pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Generalized Method of Moments
Authors: Greg Lewis, Vasilis Syrgkanis
Date: 2018-03-19 00:00:00
Tasks: Causal Inference, Clustering

We provide an approach for learning deep neural net representations of models
described via conditional moment restrictions. Conditional moment restrictions
are widely used, as they are the language by which social scientists describe
the assumptions they make to enable causal inference. We formulate the problem
of estimating the underling model as a zero-sum game between a modeler and an
adversary and apply adversarial training. Our approach is similar in nature to
Generative Adversarial Networks (GAN), though here the modeler is learning a
representation of a function that satisfies a continuum of moment conditions
and the adversary is identifying violating moments. We outline ways of
constructing effective adversaries in practice, including kernels centered by
k-means clustering, and random forests. We examine the practical performance of
our approach in the setting of non-parametric instrumental variable regression.
Towards a Neural Network Approach to Abstractive Multi-Document Summarization
Authors: Jianmin Zhang, Jiwei Tan, Xiaojun Wan
Date: 2018-04-24 00:00:00
Tasks: Abstractive Text Summarization, Document Summarization, Multi-Document Summarization

Till now, neural abstractive summarization methods have achieved great
success for single document summarization (SDS). However, due to the lack of
large scale multi-document summaries, such methods can be hardly applied to
multi-document summarization (MDS). In this paper, we investigate neural
abstractive methods for MDS by adapting a state-of-the-art neural abstractive
summarization model for SDS. We propose an approach to extend the neural
abstractive model trained on large scale SDS data to the MDS task. Our approach
only makes use of a small number of multi-document summaries for fine tuning.
Experimental results on two benchmark DUC datasets demonstrate that our
approach can outperform a variety of baseline neural models.
Generative Model with Coordinate Metric Learning for Object Recognition Based on 3D Models
Authors: Yida Wang, Weihong Deng
Date: 2017-05-24 00:00:00
Tasks: Bayesian Inference, Metric Learning, Object, Object Recognition, Object Reconstruction, Triplet

Given large amount of real photos for training, Convolutional neural network
shows excellent performance on object recognition tasks. However, the process
of collecting data is so tedious and the background are also limited which
makes it hard to establish a perfect database. In this paper, our generative
model trained with synthetic images rendered from 3D models reduces the
workload of data collection and limitation of conditions. Our structure is
composed of two sub-networks: semantic foreground object reconstruction network
based on Bayesian inference and classification network based on multi-triplet
cost function for avoiding over-fitting problem on monotone surface and fully
utilizing pose information by establishing sphere-like distribution of
descriptors in each category which is helpful for recognition on regular photos
according to poses, lighting condition, background and category information of
rendered images. Firstly, our conjugate structure called generative model with
metric learning utilizing additional foreground object channels generated from
Bayesian rendering as the joint of two sub-networks. Multi-triplet cost
function based on poses for object recognition are used for metric learning
which makes it possible training a category classifier purely based on
synthetic data. Secondly, we design a coordinate training strategy with the
help of adaptive noises acting as corruption on input images to help both
sub-networks benefit from each other and avoid inharmonious parameter tuning
due to different convergence speed of two sub-networks. Our structure achieves
the state of the art accuracy of over 50\% on ShapeNet database with data
migration obstacle from synthetic images to real photos. This pipeline makes it
applicable to do recognition on real images only based on 3D models.
An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches
Authors: Zhuoyao Zhong, Lei Sun, Qiang Huo
Date: 2018-04-24 00:00:00
Tasks: Region Proposal, Scene Text Detection, Text Detection

