pwc-archive/papers-with-abstracts huggingface dataset:
Heinrich Behmann's Contributions to Second-Order Quantifier Elimination from the View of Computational Logic
Authors: Christoph Wernhard
Date: 2017-12-19 00:00:00

For relational monadic formulas (the L\"owenheim class) second-order
quantifier elimination, which is closely related to computation of uniform
interpolants, projection and forgetting - operations that currently receive
much attention in knowledge processing - always succeeds. The decidability
proof for this class by Heinrich Behmann from 1922 explicitly proceeds by
elimination with equivalence preserving formula rewriting. Here we reconstruct
the results from Behmann's publication in detail and discuss related issues
that are relevant in the context of modern approaches to second-order
quantifier elimination in computational logic. In addition, an extensive
documentation of the letters and manuscripts in Behmann's bequest that concern
second-order quantifier elimination is given, including a commented register
and English abstracts of the German sources with focus on technical material.
In the late 1920s Behmann attempted to develop an elimination-based decision
method for formulas with predicates whose arity is larger than one. His
manuscripts and the correspondence with Wilhelm Ackermann show technical
aspects that are still of interest today and give insight into the genesis of
Ackermann's landmark paper "Untersuchungen \"uber das Eliminationsproblem der
mathematischen Logik" from 1935, which laid the foundation of the two
prevailing modern approaches to second-order quantifier elimination.
Pattern recognition techniques for Boson Sampling validation
Authors: Iris Agresti, Niko Viggianiello, Fulvio Flamini, Nicolò Spagnolo, Andrea Crespi, Roberto Osellame, Nathan Wiebe, Fabio Sciarrino
Date: 2017-12-19 00:00:00
Tasks: Clustering

The difficulty of validating large-scale quantum devices, such as Boson Samplers, poses a major challenge for any research program that aims to show quantum advantages over classical hardware. To address this problem, we propose a novel data-driven approach wherein models are trained to identify common pathologies using unsupervised machine learning methods. We illustrate this idea by training a classifier that exploits K-means clustering to distinguish between Boson Samplers that use indistinguishable photons from those that do not. We train the model on numerical simulations of small-scale Boson Samplers and then validate the pattern recognition technique on larger numerical simulations as well as on photonic chips in both traditional Boson Sampling and scattershot experiments. The effectiveness of such method relies on particle-type-dependent internal correlations present in the output distributions. This approach performs substantially better on the test data than previous methods and underscores the ability to further generalize its operation beyond the scope of the examples that it was trained on.
Single Image Deraining using Scale-Aware Multi-Stage Recurrent Network
Authors: Ruoteng Li, Loong-Fah Cheong, Robby T. Tan
Date: 2017-12-19 00:00:00
Tasks: Rain Removal, Single Image Deraining

Given a single input rainy image, our goal is to visually remove rain streaks
and the veiling effect caused by scattering and transmission of rain streaks
and rain droplets. We are particularly concerned with heavy rain, where rain
streaks of various sizes and directions can overlap each other and the veiling
effect reduces contrast severely. To achieve our goal, we introduce a
scale-aware multi-stage convolutional neural network. Our main idea here is
that different sizes of rain-streaks visually degrade the scene in different
ways. Large nearby streaks obstruct larger regions and are likely to reflect
specular highlights more prominently than smaller distant streaks. These
different effects of different streaks have their own characteristics in their
image features, and thus need to be treated differently. To realize this, we
create parallel sub-networks that are trained and made aware of these different
scales of rain streaks. To our knowledge, this idea of parallel sub-networks
that treats the same class of objects according to their unique sub-classes is
novel, particularly in the context of rain removal. To verify our idea, we
conducted experiments on both synthetic and real images, and found that our
method is effective and outperforms the state-of-the-art methods.
How consistent is my model with the data? Information-Theoretic Model Check
Authors: Andreas Svensson, Dave Zachariah, Thomas B. Schön
Date: 2017-12-07 00:00:00
Tasks: model

