pwc-archive/papers-with-abstracts huggingface dataset:
Mining for meaning: from vision to language through multiple networks consensus
Authors: Iulia Duta, Andrei Liviu Nicolicioiu, Simion-Vlad Bogolin, Marius Leordeanu
Date: 2018-06-05 00:00:00
Tasks: Decoder

Describing visual data into natural language is a very challenging task, at
the intersection of computer vision, natural language processing and machine
learning. Language goes well beyond the description of physical objects and
their interactions and can convey the same abstract idea in many ways. It is
both about content at the highest semantic level as well as about fluent form.
Here we propose an approach to describe videos in natural language by reaching
a consensus among multiple encoder-decoder networks. Finding such a consensual
linguistic description, which shares common properties with a larger group, has
a better chance to convey the correct meaning. We propose and train several
network architectures and use different types of image, audio and video
features. Each model produces its own description of the input video and the
best one is chosen through an efficient, two-phase consensus process. We
demonstrate the strength of our approach by obtaining state of the art results
on the challenging MSR-VTT dataset.
Unbiased estimates for linear regression via volume sampling
Authors: Michał Dereziński, Manfred K. Warmuth
Date: 2017-05-19 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: regression

Given a full rank matrix $X$ with more columns than rows, consider the task
of estimating the pseudo inverse $X^+$ based on the pseudo inverse of a sampled
subset of columns (of size at least the number of rows). We show that this is
possible if the subset of columns is chosen proportional to the squared volume
spanned by the rows of the chosen submatrix (ie, volume sampling). The
resulting estimator is unbiased and surprisingly the covariance of the
estimator also has a closed form: It equals a specific factor times
$X^{+\top}X^+$. Pseudo inverse plays an important part in solving the linear
least squares problem, where we try to predict a label for each column of $X$.
We assume labels are expensive and we are only given the labels for the small
subset of columns we sample from $X$. Using our methods we show that the weight
vector of the solution for the sub problem is an unbiased estimator of the
optimal solution for the whole problem based on all column labels. We believe
that these new formulas establish a fundamental connection between linear least
squares and volume sampling. We use our methods to obtain an algorithm for
volume sampling that is faster than state-of-the-art and for obtaining bounds
for the total loss of the estimated least-squares solution on all labeled
columns.
Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications
Authors: A. Hunter, B. A. Moore, M. K. Mudunuru, V. T. Chau, R. L. Miller, R. B. Tchoua, C. Nyshadham, S. Karra, D. O. Malley, E. Rougier, H. S. Viswanathan, G. Srinivasan
Date: 2018-06-05 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, five different approaches for reduced-order modeling of
brittle fracture in geomaterials, specifically concrete, are presented and
compared. Four of the five methods rely on machine learning (ML) algorithms to
approximate important aspects of the brittle fracture problem. In addition to
the ML algorithms, each method incorporates different physics-based assumptions
in order to reduce the computational complexity while maintaining the physics
as much as possible. This work specifically focuses on using the ML approaches
to model a 2D concrete sample under low strain rate pure tensile loading
conditions with 20 preexisting cracks present. A high-fidelity finite
element-discrete element model is used to both produce a training dataset of
150 simulations and an additional 35 simulations for validation. Results from
the ML approaches are directly compared against the results from the
high-fidelity model. Strengths and weaknesses of each approach are discussed
and the most important conclusion is that a combination of physics-informed and
data-driven features are necessary for emulating the physics of crack
propagation, interaction and coalescence. All of the models presented here have
runtimes that are orders of magnitude faster than the original high-fidelity
model and pave the path for developing accurate reduced order models that could
be used to inform larger length-scale models with important sub-scale physics
that often cannot be accounted for due to computational cost.
A linear time method for the detection of point and collective anomalies
Authors: Alexander T. M. Fisch, Idris A. Eckley, Paul Fearnhead
Date: 2018-06-05 00:00:00

The challenge of efficiently identifying anomalies in data sequences is an
important statistical problem that now arises in many applications. Whilst
there has been substantial work aimed at making statistical analyses robust to
outliers, or point anomalies, there has been much less work on detecting
anomalous segments, or collective anomalies, particularly in those settings
where point anomalies might also occur. In this article, we introduce
Collective And Point Anomalies (CAPA), a computationally efficient approach
that is suitable when collective anomalies are characterised by either a change
in mean, variance, or both, and distinguishes them from point anomalies.
Theoretical results establish the consistency of CAPA at detecting collective
anomalies and, as a by-product, the consistency of a popular penalised cost
based change in mean and variance detection method. Empirical results show that
CAPA has close to linear computational cost as well as being more accurate at
detecting and locating collective anomalies than other approaches. We
demonstrate the utility of CAPA through its ability to detect exoplanets from
light curve data from the Kepler telescope.
Learning to Understand Goal Specifications by Modelling Reward
Authors: Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Arian Hosseini, Pushmeet Kohli, Edward Grefenstette
Date: 2018-06-05 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales. To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples. As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data. This framework effectively separates the representation of what instructions require from how they can be executed. In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples.
Towards Black-box Iterative Machine Teaching
Authors: Weiyang Liu, Bo Dai, Xingguo Li, Zhen Liu, James M. Rehg, Le Song
Date: 2017-10-21 00:00:00
Proceeding: ICML 2018 7

