pwc-archive/papers-with-abstracts huggingface dataset:
Matching Code and Law: Achieving Algorithmic Fairness with Optimal Transport
Authors: Meike Zehlike, Philipp Hacker, Emil Wiedemann
Date: 2017-12-21 00:00:00
Tasks: Fairness

Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the Continuous Fairness Algorithm (CFA$\theta$) which enables a continuous interpolation between different fairness definitions. More specifically, we make three main contributions to the existing literature. First, our approach allows the decision maker to continuously vary between specific concepts of individual and group fairness. As a consequence, the algorithm enables the decision maker to adopt intermediate ``worldviews'' on the degree of discrimination encoded in algorithmic processes, adding nuance to the extreme cases of ``we're all equal'' (WAE) and ``what you see is what you get'' (WYSIWYG) proposed so far in the literature. Second, we use optimal transport theory, and specifically the concept of the barycenter, to maximize decision maker utility under the chosen fairness constraints. Third, the algorithm is able to handle cases of intersectionality, i.e., of multi-dimensional discrimination of certain groups on grounds of several criteria. We discuss three main examples (credit applications; college admissions; insurance contracts) and map out the legal and policy implications of our approach. The explicit formalization of the trade-off between individual and group fairness allows this post-processing approach to be tailored to different situational contexts in which one or the other fairness criterion may take precedence. Finally, we evaluate our model experimentally.
Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking
Authors: Aljoša Ošep, Wolfgang Mehner, Paul Voigtlaender, Bastian Leibe
Date: 2017-12-21 00:00:00
Tasks: Image Segmentation, Multi-Object Tracking, Object, Object Tracking, Segmentation, Semantic Segmentation

The most common paradigm for vision-based multi-object tracking is
tracking-by-detection, due to the availability of reliable detectors for
several important object categories such as cars and pedestrians. However,
future mobile systems will need a capability to cope with rich human-made
environments, in which obtaining detectors for every possible object category
would be infeasible. In this paper, we propose a model-free multi-object
tracking approach that uses a category-agnostic image segmentation method to
track objects. We present an efficient segmentation mask-based tracker which
associates pixel-precise masks reported by the segmentation. Our approach can
utilize semantic information whenever it is available for classifying objects
at the track level, while retaining the capability to track generic unknown
objects in the absence of such information. We demonstrate experimentally that
our approach achieves performance comparable to state-of-the-art
tracking-by-detection methods for popular object categories such as cars and
pedestrians. Additionally, we show that the proposed method can discover and
robustly track a large variety of other objects.
Region-based Quality Estimation Network for Large-scale Person Re-identification
Authors: Guanglu Song, Biao Leng, Yu Liu, Congrui Hetang, Shaofan Cai
Date: 2017-11-23 00:00:00
Tasks: Large-Scale Person Re-Identification, Person Re-Identification

One of the major restrictions on the performance of video-based person re-id
is partial noise caused by occlusion, blur and illumination. Since different
spatial regions of a single frame have various quality, and the quality of the
same region also varies across frames in a tracklet, a good way to address the
problem is to effectively aggregate complementary information from all frames
in a sequence, using better regions from other frames to compensate the
influence of an image region with poor quality. To achieve this, we propose a
novel Region-based Quality Estimation Network (RQEN), in which an ingenious
training mechanism enables the effective learning to extract the complementary
region-based information between different frames. Compared with other feature
extraction methods, we achieved comparable results of 92.4%, 76.1% and 77.83%
on the PRID 2011, iLIDS-VID and MARS, respectively. In addition, to alleviate
the lack of clean large-scale person re-id datasets for the community, this
paper also contributes a new high-quality dataset, named "Labeled Pedestrian in
the Wild (LPW)" which contains 7,694 tracklets with over 590,000 images.
Despite its relatively large scale, the annotations also possess high
cleanliness. Moreover, it's more challenging in the following aspects: the age
of characters varies from childhood to elderhood; the postures of people are
diverse, including running and cycling in addition to the normal walking state.
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Authors: Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio, Tuan Anh Le, Frank Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat
Date: 2017-12-21 00:00:00
Tasks: Bayesian Inference, Probabilistic Programming

We consider the problem of Bayesian inference in the family of probabilistic
models implicitly defined by stochastic generative models of data. In
scientific fields ranging from population biology to cosmology, low-level
mechanistic components are composed to create complex generative models. These
models lead to intractable likelihoods and are typically non-differentiable,
which poses challenges for traditional approaches to inference. We extend
previous work in "inference compilation", which combines universal
probabilistic programming and deep learning methods, to large-scale scientific
simulators, and introduce a C++ based probabilistic programming library called
CPProb. We successfully use CPProb to interface with SHERPA, a large code-base
used in particle physics. Here we describe the technical innovations realized
and planned for this library.
Non-convex Optimization for Machine Learning
Authors: Prateek Jain, Purushottam Kar
Date: 2017-12-21 00:00:00
Tasks: BIG-bench Machine Learning