The anchor mechanism of Faster R-CNN and SSD framework is considered not
effective enough to scene text detection, which can be attributed to its IoU
based matching criterion between anchors and ground-truth boxes. In order to
better enclose scene text instances of various shapes, it requires to design
anchors of various scales, aspect ratios and even orientations manually, which
makes anchor-based methods sophisticated and inefficient. In this paper, we
propose a novel anchor-free region proposal network (AF-RPN) to replace the
original anchor-based RPN in the Faster R-CNN framework to address the above
problem. Compared with a vanilla RPN and FPN-RPN, AF-RPN can get rid of
complicated anchor design and achieve higher recall rate on large-scale
COCO-Text dataset. Owing to the high-quality text proposals, our Faster R-CNN
based two-stage text detection approach achieves state-of-the-art results on
ICDAR-2017 MLT, ICDAR-2015 and ICDAR-2013 text detection benchmarks when using
single-scale and single-model (ResNet50) testing only.
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
Authors: Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, Kun Gai
Date: 2018-04-21 00:00:00
Tasks: Click-Through Rate Prediction, Recommendation Systems, Selection bias, Transfer Learning

Estimating post-click conversion rate (CVR) accurately is crucial for ranking
systems in industrial applications such as recommendation and advertising.
Conventional CVR modeling applies popular deep learning methods and achieves
state-of-the-art performance. However it encounters several task-specific
problems in practice, making CVR modeling challenging. For example,
conventional CVR models are trained with samples of clicked impressions while
utilized to make inference on the entire space with samples of all impressions.
This causes a sample selection bias problem. Besides, there exists an extreme
data sparsity problem, making the model fitting rather difficult. In this
paper, we model CVR in a brand-new perspective by making good use of sequential
pattern of user actions, i.e., impression -> click -> conversion. The proposed
Entire Space Multi-task Model (ESMM) can eliminate the two problems
simultaneously by i) modeling CVR directly over the entire space, ii) employing
a feature representation transfer learning strategy. Experiments on dataset
gathered from Taobao's recommender system demonstrate that ESMM significantly
outperforms competitive methods. We also release a sampling version of this
dataset to enable future research. To the best of our knowledge, this is the
first public dataset which contains samples with sequential dependence of click
and conversion labels for CVR modeling.
Learning Spatial-Aware Regressions for Visual Tracking
Authors: Chong Sun, Dong Wang, Huchuan Lu, Ming-Hsuan Yang
Date: 2017-06-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: regression, Visual Object Tracking, Visual Tracking

In this paper, we analyze the spatial information of deep features, and
propose two complementary regressions for robust visual tracking. First, we
propose a kernelized ridge regression model wherein the kernel value is defined
as the weighted sum of similarity scores of all pairs of patches between two
samples. We show that this model can be formulated as a neural network and thus
can be efficiently solved. Second, we propose a fully convolutional neural
network with spatially regularized kernels, through which the filter kernel
corresponding to each output channel is forced to focus on a specific region of
the target. Distance transform pooling is further exploited to determine the
effectiveness of each output channel of the convolution layer. The outputs from
the kernelized ridge regression model and the fully convolutional neural
network are combined to obtain the ultimate response. Experimental results on
two benchmark datasets validate the effectiveness of the proposed method.
Learn and Pick Right Nodes to Offload
Authors: Zhaowei Zhu, Ting Liu, Shengda Jin, Xiliang Luo
Date: 2018-04-20 00:00:00

Task offloading is a promising technology to exploit the benefits of fog
computing. An effective task offloading strategy is needed to utilize the
computational resources efficiently. In this paper, we endeavor to seek an
online task offloading strategy to minimize the long-term latency. In
particular, we formulate a stochastic programming problem, where the
expectations of the system parameters change abruptly at unknown time instants.
Meanwhile, we consider the fact that the queried nodes can only feed back the
processing results after finishing the tasks. We then put forward an effective
algorithm to solve this challenging stochastic programming under the
non-stationary bandit model. We further prove that our proposed algorithm is
asymptotically optimal in a non-stationary fog-enabled network. Numerical
simulations are carried out to corroborate our designs.
Correlation Tracking via Joint Discrimination and Reliability Learning
Authors: Chong Sun, Dong Wang, Huchuan Lu, Ming-Hsuan Yang
Date: 2018-04-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Visual Tracking