The choice of model class is fundamental in statistical learning and system
identification, no matter whether the class is derived from physical principles
or is a generic black-box. We develop a method to evaluate the specified model
class by assessing its capability of reproducing data that is similar to the
observed data record. This model check is based on the information-theoretic
properties of models viewed as data generators and is applicable to e.g.
sequential data and nonlinear dynamical models. The method can be understood as
a specific two-sided posterior predictive test. We apply the
information-theoretic model check to both synthetic and real data and compare
it with a classical whiteness test.
Hierarchical Cross Network for Person Re-identification
Authors: Huan-Cheng Hsu, Ching-Hang Chen, Hsiao-Rong Tyan, Hong-Yuan Mark Liao
Date: 2017-12-19 00:00:00
Tasks: Person Re-Identification, Retrieval

Person re-identification (person re-ID) aims at matching target person(s)
grabbed from different and non-overlapping camera views. It plays an important
role for public safety and has application in various tasks such as, human
retrieval, human tracking, and activity analysis. In this paper, we propose a
new network architecture called Hierarchical Cross Network (HCN) to perform
person re-ID. In addition to the backbone model of a conventional CNN, HCN is
equipped with two additional maps called hierarchical cross feature maps. The
maps of an HCN are formed by merging layers with different resolutions and
semantic levels. With the hierarchical cross feature maps, an HCN can
effectively uncover additional semantic features which could not be discovered
by a conventional CNN. Although the proposed HCN can discover features with
higher semantics, its representation power is still limited. To derive more
general representations, we augment the data during the training process by
combining multiple datasets. Experiment results show that the proposed method
outperformed several state-of-the-art methods.
Robust Piecewise-Constant Smoothing: M-Smoother Revisited
Authors: Linchao Bao, Qingxiong Yang
Date: 2014-10-28 00:00:00
Tasks: Denoising

A robust estimator, namely M-smoother, for piecewise-constant smoothing is
revisited in this paper. Starting from its generalized formulation, we propose
a numerical scheme/framework for solving it via a series of weighted-average
filtering (e.g., box filtering, Gaussian filtering, bilateral filtering, and
guided filtering). Because of the equivalence between M-smoother and
local-histogram-based filters (such as median filter and mode filter), the
proposed framework enables fast approximation of histogram filters via a number
of box filtering or Gaussian filtering. In addition, high-quality
piecewise-constant smoothing can be achieved via a number of bilateral
filtering or guided filtering integrated in the proposed framework. Experiments
on depth map denoising show the effectiveness of our framework.
Two-dimensional Anti-jamming Mobile Communication Based on Reinforcement Learning
Authors: Liang Xiao, Guoan Han, Donghua Jiang, Hongzi Zhu, Yanyong Zhang, H. Vincent Poor
Date: 2017-12-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Vocal Bursts Valence Prediction

By using smart radio devices, a jammer can dynamically change its jamming
policy based on opposing security mechanisms; it can even induce the mobile
device to enter a specific communication mode and then launch the jamming
policy accordingly. On the other hand, mobile devices can exploit spread
spectrum and user mobility to address both jamming and interference. In this
paper, a two-dimensional anti-jamming mobile communication scheme is proposed
in which a mobile device leaves a heavily jammed/interfered-with frequency or
area. It is shown that, by applying reinforcement learning techniques, a mobile
device can achieve an optimal communication policy without the need to know the
jamming and interference model and the radio channel model in a dynamic game
framework. More specifically, a hotbooting deep Q-network based two-dimensional
mobile communication scheme is proposed that exploits experiences in similar
scenarios to reduce the exploration time at the beginning of the game, and
applies deep convolutional neural network and macro-action techniques to
accelerate the learning speed in dynamic situations. Several real-world
scenarios are simulated to evaluate the proposed method. These simulation
results show that our proposed scheme can improve both the
signal-to-interference-plus-noise ratio of the signals and the utility of the
mobile devices against cooperative jamming compared with benchmark schemes.
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations
Authors: Upol Ehsan, Brent Harrison, Larry Chan, Mark O. Riedl
Date: 2017-02-25 00:00:00
Tasks: Explanation Generation, Machine Translation, Translation