A vast majority of machine learning algorithms train their models and perform
inference by solving optimization problems. In order to capture the learning
and prediction problems accurately, structural constraints such as sparsity or
low rank are frequently imposed or else the objective itself is designed to be
a non-convex function. This is especially true of algorithms that operate in
high-dimensional spaces or that train non-linear models such as tensor models
and deep networks.
  The freedom to express the learning problem as a non-convex optimization
problem gives immense modeling power to the algorithm designer, but often such
problems are NP-hard to solve. A popular workaround to this has been to relax
non-convex problems to convex ones and use traditional methods to solve the
(convex) relaxed optimization problems. However this approach may be lossy and
nevertheless presents significant challenges for large scale optimization.
  On the other hand, direct approaches to non-convex optimization have met with
resounding success in several domains and remain the methods of choice for the
practitioner, as they frequently outperform relaxation-based techniques -
popular heuristics include projected gradient descent and alternating
minimization. However, these are often poorly understood in terms of their
convergence and other properties.
  This monograph presents a selection of recent advances that bridge a
long-standing gap in our understanding of these heuristics. The monograph will
lead the reader through several widely used non-convex optimization techniques,
as well as applications thereof. The goal of this monograph is to both,
introduce the rich literature in this area, as well as equip the reader with
the tools and techniques needed to analyze these simple procedures for
non-convex problems.
Multiagent-based Participatory Urban Simulation through Inverse Reinforcement Learning
Authors: Soma Suzuki
Date: 2017-12-21 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The multiagent-based participatory simulation features prominently in urban
planning as the acquired model is considered as the hybrid system of the domain
and the local knowledge. However, the key problem of generating realistic
agents for particular social phenomena invariably remains. The existing models
have attempted to dictate the factors involving human behavior, which appeared
to be intractable. In this paper, Inverse Reinforcement Learning (IRL) is
introduced to address this problem. IRL is developed for computational modeling
of human behavior and has achieved great successes in robotics, psychology and
machine learning. The possibilities presented by this new style of modeling are
drawn out as conclusions, and the relative challenges with this modeling are
highlighted.
Transfer Learning for OCRopus Model Training on Early Printed Books
Authors: Christian Reul, Christoph Wick, Uwe Springmann, Frank Puppe
Date: 2017-12-15 00:00:00
Tasks: Optical Character Recognition (OCR), Transfer Learning

A method is presented that significantly reduces the character error rates
for OCR text obtained from OCRopus models trained on early printed books when
only small amounts of diplomatic transcriptions are available. This is achieved
by building from already existing models during training instead of starting
from scratch. To overcome the discrepancies between the set of characters of
the pretrained model and the additional ground truth the OCRopus code is
adapted to allow for alphabet expansion or reduction. The character set is now
capable of flexibly adding and deleting characters from the pretrained alphabet
when an existing model is loaded. For our experiments we use a self-trained
mixed model on early Latin prints and the two standard OCRopus models on modern
English and German Fraktur texts. The evaluation on seven early printed books
showed that training from the Latin mixed model reduces the average amount of
errors by 43% and 26%, respectively compared to training from scratch with 60
and 150 lines of ground truth, respectively. Furthermore, it is shown that even
building from mixed models trained on data unrelated to the newly added
training and test data can lead to significantly improved recognition results.
Multi-task learning of time series and its application to the travel demand
Authors: Boris Chidlovskii
Date: 2017-12-21 00:00:00
Tasks: Dynamic Time Warping, Multi-Task Learning, Time Series, Time Series Analysis

We address the problem of modeling and prediction of a set of temporal events
in the context of intelligent transportation systems. To leverage the
information shared by different events, we propose a multi-task learning
framework. We develop a support vector regression model for joint learning of
mutually dependent time series. It is the regularization-based multi-task
learning previously developed for the classification case and extended to time
series. We discuss the relatedness of observed time series and first deploy the
dynamic time warping distance measure to identify groups of similar series.
Then we take into account both time and scale warping and propose to align
multiple time series by inferring their common latent representation. We test
the proposed models on the problem of travel demand prediction in Nancy
(France) public transport system and analyze the benefits of multi-task
learning.
Compiling quantum circuits to realistic hardware architectures using temporal planners
Authors: Davide Venturelli, Minh Do, Eleanor Rieffel, Jeremy Frank
Date: 2017-05-24 00:00:00