In this paper, we make an important step towards the black-box machine
teaching by considering the cross-space machine teaching, where the teacher and
the learner use different feature representations and the teacher can not fully
observe the learner's model. In such scenario, we study how the teacher is
still able to teach the learner to achieve faster convergence rate than the
traditional passive learning. We propose an active teacher model that can
actively query the learner (i.e., make the learner take exams) for estimating
the learner's status and provably guide the learner to achieve faster
convergence. The sample complexities for both teaching and query are provided.
In the experiments, we compare the proposed active teacher with the omniscient
teacher and verify the effectiveness of the active teacher model.
Supervised learning with quantum enhanced feature spaces
Authors: Vojtech Havlicek, Antonio D. Córcoles, Kristan Temme, Aram W. Harrow, Abhinav Kandala, Jerry M. Chow, Jay M. Gambetta
Date: 2018-04-30 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Machine learning and quantum computing are two technologies each with the
potential for altering how computation is performed to address previously
untenable problems. Kernel methods for machine learning are ubiquitous for
pattern recognition, with support vector machines (SVMs) being the most
well-known method for classification problems. However, there are limitations
to the successful solution to such problems when the feature space becomes
large, and the kernel functions become computationally expensive to estimate. A
core element to computational speed-ups afforded by quantum algorithms is the
exploitation of an exponentially large quantum state space through controllable
entanglement and interference. Here, we propose and experimentally implement
two novel methods on a superconducting processor. Both methods represent the
feature space of a classification problem by a quantum state, taking advantage
of the large dimensionality of quantum Hilbert space to obtain an enhanced
solution. One method, the quantum variational classifier builds on [1,2] and
operates through using a variational quantum circuit to classify a training set
in direct analogy to conventional SVMs. In the second, a quantum kernel
estimator, we estimate the kernel function and optimize the classifier
directly. The two methods present a new class of tools for exploring the
applications of noisy intermediate scale quantum computers [3] to machine
learning.
EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch
Authors: Jian Ren, Zhe Li, Jianchao Yang, Ning Xu, Tianbao Yang, David J. Foran
Date: 2018-06-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: GPU

Designing the structure of neural networks is considered one of the most
challenging tasks in deep learning, especially when there is few prior
knowledge about the task domain. In this paper, we propose an
Ecologically-Inspired GENetic (EIGEN) approach that uses the concept of
succession, extinction, mimicry, and gene duplication to search neural network
structure from scratch with poorly initialized simple network and few
constraints forced during the evolution, as we assume no prior knowledge about
the task domain. Specifically, we first use primary succession to rapidly
evolve a population of poorly initialized neural network structures into a more
diverse population, followed by a secondary succession stage for fine-grained
searching based on the networks from the primary succession. Extinction is
applied in both stages to reduce computational cost. Mimicry is employed during
the entire evolution process to help the inferior networks imitate the behavior
of a superior network and gene duplication is utilized to duplicate the learned
blocks of novel structures, both of which help to find better network
structures. Experimental results show that our proposed approach can achieve
similar or better performance compared to the existing genetic approaches with
dramatically reduced computation cost. For example, the network discovered by
our approach on CIFAR-100 dataset achieves 78.1% test accuracy under 120 GPU
hours, compared to 77.0% test accuracy in more than 65, 536 GPU hours in [35].
Exploring Feature Reuse in DenseNet Architectures
Authors: Andy Hess
Date: 2018-06-05 00:00:00
Tasks: General Classification, image-classification, Image Classification

Densely Connected Convolutional Networks (DenseNets) have been shown to
achieve state-of-the-art results on image classification tasks while using
fewer parameters and computation than competing methods. Since each layer in
this architecture has full access to the feature maps of all previous layers,
the network is freed from the burden of having to relearn previously useful
features, thus alleviating issues with vanishing gradients. In this work we
explore the question: To what extent is it necessary to connect to all previous
layers in order to reap the benefits of feature reuse? To this end, we
introduce the notion of local dense connectivity and present evidence that less
connectivity, allowing for increased growth rate at a fixed network capacity,
can achieve a more efficient reuse of features and lead to higher accuracy in
dense architectures.
Explainable Neural Networks based on Additive Index Models
Authors: Joel Vaughan, Agus Sudjianto, Erind Brahimi, Jie Chen, Vijayan N. Nair
Date: 2018-06-05 00:00:00
Tasks: Feature Engineering