For visual tracking, an ideal filter learned by the correlation filter (CF)
method should take both discrimination and reliability information. However,
existing attempts usually focus on the former one while pay less attention to
reliability learning. This may make the learned filter be dominated by the
unexpected salient regions on the feature map, thereby resulting in model
degradation. To address this issue, we propose a novel CF-based optimization
problem to jointly model the discrimination and reliability information. First,
we treat the filter as the element-wise product of a base filter and a
reliability term. The base filter is aimed to learn the discrimination
information between the target and backgrounds, and the reliability term
encourages the final filter to focus on more reliable regions. Second, we
introduce a local response consistency regular term to emphasize equal
contributions of different regions and avoid the tracker being dominated by
unreliable regions. The proposed optimization problem can be solved using the
alternating direction method and speeded up in the Fourier domain. We conduct
extensive experiments on the OTB-2013, OTB-2015 and VOT-2016 datasets to
evaluate the proposed tracker. Experimental results show that our tracker
performs favorably against other state-of-the-art trackers.
Faster quantum mixing for slowly evolving sequences of Markov chains
Authors: Davide Orsucci, Hans J. Briegel, Vedran Dunjko
Date: 2015-03-04 00:00:00
Tasks: BIG-bench Machine Learning, Combinatorial Optimization

Markov chain methods are remarkably successful in computational physics,
machine learning, and combinatorial optimization. The cost of such methods
often reduces to the mixing time, i.e., the time required to reach the steady
state of the Markov chain, which scales as $\delta^{-1}$, the inverse of the
spectral gap. It has long been conjectured that quantum computers offer nearly
generic quadratic improvements for mixing problems. However, except in special
cases, quantum algorithms achieve a run-time of $\mathcal{O}(\sqrt{\delta^{-1}}
\sqrt{N})$, which introduces a costly dependence on the Markov chain size $N,$
not present in the classical case. Here, we re-address the problem of mixing of
Markov chains when these form a slowly evolving sequence. This setting is akin
to the simulated annealing setting and is commonly encountered in physics,
material sciences and machine learning. We provide a quantum memory-efficient
algorithm with a run-time of $\mathcal{O}(\sqrt{\delta^{-1}} \sqrt[4]{N})$,
neglecting logarithmic terms, which is an important improvement for large state
spaces. Moreover, our algorithms output quantum encodings of distributions,
which has advantages over classical outputs. Finally, we discuss the run-time
bounds of mixing algorithms and show that, under certain assumptions, our
algorithms are optimal.
DenseFuse: A Fusion Approach to Infrared and Visible Images
Authors: Hui Li, Xiao-Jun Wu
Date: 2018-04-23 00:00:00
Tasks: Decoder

In this paper, we present a novel deep learning architecture for infrared and
visible images fusion problem. In contrast to conventional convolutional
networks, our encoding network is combined by convolutional layers, fusion
layer and dense block in which the output of each layer is connected to every
other layer. We attempt to use this architecture to get more useful features
from source images in encoding process. And two fusion layers(fusion
strategies) are designed to fuse these features. Finally, the fused image is
reconstructed by decoder. Compared with existing fusion methods, the proposed
fusion method achieves state-of-the-art performance in objective and subjective
assessment. Code and pre-trained models are available at
https://github.com/hli1221/imagefusion_densefuse
Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model
Authors: Kyongsik Yun, Joseph Osborne, Madison Lee, Thomas Lu, Edward Chow
Date: 2018-04-24 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Transcribing voice communications in NASA's launch control center is
important for information utilization. However, automatic speech recognition in
this environment is particularly challenging due to the lack of training data,
unfamiliar words in acronyms, multiple different speakers and accents, and
conversational characteristics of speaking. We used bidirectional deep
recurrent neural networks to train and test speech recognition performance. We
showed that data augmentation and custom language models can improve speech
recognition accuracy. Transcribing communications from the launch control
center will help the machine analyze information and accelerate knowledge
generation.
Compositional Attention Networks for Machine Reasoning
Authors: Drew A. Hudson, Christopher D. Manning
Date: 2018-03-08 00:00:00
Proceeding: ICLR 2018 1
Tasks: Referring Expression Comprehension, Visual Question Answering (VQA), Visual Reasoning