To run quantum algorithms on emerging gate-model quantum hardware, quantum
circuits must be compiled to take into account constraints on the hardware. For
near-term hardware, with only limited means to mitigate decoherence, it is
critical to minimize the duration of the circuit. We investigate the
application of temporal planners to the problem of compiling quantum circuits
to newly emerging quantum hardware. While our approach is general, we focus on
compiling to superconducting hardware architectures with nearest neighbor
constraints. Our initial experiments focus on compiling Quantum Alternating
Operator Ansatz (QAOA) circuits whose high number of commuting gates allow
great flexibility in the order in which the gates can be applied. That freedom
makes it more challenging to find optimal compilations but also means there is
a greater potential win from more optimized compilation than for less flexible
circuits. We map this quantum circuit compilation problem to a temporal
planning problem, and generated a test suite of compilation problems for QAOA
circuits of various sizes to a realistic hardware architecture. We report
compilation results from several state-of-the-art temporal planners on this
test set. This early empirical evaluation demonstrates that temporal planning
is a viable approach to quantum circuit compilation.
Human experts vs. machines in taxa recognition
Authors: Johanna Ärje, Jenni Raitoharju, Alexandros Iosifidis, Ville Tirronen, Kristian Meissner, Moncef Gabbouj, Serkan Kiranyaz, Salme Kärkkäinen
Date: 2017-08-23 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Transfer Learning

The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards the ability and logic of machines. In our study, we investigate both the differences in accuracy and in the identification logic of taxonomic experts and machines. We propose a systematic approach utilizing deep Convolutional Neural Nets with the transfer learning paradigm and extensively evaluate it over a multi-pose taxonomic dataset with hierarchical labels specifically created for this comparison. We also study the prediction accuracy on different ranks of taxonomic hierarchy in detail. We used support vector machine classifier as a benchmark. Our results revealed that human experts using actual specimens yield the lowest classification error ($\overline{CE}=6.1\%$). However, a much faster, automated approach using deep Convolutional Neural Nets comes close to human accuracy ($\overline{CE}=11.4\%$) when a typical flat classification approach is used. Contrary to previous findings in the literature, we find that for machines following a typical flat classification approach commonly used in machine learning performs better than forcing machines to adopt a hierarchical, local per parent node approach used by human taxonomic experts ($\overline{CE}=13.8\%$). Finally, we publicly share our unique dataset to serve as a public benchmark dataset in this field.
Inverse Ising problem in continuous time: A latent variable approach
Authors: Christian Donner, Manfred Opper
Date: 2017-09-04 00:00:00
Tasks: Bayesian Inference

We consider the inverse Ising problem, i.e. the inference of network
couplings from observed spin trajectories for a model with continuous time
Glauber dynamics. By introducing two sets of auxiliary latent random variables
we render the likelihood into a form, which allows for simple iterative
inference algorithms with analytical updates. The variables are: (1) Poisson
variables to linearise an exponential term which is typical for point process
likelihoods and (2) P\'olya-Gamma variables, which make the likelihood
quadratic in the coupling parameters. Using the augmented likelihood, we derive
an expectation-maximization (EM) algorithm to obtain the maximum likelihood
estimate of network parameters. Using a third set of latent variables we extend
the EM algorithm to sparse couplings via L1 regularization. Finally, we develop
an efficient approximate Bayesian inference algorithm using a variational
approach. We demonstrate the performance of our algorithms on data simulated
from an Ising model. For data which are simulated from a more biologically
plausible network with spiking neurons, we show that the Ising model captures
well the low order statistics of the data and how the Ising couplings are
related to the underlying synaptic structure of the simulated network.
Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations
Authors: Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson
Date: 2017-12-21 00:00:00

Neural networks have been widely used to solve complex real-world problems.
Due to the complicate, nonlinear, non-convex nature of neural networks, formal
safety guarantees for the output behaviors of neural networks will be crucial
for their applications in safety-critical systems.In this paper, the output
reachable set computation and safety verification problems for a class of
neural networks consisting of Rectified Linear Unit (ReLU) activation functions
are addressed. A layer-by-layer approach is developed to compute output
reachable set. The computation is formulated in the form of a set of
manipulations for a union of polyhedra, which can be efficiently applied with
the aid of polyhedron computation tools. Based on the output reachable set
computation results, the safety verification for a ReLU neural network can be
performed by checking the intersections of unsafe regions and output reachable
set described by a union of polyhedra. A numerical example of a randomly
generated ReLU neural network is provided to show the effectiveness of the
approach developed in this paper.
Exploring Models and Data for Remote Sensing Image Caption Generation
Authors: Xiaoqiang Lu, Binqiang Wang, Xiangtao Zheng, Xuelong. Li
Date: 2017-12-21 00:00:00
Tasks: Caption Generation, Image-to-Text Retrieval, Scene Classification

