pwc-archive/papers-with-abstracts huggingface dataset:
The Boolean Solution Problem from the Perspective of Predicate Logic - Extended Version
Authors: Christoph Wernhard
Date: 2017-06-26 00:00:00

Finding solution values for unknowns in Boolean equations was a principal
reasoning mode in the Algebra of Logic of the 19th century. Schr\"oder
investigated it as "Aufl\"osungsproblem" ("solution problem"). It is closely
related to the modern notion of Boolean unification. Today it is commonly
presented in an algebraic setting, but seems potentially useful also in
knowledge representation based on predicate logic. We show that it can be
modeled on the basis of first-order logic extended by second-order
quantification. A wealth of classical results transfers, foundations for
algorithms unfold, and connections with second-order quantifier elimination and
Craig interpolation show up. Although for first-order inputs the set of
solutions is recursively enumerable, the development of constructive methods
remains a challenge. We identify some cases that allow constructions, most of
them based on Craig interpolation, and show a method to take vocabulary
restrictions on solution components into account.
Accurate Inference for Adaptive Linear Models
Authors: Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, Matt Taddy
Date: 2017-12-18 00:00:00
Proceeding: ICML 2018 7
Tasks: Time Series, Time Series Analysis

Estimators computed from adaptively collected data do not behave like their non-adaptive brethren. Rather, the sequential dependence of the collection policy can lead to severe distributional biases that persist even in the infinite data limit. We develop a general method -- $\mathbf{W}$-decorrelation -- for transforming the bias of adaptive linear regression estimators into variance. The method uses only coarse-grained information about the data collection policy and does not need access to propensity scores or exact knowledge of the policy. We bound the finite-sample bias and variance of the $\mathbf{W}$-estimator and develop asymptotically correct confidence intervals based on a novel martingale central limit theorem. We then demonstrate the empirical benefits of the generic $\mathbf{W}$-decorrelation procedure in two different adaptive data settings: the multi-armed bandit and the autoregressive time series.
Learning Visual Reasoning Without Strong Priors
Authors: Ethan Perez, Harm de Vries, Florian Strub, Vincent Dumoulin, Aaron Courville
Date: 2017-07-10 00:00:00
Tasks: Visual Reasoning

Achieving artificial visual reasoning - the ability to answer image-related
questions which require a multi-step, high-level process - is an important step
towards artificial general intelligence. This multi-modal task requires
learning a question-dependent, structured reasoning process over images from
language. Standard deep learning approaches tend to exploit biases in the data
rather than learn this underlying structure, while leading methods learn to
visually reason successfully but are hand-crafted for reasoning. We show that a
general-purpose, Conditional Batch Normalization approach achieves
state-of-the-art results on the CLEVR Visual Reasoning benchmark with a 2.4%
error rate. We outperform the next best end-to-end method (4.5%) and even
methods that use extra supervision (3.1%). We probe our model to shed light on
how it reasons, showing it has learned a question-dependent, multi-step
process. Previous work has operated under the assumption that visual reasoning
calls for a specialized architecture, but we show that a general architecture
with proper conditioning can learn to visually reason effectively.
Ranking Median Regression: Learning to Order through Local Consensus
Authors: Stephan Clémençon, Anna Korba, Eric Sibony
Date: 2017-10-31 00:00:00
Tasks: regression