We introduce AI rationalization, an approach for generating explanations of
autonomous system behavior as if a human had performed the behavior. We
describe a rationalization technique that uses neural machine translation to
translate internal state-action representations of an autonomous agent into
natural language. We evaluate our technique in the Frogger game environment,
training an autonomous game playing agent to rationalize its action choices
using natural language. A natural language training corpus is collected from
human players thinking out loud as they play the game. We motivate the use of
rationalization as an approach to explanation generation and show the results
of two experiments evaluating the effectiveness of rationalization. Results of
these evaluations show that neural machine translation is able to accurately
generate rationalizations that describe agent behavior, and that
rationalizations are more satisfying to humans than other alternative methods
of explanation.
Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies
Authors: Fangyi Zhang, Jürgen Leitner, Michael Milford, Peter Corke
Date: 2016-10-21 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

While deep learning has had significant successes in computer vision thanks
to the abundance of visual data, collecting sufficiently large real-world
datasets for robot learning can be costly. To increase the practicality of
these techniques on real robots, we propose a modular deep reinforcement
learning method capable of transferring models trained in simulation to a
real-world robotic task. We introduce a bottleneck between perception and
control, enabling the networks to be trained independently, but then merged and
fine-tuned in an end-to-end manner to further improve hand-eye coordination. On
a canonical, planar visually-guided robot reaching task a fine-tuned accuracy
of 1.6 pixels is achieved, a significant improvement over naive transfer (17.5
pixels), showing the potential for more complicated and broader applications.
Our method provides a technique for more efficient learning and transfer of
visuo-motor policies for real robotic systems without relying entirely on large
real-world robot datasets.
Tracking objects using 3D object proposals
Authors: Ramanpreet Singh Pahwa, Tian Tsong Ng, Minh N. Do
Date: 2017-12-19 00:00:00
Tasks: Computational Efficiency, CPU, Object, object-detection, Object Detection

3D object proposals, quickly detected regions in a 3D scene that likely
contain an object of interest, are an effective approach to improve the
computational efficiency and accuracy of the object detection framework. In
this work, we propose a novel online method that uses our previously developed
3D object proposals, in a RGB-D video sequence, to match and track static
objects in the scene using shape matching. Our main observation is that depth
images provide important information about the geometry of the scene that is
often ignored in object matching techniques. Our method takes less than a
second in MATLAB on the UW-RGBD scene dataset on a single thread CPU and thus,
has potential to be used in low-power chips in Unmanned Aerial Vehicles (UAVs),
quadcopters, and drones.
Genetic Algorithm with Optimal Recombination for the Asymmetric Travelling Salesman Problem
Authors: A. V. Eremeev, Yu. V. Kovalenko
Date: 2017-06-21 00:00:00

We propose a new genetic algorithm with optimal recombination for the
asymmetric instances of travelling salesman problem. The algorithm incorporates
several new features that contribute to its effectiveness: (i) Optimal
recombination problem is solved within crossover operator. (ii) A new mutation
operator performs a random jump within 3-opt or 4-opt neighborhood. (iii)
Greedy constructive heuristic of W.Zhang and 3-opt local search heuristic are
used to generate the initial population. A computational experiment on TSPLIB
instances shows that the proposed algorithm yields competitive results to other
well-known memetic algorithms for asymmetric travelling salesman problem.
A Geometric View of Optimal Transportation and Generative Model
Authors: Na Lei, Kehua Su, Li Cui, Shing-Tung Yau, David Xianfeng Gu
Date: 2017-10-16 00:00:00