We present the MAC network, a novel fully differentiable neural network
architecture, designed to facilitate explicit and expressive reasoning. MAC
moves away from monolithic black-box neural architectures towards a design that
encourages both transparency and versatility. The model approaches problems by
decomposing them into a series of attention-based reasoning steps, each
performed by a novel recurrent Memory, Attention, and Composition (MAC) cell
that maintains a separation between control and memory. By stringing the cells
together and imposing structural constraints that regulate their interaction,
MAC effectively learns to perform iterative reasoning processes that are
directly inferred from the data in an end-to-end approach. We demonstrate the
model's strength, robustness and interpretability on the challenging CLEVR
dataset for visual reasoning, achieving a new state-of-the-art 98.9% accuracy,
halving the error rate of the previous best model. More importantly, we show
that the model is computationally-efficient and data-efficient, in particular
requiring 5x less data than existing models to achieve strong results.
How swarm size during evolution impacts the behavior, generalizability, and brain complexity of animats performing a spatial navigation task
Authors: Dominik Fischer, Sanaz Mostaghim, Larissa Albantakis
Date: 2018-04-24 00:00:00

While it is relatively easy to imitate and evolve natural swarm behavior in
simulations, less is known about the social characteristics of simulated,
evolved swarms, such as the optimal (evolutionary) group size, why individuals
in a swarm perform certain actions, and how behavior would change in swarms of
different sizes. To address these questions, we used a genetic algorithm to
evolve animats equipped with Markov Brains in a spatial navigation task that
facilitates swarm behavior. The animats' goal was to frequently cross between
two rooms without colliding with other animats. Animats were evolved in swarms
of various sizes. We then evaluated the task performance and social behavior of
the final generation from each evolution when placed with swarms of different
sizes in order to evaluate their generalizability across conditions. According
to our experiments, we find that swarm size during evolution matters: animats
evolved in a balanced swarm developed more flexible behavior, higher fitness
across conditions, and, in addition, higher brain complexity.
Fusion of Head and Full-Body Detectors for Multi-Object Tracking
Authors: Roberto Henschel, Laura Leal-Taixé, Daniel Cremers, Bodo Rosenhahn
Date: 2017-05-23 00:00:00
Tasks: Multi-Object Tracking, Object Tracking

In order to track all persons in a scene, the tracking-by-detection paradigm
has proven to be a very effective approach. Yet, relying solely on a single
detector is also a major limitation, as useful image information might be
ignored. Consequently, this work demonstrates how to fuse two detectors into a
tracking system. To obtain the trajectories, we propose to formulate tracking
as a weighted graph labeling problem, resulting in a binary quadratic program.
As such problems are NP-hard, the solution can only be approximated. Based on
the Frank-Wolfe algorithm, we present a new solver that is crucial to handle
such difficult problems. Evaluation on pedestrian tracking is provided for
multiple scenarios, showing superior results over single detector tracking and
standard QP-solvers. Finally, our tracker ranks 2nd on the MOT16 benchmark and
1st on the new MOT17 benchmark, outperforming over 90 trackers.
Scheduled Multi-Task Learning: From Syntax to Translation
Authors: Eliyahu Kiperwasser, Miguel Ballesteros
Date: 2018-04-24 00:00:00
Proceeding: TACL 2018 1
Tasks: Decoder, Machine Translation, Multi-Task Learning, Translation