This article is devoted to the problem of predicting the value taken by a
random permutation $\Sigma$, describing the preferences of an individual over a
set of numbered items $\{1,\; \ldots,\; n\}$ say, based on the observation of
an input/explanatory r.v. $X$ e.g. characteristics of the individual), when
error is measured by the Kendall $\tau$ distance. In the probabilistic
formulation of the 'Learning to Order' problem we propose, which extends the
framework for statistical Kemeny ranking aggregation developped in
\citet{CKS17}, this boils down to recovering conditional Kemeny medians of
$\Sigma$ given $X$ from i.i.d. training examples $(X_1, \Sigma_1),\; \ldots,\;
(X_N, \Sigma_N)$. For this reason, this statistical learning problem is
referred to as \textit{ranking median regression} here. Our contribution is
twofold. We first propose a probabilistic theory of ranking median regression:
the set of optimal elements is characterized, the performance of empirical risk
minimizers is investigated in this context and situations where fast learning
rates can be achieved are also exhibited. Next we introduce the concept of
local consensus/median, in order to derive efficient methods for ranking median
regression. The major advantage of this local learning approach lies in its
close connection with the widely studied Kemeny aggregation problem. From an
algorithmic perspective, this permits to build predictive rules for ranking
median regression by implementing efficient techniques for (approximate) Kemeny
median computations at a local level in a tractable manner. In particular,
versions of $k$-nearest neighbor and tree-based methods, tailored to ranking
median regression, are investigated. Accuracy of piecewise constant ranking
median regression rules is studied under a specific smoothness assumption for
$\Sigma$'s conditional distribution given $X$.
FiLM: Visual Reasoning with a General Conditioning Layer
Authors: Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, Aaron Courville
Date: 2017-09-22 00:00:00
Tasks: Image Retrieval with Multi-Modal Query, Visual Question Answering (VQA), Visual Question Answering (VQA) Split A, Visual Question Answering (VQA) Split B, Visual Reasoning

We introduce a general-purpose conditioning method for neural networks called
FiLM: Feature-wise Linear Modulation. FiLM layers influence neural network
computation via a simple, feature-wise affine transformation based on
conditioning information. We show that FiLM layers are highly effective for
visual reasoning - answering image-related questions which require a
multi-step, high-level process - a task which has proven difficult for standard
deep learning methods that do not explicitly model reasoning. Specifically, we
show on visual reasoning tasks that FiLM layers 1) halve state-of-the-art error
for the CLEVR benchmark, 2) modulate features in a coherent manner, 3) are
robust to ablations and architectural modifications, and 4) generalize well to
challenging, new data from few examples or even zero-shot.
Synthesizing Novel Pairs of Image and Text
Authors: Jason Xie, Tingwen Bao
Date: 2017-12-18 00:00:00
Tasks: Image to text

Generating novel pairs of image and text is a problem that combines computer
vision and natural language processing. In this paper, we present strategies
for generating novel image and caption pairs based on existing captioning
datasets. The model takes advantage of recent advances in generative
adversarial networks and sequence-to-sequence modeling. We make generalizations
to generate paired samples from multiple domains. Furthermore, we study cycles
-- generating from image to text then back to image and vise versa, as well as
its connection with autoencoders.
word representation or word embedding in Persian text
Authors: Siamak Sarmady, Erfan Rahmani
Date: 2017-12-18 00:00:00

Text processing is one of the sub-branches of natural language processing.
Recently, the use of machine learning and neural networks methods has been
given greater consideration. For this reason, the representation of words has
become very important. This article is about word representation or converting
words into vectors in Persian text. In this research GloVe, CBOW and skip-gram
methods are updated to produce embedded vectors for Persian words. In order to
train a neural networks, Bijankhan corpus, Hamshahri corpus and UPEC corpus
have been compound and used. Finally, we have 342,362 words that obtained
vectors in all three models for this words. These vectors have many usage for
Persian natural language processing.
Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology
Authors: Andreas Holzinger, Bernd Malle, Peter Kieseberg, Peter M. Roth, Heimo Müller, Robert Reihs, Kurt Zatloukal
Date: 2017-12-18 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic, Prognosis