In this work, we show the intrinsic relations between optimal transportation
and convex geometry, especially the variational approach to solve Alexandrov
problem: constructing a convex polytope with prescribed face normals and
volumes. This leads to a geometric interpretation to generative models, and
leads to a novel framework for generative models. By using the optimal
transportation view of GAN model, we show that the discriminator computes the
Kantorovich potential, the generator calculates the transportation map. For a
large class of transportation costs, the Kantorovich potential can give the
optimal transportation map by a close-form formula. Therefore, it is sufficient
to solely optimize the discriminator. This shows the adversarial competition
can be avoided, and the computational architecture can be simplified.
Preliminary experimental results show the geometric method outperforms WGAN for
approximating probability measures with multiple clusters in low dimensional
space.
PixelBNN: Augmenting the PixelCNN with batch normalization and the presentation of a fast architecture for retinal vessel segmentation
Authors: Henry A. Leopold, Jeff Orchard, John S. Zelek, Vasudevan Lakshminarayanan
Date: 2017-12-19 00:00:00
Tasks: Image Registration, Retinal Vessel Segmentation, Segmentation

Analysis of retinal fundus images is essential for eye-care physicians in the
diagnosis, care and treatment of patients. Accurate fundus and/or retinal
vessel maps give rise to longitudinal studies able to utilize multimedia image
registration and disease/condition status measurements, as well as applications
in surgery preparation and biometrics. The segmentation of retinal morphology
has numerous applications in assessing ophthalmologic and cardiovascular
disease pathologies. The early detection of many such conditions is often the
most effective method for reducing patient risk. Computer aided segmentation of
the vasculature has proven to be a challenge, mainly due to inconsistencies
such as noise and variations in hue and brightness that can greatly reduce the
quality of fundus images. This paper presents PixelBNN, a highly efficient deep
method for automating the segmentation of fundus morphologies. The model was
trained, tested and cross tested on the DRIVE, STARE and CHASE\_DB1 retinal
vessel segmentation datasets. Performance was evaluated using G-mean, Mathews
Correlation Coefficient and F1-score. The network was 8.5 times faster than the
current state-of-the-art at test time and performed comparatively well,
considering a 5 to 19 times reduction in information from resizing images
during preprocessing.
Deformable Classifiers
Authors: Jiajun Shen, Yali Amit
Date: 2017-12-18 00:00:00
Tasks: Object, Object Recognition, Rotated MNIST

Geometric variations of objects, which do not modify the object class, pose a
major challenge for object recognition. These variations could be rigid as well
as non-rigid transformations. In this paper, we design a framework for training
deformable classifiers, where latent transformation variables are introduced,
and a transformation of the object image to a reference instantiation is
computed in terms of the classifier output, separately for each class. The
classifier outputs for each class, after transformation, are compared to yield
the final decision. As a by-product of the classification this yields a
transformation of the input object to a reference pose, which can be used for
downstream tasks such as the computation of object support. We apply a two-step
training mechanism for our framework, which alternates between optimizing over
the latent transformation variables and the classifier parameters to minimize
the loss function. We show that multilayer perceptrons, also known as deep
networks, are well suited for this approach and achieve state of the art
results on the rotated MNIST and the Google Earth dataset, and produce
competitive results on MNIST and CIFAR-10 when training on smaller subsets of
training data.
Multilingual Topic Models
Authors: Kriste Krstovski, Michael J. Kurtz, David A. Smith, Alberto Accomazzi
Date: 2017-12-18 00:00:00
Tasks: Articles, Topic Models

Scientific publications have evolved several features for mitigating
vocabulary mismatch when indexing, retrieving, and computing similarity between
articles. These mitigation strategies range from simply focusing on high-value
article sections, such as titles and abstracts, to assigning keywords, often
from controlled vocabularies, either manually or through automatic annotation.
Various document representation schemes possess different cost-benefit
tradeoffs. In this paper, we propose to model different representations of the
same article as translations of each other, all generated from a common latent
representation in a multilingual topic model. We start with a methodological
overview on latent variable models for parallel document representations that
could be used across many information science tasks. We then show how solving
the inference problem of mapping diverse representations into a shared topic
space allows us to evaluate representations based on how topically similar they
are to the original article. In addition, our proposed approach provides means
to discover where different concept vocabularies require improvement.
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer
Authors: Yuntao Chen, Naiyan Wang, Zhao-Xiang Zhang
Date: 2017-07-05 00:00:00
Tasks: Clustering, Image Clustering, Image Retrieval, Learning-To-Rank, Metric Learning, Model Compression, Retrieval

