novel method that can adapt a pre-trained neural network to novel categories by
directly predicting the parameters from the activations. Zero training is
required in adaptation to novel categories, and fast inference is realized by a
single forward pass. We evaluate our method by doing few-shot image recognition
on the ImageNet dataset, which achieves the state-of-the-art classification
accuracy on novel categories by a significant margin while keeping comparable
performance on the large-scale categories. We also test our method on the
MiniImageNet dataset and it strongly outperforms the previous state-of-the-art
methods.
Effective Use of Bidirectional Language Modeling for Transfer Learning in Biomedical Named Entity Recognition
Authors: Devendra Singh Sachan, Pengtao Xie, Mrinmaya Sachan, Eric P. Xing
Date: 2017-11-21 00:00:00
Tasks: Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning

Biomedical named entity recognition (NER) is a fundamental task in text
mining of medical documents and has many applications. Deep learning based
approaches to this task have been gaining increasing attention in recent years
as their parameters can be learned end-to-end without the need for
hand-engineered features. However, these approaches rely on high-quality
labeled data, which is expensive to obtain. To address this issue, we
investigate how to use unlabeled text data to improve the performance of NER
models. Specifically, we train a bidirectional language model (BiLM) on
unlabeled data and transfer its weights to "pretrain" an NER model with the
same architecture as the BiLM, which results in a better parameter
initialization of the NER model. We evaluate our approach on four benchmark
datasets for biomedical NER and show that it leads to a substantial improvement
in the F1 scores compared with the state-of-the-art approaches. We also show
that BiLM weight transfer leads to a faster model training and the pretrained
model requires fewer training examples to achieve a particular F1 score.