Digital pathology is not only one of the most promising fields of diagnostic
medicine, but at the same time a hot topic for fundamental research. Digital
pathology is not just the transfer of histopathological slides into digital
representations. The combination of different data sources (images, patient
records, and *omics data) together with current advances in artificial
intelligence/machine learning enable to make novel information accessible and
quantifiable to a human expert, which is not yet available and not exploited in
current medical settings. The grand goal is to reach a level of usable
intelligence to understand the data in the context of an application task,
thereby making machine decisions transparent, interpretable and explainable.
The foundation of such an "augmented pathologist" needs an integrated approach:
While machine learning algorithms require many thousands of training examples,
a human expert is often confronted with only a few data points. Interestingly,
humans can learn from such few examples and are able to instantly interpret
complex patterns. Consequently, the grand goal is to combine the possibilities
of artificial intelligence with human intelligence and to find a well-suited
balance between them to enable what neither of them could do on their own. This
can raise the quality of education, diagnosis, prognosis and prediction of
cancer and other diseases. In this paper we describe some (incomplete) research
issues which we believe should be addressed in an integrated and concerted
effort for paving the way towards the augmented pathologist.
Modulating early visual processing by language
Authors: Harm de Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, Aaron Courville
Date: 2017-07-02 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

It is commonly assumed that language refers to high-level visual concepts
while leaving low-level visual processing unaffected. This view dominates the
current literature in computational models for language-vision tasks, where
visual and linguistic input are mostly processed independently before being
fused into a single representation. In this paper, we deviate from this classic
pipeline and propose to modulate the \emph{entire visual processing} by
linguistic input. Specifically, we condition the batch normalization parameters
of a pretrained residual network (ResNet) on a language embedding. This
approach, which we call MOdulated RESnet (\MRN), significantly improves strong
baselines on two visual question answering tasks. Our ablation study shows that
modulating from the early stages of the visual processing is beneficial.
Feature Selection based on the Local Lift Dependence Scale
Authors: Diego Marcondes, Adilson Simonis, Junior Barrera
Date: 2017-11-11 00:00:00
Tasks: feature selection

This paper uses a classical approach to feature selection: minimization of a
cost function applied on estimated joint distributions. However, the search
space in which such minimization is performed is extended. In the original
formulation, the search space is the Boolean lattice of features sets (BLFS),
while, in the present formulation, it is a collection of Boolean lattices of
ordered pairs (features, associated value) (CBLOP), indexed by the elements of
the BLFS. In this approach, we may not only select the features that are most
related to a variable Y, but also select the values of the features that most
influence the variable or that are most prone to have a specific value of Y. A
local formulation of Shanon's mutual information is applied on a CBLOP to
select features, namely, the Local Lift Dependence Scale, an scale for
measuring variable dependence in multiple resolutions. The main contribution of
this paper is to define and apply this local measure, which permits to analyse
local properties of joint distributions that are neglected by the classical
Shanon's global measure. The proposed approach is applied to a dataset
consisting of student performances on a university entrance exam, as well as on
undergraduate courses. The approach is also applied to two datasets of the UCI
Machine Learning Repository.
Objects that Sound
Authors: Relja Arandjelović, Andrew Zisserman
Date: 2017-12-18 00:00:00
Proceeding: ECCV 2018 9
Tasks: Cross-Modal Retrieval, Optical Flow Estimation, Retrieval

In this paper our objectives are, first, networks that can embed audio and
visual inputs into a common space that is suitable for cross-modal retrieval;
and second, a network that can localize the object that sounds in an image,
given the audio signal. We achieve both these objectives by training from
unlabelled video using only audio-visual correspondence (AVC) as the objective
function. This is a form of cross-modal self-supervision from video.
  To this end, we design new network architectures that can be trained for
cross-modal retrieval and localizing the sound source in an image, by using the
AVC task. We make the following contributions: (i) show that audio and visual
embeddings can be learnt that enable both within-mode (e.g. audio-to-audio) and
between-mode retrieval; (ii) explore various architectures for the AVC task,
including those for the visual stream that ingest a single image, or multiple
images, or a single image and multi-frame optical flow; (iii) show that the
semantic object that sounds within an image can be localized (using only the
sound, no motion or flow information); and (iv) give a cautionary tale on how
to avoid undesirable shortcuts in the data preparation.
When Not to Classify: Anomaly Detection of Attacks (ADA) on DNN Classifiers at Test Time
Authors: David J. Miller, Yulia Wang, George Kesidis
Date: 2017-12-18 00:00:00
Tasks: Anomaly Detection, General Classification