Neural encoder-decoder models of machine translation have achieved impressive
results, while learning linguistic knowledge of both the source and target
languages in an implicit end-to-end manner. We propose a framework in which our
model begins learning syntax and translation interleaved, gradually putting
more focus on translation. Using this approach, we achieve considerable
improvements in terms of BLEU score on relatively large parallel corpus (WMT14
English to German) and a low-resource (WIT German to English) setup.
Accurate 3-D Reconstruction with RGB-D Cameras using Depth Map Fusion and Pose Refinement
Authors: Markus Ylimäki, Juho Kannala, Janne Heikkilä
Date: 2018-04-24 00:00:00

Depth map fusion is an essential part in both stereo and RGB-D based 3-D
reconstruction pipelines. Whether produced with a passive stereo reconstruction
or using an active depth sensor, such as Microsoft Kinect, the depth maps have
noise and may have poor initial registration. In this paper, we introduce a
method which is capable of handling outliers, and especially, even significant
registration errors. The proposed method first fuses a sequence of depth maps
into a single non-redundant point cloud so that the redundant points are merged
together by giving more weight to more certain measurements. Then, the original
depth maps are re-registered to the fused point cloud to refine the original
camera extrinsic parameters. The fusion is then performed again with the
refined extrinsic parameters. This procedure is repeated until the result is
satisfying or no significant changes happen between iterations. The method is
robust to outliers and erroneous depth measurements as well as even significant
depth map registration errors due to inaccurate initial camera poses.
Learning Software Constraints via Installation Attempts
Authors: Ran Ben Basat, Maayan Goldstein, Itai Segall
Date: 2018-04-24 00:00:00

Modern software systems are expected to be secure and contain all the latest
features, even when new versions of software are released multiple times an
hour. Each system may include many interacting packages. The problem of
installing multiple dependent packages has been extensively studied in the
past, yielding some promising solutions that work well in practice. However,
these assume that the developers declare all the dependencies and conflicts
between the packages. Oftentimes, the entire repository structure may not be
known upfront, for example when packages are developed by different vendors. In
this paper, we present algorithms for learning dependencies, conflicts and
defective packages from installation attempts. Our algorithms use combinatorial
data structures to generate queries that test installations and discover the
entire dependency structure. A query that the algorithms make corresponds to
trying to install a subset of packages and getting a Boolean feedback on
whether all constraints were satisfied in this subset. Our goal is to minimize
the query complexity of the algorithms. We prove lower and upper bounds on the
number of queries that these algorithms require to make for different settings
of the problem.
Residual Gated Graph ConvNets
Authors: Xavier Bresson, Thomas Laurent
Date: 2017-11-20 00:00:00
Proceeding: ICLR 2018 1
Tasks: Clustering, General Classification, Graph Classification, Graph Clustering, Graph Learning, Graph Regression, Node Classification

Graph-structured data such as social networks, functional brain networks,
gene regulatory networks, communications networks have brought the interest in
generalizing deep learning techniques to graph domains. In this paper, we are
interested to design neural networks for graphs with variable length in order
to solve learning problems such as vertex classification, graph classification,
graph regression, and graph generative tasks. Most existing works have focused
on recurrent neural networks (RNNs) to learn meaningful representations of
graphs, and more recently new convolutional neural networks (ConvNets) have
been introduced. In this work, we want to compare rigorously these two
fundamental families of architectures to solve graph learning tasks. We review
existing graph RNN and ConvNet architectures, and propose natural extension of
LSTM and ConvNet to graphs with arbitrary size. Then, we design a set of
analytically controlled experiments on two basic graph problems, i.e. subgraph
matching and graph clustering, to test the different architectures. Numerical
results show that the proposed graph ConvNets are 3-17% more accurate and
1.5-4x faster than graph RNNs. Graph ConvNets are also 36% more accurate than
variational (non-learning) techniques. Finally, the most effective graph
ConvNet architecture uses gated edges and residuality. Residuality plays an
essential role to learn multi-layer architectures as they provide a 10% gain of
performance.