Inspired by recent development of artificial satellite, remote sensing images
have attracted extensive attention. Recently, noticeable progress has been made
in scene classification and target detection.However, it is still not clear how
to describe the remote sensing image content with accurate and concise
sentences. In this paper, we investigate to describe the remote sensing images
with accurate and flexible sentences. First, some annotated instructions are
presented to better describe the remote sensing images considering the special
characteristics of remote sensing images. Second, in order to exhaustively
exploit the contents of remote sensing images, a large-scale aerial image data
set is constructed for remote sensing image caption. Finally, a comprehensive
review is presented on the proposed data set to fully advance the task of
remote sensing caption. Extensive experiments on the proposed data set
demonstrate that the content of the remote sensing image can be completely
described by generating language descriptions. The data set is available at
https://github.com/201528014227051/RSICD_optimal
Indoor Sound Source Localization with Probabilistic Neural Network
Authors: Yingxiang Sun, Jiajia Chen, Chau Yuen, Susanto Rahardja
Date: 2017-12-21 00:00:00
Tasks: Direction of Arrival Estimation, Sound Source Localization

It is known that adverse environments such as high reverberation and low
signal-to-noise ratio (SNR) pose a great challenge to indoor sound source
localization. To address this challenge, in this paper, we propose a sound
source localization algorithm based on probabilistic neural network, namely
Generalized cross correlation Classification Algorithm (GCA). Experimental
results for adverse environments with high reverberation time T60 up to 600ms
and low SNR such as -10dB show that, the average azimuth angle error and
elevation angle error by GCA are only 4.6 degrees and 3.1 degrees respectively.
Compared with three recently published algorithms, GCA has increased the
success rate on direction of arrival estimation significantly with good
robustness to environmental changes. These results show that the proposed GCA
can localize accurately and robustly for diverse indoor applications where the
site acoustic features can be studied prior to the localization stage.
Fast and Efficient Calculations of Structural Invariants of Chirality
Authors: He Zhang, Hanlin Mo, You Hao, Shirui Li, Hua Li
Date: 2017-10-20 00:00:00
Tasks: Symmetry Detection

Chirality plays an important role in physics, chemistry, biology, and other
fields. It describes an essential symmetry in structure. However, chirality
invariants are usually complicated in expression or difficult to evaluate. In
this paper, we present five general three-dimensional chirality invariants
based on the generating functions. And the five chiral invariants have four
characteristics:(1) They play an important role in the detection of symmetry,
especially in the treatment of 'false zero' problem. (2) Three of the five
chiral invariants decode an universal chirality index. (3) Three of them are
proposed for the first time. (4) The five chiral invariants have low order no
bigger than 4, brief expression, low time complexity O(n) and can act as
descriptors of three-dimensional objects in shape analysis. The five chiral
invariants give a geometric view to study the chiral invariants. And the
experiments show that the five chirality invariants are effective and
efficient, they can be used as a tool for symmetry detection or features in
shape analysis.
Multi-dimensional Graph Fourier Transform
Authors: Takashi Kurokawa, Taihei Oki, Hiromichi Nagao
Date: 2017-12-21 00:00:00
Tasks: Time Series Analysis

Many signals on Cartesian product graphs appear in the real world, such as
digital images, sensor observation time series, and movie ratings on Netflix.
These signals are "multi-dimensional" and have directional characteristics
along each factor graph. However, the existing graph Fourier transform does not
distinguish these directions, and assigns 1-D spectra to signals on product
graphs. Further, these spectra are often multi-valued at some frequencies. Our
main result is a multi-dimensional graph Fourier transform that solves such
problems associated with the conventional GFT. Using algebraic properties of
Cartesian products, the proposed transform rearranges 1-D spectra obtained by
the conventional GFT into the multi-dimensional frequency domain, of which each
dimension represents a directional frequency along each factor graph. Thus, the
multi-dimensional graph Fourier transform enables directional frequency
analysis, in addition to frequency analysis with the conventional GFT.
Moreover, this rearrangement resolves the multi-valuedness of spectra in some
cases. The multi-dimensional graph Fourier transform is a foundation of novel
filterings and stationarities that utilize dimensional information of graph
signals, which are also discussed in this study. The proposed methods are
applicable to a wide variety of data that can be regarded as signals on
Cartesian product graphs. This study also notes that multivariate graph signals
can be regarded as 2-D univariate graph signals. This correspondence provides
natural definitions of the multivariate graph Fourier transform and the
multivariate stationarity based on their 2-D univariate versions.