We have witnessed rapid evolution of deep neural network architecture design
in the past years. These latest progresses greatly facilitate the developments
in various areas such as computer vision and natural language processing.
However, along with the extraordinary performance, these state-of-the-art
models also bring in expensive computational cost. Directly deploying these
models into applications with real-time requirement is still infeasible.
Recently, Hinton etal. have shown that the dark knowledge within a powerful
teacher model can significantly help the training of a smaller and faster
student network. These knowledge are vastly beneficial to improve the
generalization ability of the student model. Inspired by their work, we
introduce a new type of knowledge -- cross sample similarities for model
compression and acceleration. This knowledge can be naturally derived from deep
metric learning model. To transfer them, we bring the "learning to rank"
technique into deep metric learning formulation. We test our proposed DarkRank
method on various metric learning tasks including pedestrian re-identification,
image retrieval and image clustering. The results are quite encouraging. Our
method can improve over the baseline method by a large margin. Moreover, it is
fully compatible with other existing methods. When combined, the performance
can be further boosted.
Data-Driven Sparse Structure Selection for Deep Neural Networks
Authors: Zehao Huang, Naiyan Wang
Date: 2017-07-05 00:00:00
Proceeding: ECCV 2018 9

Deep convolutional neural networks have liberated its extraordinary power on
various tasks. However, it is still very challenging to deploy state-of-the-art
models into real-world applications due to their high computational complexity.
How can we design a compact and effective network without massive experiments
and expert knowledge? In this paper, we propose a simple and effective
framework to learn and prune deep models in an end-to-end manner. In our
framework, a new type of parameter -- scaling factor is first introduced to
scale the outputs of specific structures, such as neurons, groups or residual
blocks. Then we add sparsity regularizations on these factors, and solve this
optimization problem by a modified stochastic Accelerated Proximal Gradient
(APG) method. By forcing some of the factors to zero, we can safely remove the
corresponding structures, thus prune the unimportant parts of a CNN. Comparing
with other structure selection methods that may need thousands of trials or
iterative fine-tuning, our method is trained fully end-to-end in one training
pass without bells and whistles. We evaluate our method, Sparse Structure
Selection with several state-of-the-art CNNs, and demonstrate very promising
results with adaptive depth and width selection.
Like What You Like: Knowledge Distill via Neuron Selectivity Transfer
Authors: Zehao Huang, Naiyan Wang
Date: 2017-07-05 00:00:00
Proceeding: ICLR 2019 5
Tasks: object-detection, Object Detection, Transfer Learning

Despite deep neural networks have demonstrated extraordinary power in various
applications, their superior performances are at expense of high storage and
computational costs. Consequently, the acceleration and compression of neural
networks have attracted much attention recently. Knowledge Transfer (KT), which
aims at training a smaller student network by transferring knowledge from a
larger teacher model, is one of the popular solutions. In this paper, we
propose a novel knowledge transfer method by treating it as a distribution
matching problem. Particularly, we match the distributions of neuron
selectivity patterns between teacher and student networks. To achieve this
goal, we devise a new KT loss function by minimizing the Maximum Mean
Discrepancy (MMD) metric between these distributions. Combined with the
original loss function, our method can significantly improve the performance of
student networks. We validate the effectiveness of our method across several
datasets, and further combine it with other KT methods to explore the best
possible results. Last but not least, we fine-tune the model to other tasks
such as object detection. The results are also encouraging, which confirm the
transferability of the learned features.