A significant threat to the recent, wide deployment of machine learning-based
systems, including deep neural networks (DNNs), is adversarial learning
attacks. We analyze possible test-time evasion-attack mechanisms and show that,
in some important cases, when the image has been attacked, correctly
classifying it has no utility: i) when the image to be attacked is (even
arbitrarily) selected from the attacker's cache; ii) when the sole recipient of
the classifier's decision is the attacker. Moreover, in some application
domains and scenarios it is highly actionable to detect the attack irrespective
of correctly classifying in the face of it (with classification still performed
if no attack is detected). We hypothesize that, even if human-imperceptible,
adversarial perturbations are machine-detectable. We propose a purely
unsupervised anomaly detector (AD) that, unlike previous works: i) models the
joint density of a deep layer using highly suitable null hypothesis density
models (matched in particular to the non- negative support for RELU layers);
ii) exploits multiple DNN layers; iii) leverages a "source" and "destination"
class concept, source class uncertainty, the class confusion matrix, and DNN
weight information in constructing a novel decision statistic grounded in the
Kullback-Leibler divergence. Tested on MNIST and CIFAR-10 image databases under
three prominent attack strategies, our approach outperforms previous detection
methods, achieving strong ROC AUC detection accuracy on two attacks and better
accuracy than recently reported for a variety of methods on the strongest (CW)
attack. We also evaluate a fully white box attack on our system. Finally, we
evaluate other important performance measures, such as classification accuracy,
versus detection rate and attack strength.
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima
Authors: Yaodong Yu, Pan Xu, Quanquan Gu
Date: 2017-12-18 00:00:00
Tasks: Stochastic Optimization

We propose stochastic optimization algorithms that can find local minima
faster than existing algorithms for nonconvex optimization problems, by
exploiting the third-order smoothness to escape non-degenerate saddle points
more efficiently. More specifically, the proposed algorithm only needs
$\tilde{O}(\epsilon^{-10/3})$ stochastic gradient evaluations to converge to an
approximate local minimum $\mathbf{x}$, which satisfies $\|\nabla
f(\mathbf{x})\|_2\leq\epsilon$ and $\lambda_{\min}(\nabla^2 f(\mathbf{x}))\geq
-\sqrt{\epsilon}$ in the general stochastic optimization setting, where
$\tilde{O}(\cdot)$ hides logarithm polynomial terms and constants. This
improves upon the $\tilde{O}(\epsilon^{-7/2})$ gradient complexity achieved by
the state-of-the-art stochastic local minima finding algorithms by a factor of
$\tilde{O}(\epsilon^{-1/6})$. For nonconvex finite-sum optimization, our
algorithm also outperforms the best known algorithms in a certain regime.
End-to-end Recovery of Human Shape and Pose
Authors: Angjoo Kanazawa, Michael J. Black, David W. Jacobs, Jitendra Malik
Date: 2017-12-18 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Hand Pose Estimation, 3D Human Pose Estimation, 3D Human Shape Estimation, 3D Multi-Person Pose Estimation, Human Mesh Recovery, Monocular 3D Human Pose Estimation, Multi-Hypotheses 3D Human Pose Estimation, Weakly-supervised 3D Human Pose Estimation

We describe Human Mesh Recovery (HMR), an end-to-end framework for
reconstructing a full 3D mesh of a human body from a single RGB image. In
contrast to most current methods that compute 2D or 3D joint locations, we
produce a richer and more useful mesh representation that is parameterized by
shape and 3D joint angles. The main objective is to minimize the reprojection
loss of keypoints, which allow our model to be trained using images in-the-wild
that only have ground truth 2D annotations. However, the reprojection loss
alone leaves the model highly under constrained. In this work we address this
problem by introducing an adversary trained to tell whether a human body
parameter is real or not using a large database of 3D human meshes. We show
that HMR can be trained with and without using any paired 2D-to-3D supervision.
We do not rely on intermediate 2D keypoint detections and infer 3D pose and
shape parameters directly from image pixels. Our model runs in real-time given
a bounding box containing the person. We demonstrate our approach on various
images in-the-wild and out-perform previous optimization based methods that
output 3D meshes and show competitive results on tasks such as 3D joint
location estimation and part segmentation.
Parallel Complexity of Forward and Backward Propagation
Authors: Maxim Naumov
Date: 2017-12-18 00:00:00

We show that the forward and backward propagation can be formulated as a
solution of lower and upper triangular systems of equations. For standard
feedforward (FNNs) and recurrent neural networks (RNNs) the triangular systems
are always block bi-diagonal, while for a general computation graph (directed
acyclic graph) they can have a more complex triangular sparsity pattern. We
discuss direct and iterative parallel algorithms that can be used for their
solution and interpreted as different ways of performing model parallelism.
Also, we show that for FNNs and RNNs with $k$ layers and $\tau$ time steps the
backward propagation can be performed in parallel in O($\log k$) and O($\log k
\log \tau$) steps, respectively. Finally, we outline the generalization of this
technique using Jacobians that potentially allows us to handle arbitrary
layers.
A generalised framework for detailed classification of swimming paths inside the Morris Water Maze
Authors: Avgoustinos Vouros, Tiago V. Gehring, Kinga Szydlowska, Artur Janusz, Mike Croucher, Katarzyna Lukasiuk, Witold Konopka, Carmen Sandi, Zehai Tu, Eleni Vasilaki
Date: 2017-11-20 00:00:00
Tasks: Classification, General Classification, Robust classification

The Morris Water Maze is commonly used in behavioural neuroscience for the
study of spatial learning with rodents. Over the years, various methods of
analysing rodent data collected in this task have been proposed. These methods
span from classical performance measurements (e.g. escape latency, rodent
speed, quadrant preference) to more sophisticated methods of categorisation
which classify the animal swimming path into behavioural classes known as
strategies. Classification techniques provide additional insight in relation to
the actual animal behaviours but still only a limited amount of studies utilise
them mainly because they highly depend on machine learning knowledge. We have
previously demonstrated that the animals implement various strategies and by
classifying whole trajectories can lead to the loss of important information.
In this work, we developed a generalised and robust classification methodology
which implements majority voting to boost the classification performance and
successfully nullify the need of manual tuning. Based on this framework, we
built a complete software, capable of performing the full analysis described in
this paper. The software provides an easy to use graphical user interface (GUI)
through which users can enter their trajectory data, segment and label them and
finally generate reports and figures of the results.
Multi-point Vibration Measurement for Mode Identification of Bridge Structures using Video-based Motion Magnification
Authors: Zhexiong Shang, Zhigang Shen
Date: 2017-12-18 00:00:00
Tasks: Motion Magnification

Image-based vibration mode identification gained increased attentions in
civil and construction communities. A recent video-based motion magnification
method was developed to measure and visualize small structure motions. This new
approach presents a potential for low-cost vibration measurement and mode shape
identification. Pilot studies using this approach on simple rigid body
structures was reported. Its validity on complex outdoor structures have not
been investigated. The objective is to investigate the capacity of video-based
motion magnification approach in measuring the modal frequency and visualizing
the mode shapes of complex steel bridges. A novel method that increases the
performance of the current motion magnification for efficient structure modal
analysis is introduced. This method was tested in both indoor and outdoor
environments for validation. The results of the investigation show that motion
magnification can be an efficient tool for modal analysis on complex bridge
structures. With the developed method, mode frequencies of multiple structures
are simultaneously measured and mode shapes of each structure are automatically
visualized.