Machine Learning algorithms are increasingly being used in recent years due
to their flexibility in model fitting and increased predictive performance.
However, the complexity of the models makes them hard for the data analyst to
interpret the results and explain them without additional tools. This has led
to much research in developing various approaches to understand the model
behavior. In this paper, we present the Explainable Neural Network (xNN), a
structured neural network designed especially to learn interpretable features.
Unlike fully connected neural networks, the features engineered by the xNN can
be extracted from the network in a relatively straightforward manner and the
results displayed. With appropriate regularization, the xNN provides a
parsimonious explanation of the relationship between the features and the
output. We illustrate this interpretable feature--engineering property on
simulated examples.
PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making
Authors: Fangkai Yang, Daoming Lyu, Bo Liu, Steven Gustafson
Date: 2018-04-20 00:00:00
Tasks: Decision Making, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning and symbolic planning have both been used to build
intelligent autonomous agents. Reinforcement learning relies on learning from
interactions with real world, which often requires an unfeasibly large amount
of experience. Symbolic planning relies on manually crafted symbolic knowledge,
which may not be robust to domain uncertainties and changes. In this paper we
present a unified framework {\em PEORL} that integrates symbolic planning with
hierarchical reinforcement learning (HRL) to cope with decision-making in a
dynamic environment with uncertainties.
  Symbolic plans are used to guide the agent's task execution and learning, and
the learned experience is fed back to symbolic knowledge to improve planning.
This method leads to rapid policy search and robust symbolic plans in complex
domains. The framework is tested on benchmark domains of HRL.
Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
Authors: Yang Zhang, Philip David, Boqing Gong
Date: 2017-07-29 00:00:00
Proceeding: ICCV 2017 10
Tasks: Autonomous Driving, Domain Adaptation, Image-to-Image Translation, Segmentation, Semantic Segmentation, Superpixels, Synthetic-to-Real Translation

During the last half decade, convolutional neural networks (CNNs) have
triumphed over semantic segmentation, which is one of the core tasks in many
applications such as autonomous driving. However, to train CNNs requires a
considerable amount of data, which is difficult to collect and laborious to
annotate. Recent advances in computer graphics make it possible to train CNNs
on photo-realistic synthetic imagery with computer-generated annotations.
Despite this, the domain mismatch between the real images and the synthetic
data cripples the models' performance. Hence, we propose a curriculum-style
learning approach to minimize the domain gap in urban scenery semantic
segmentation. The curriculum domain adaptation solves easy tasks first to infer
necessary properties about the target domain; in particular, the first task is
to learn global label distributions over images and local distributions over
landmark superpixels. These are easy to estimate because images of urban scenes
have strong idiosyncrasies (e.g., the size and spatial relations of buildings,
streets, cars, etc.). We then train a segmentation network while regularizing
its predictions in the target domain to follow those inferred properties. In
experiments, our method outperforms the baselines on two datasets and two
backbone networks. We also report extensive ablation studies about our
approach.
A fast minimal solver for absolute camera pose with unknown focal length and radial distortion from four planar points
Authors: Magnus Oskarsson
Date: 2018-05-27 00:00:00
Tasks: Camera Pose Estimation, Pose Estimation

In this paper we present a fast minimal solver for absolute camera pose
estimation from four known points that lie in a plane. We assume a perspective
camera model with unknown focal length and unknown radial distortion. The
radial distortion is modelled using the division model with one parameter. We
show that the solutions to this problem can be found from a univariate
six-degree polynomial. This results in a very fast and numerically stable
solver.
Normative Modeling of Neuroimaging Data using Scalable Multi-Task Gaussian Processes
Authors: Seyed Mostafa Kia, Andre Marquand
Date: 2018-06-04 00:00:00
Tasks: Gaussian Processes, Novelty Detection, regression

Normative modeling has recently been proposed as an alternative for the
case-control approach in modeling heterogeneity within clinical cohorts.
Normative modeling is based on single-output Gaussian process regression that
provides coherent estimates of uncertainty required by the method but does not
consider spatial covariance structure. Here, we introduce a scalable multi-task
Gaussian process regression (S-MTGPR) approach to address this problem. To this
end, we exploit a combination of a low-rank approximation of the spatial
covariance matrix with algebraic properties of Kronecker product in order to
reduce the computational complexity of Gaussian process regression in
high-dimensional output spaces. On a public fMRI dataset, we show that S-MTGPR:
1) leads to substantial computational improvements that allow us to estimate
normative models for high-dimensional fMRI data whilst accounting for spatial
structure in data; 2) by modeling both spatial and across-sample variances, it
provides higher sensitivity in novelty detection scenarios.
A Framework for the construction of upper bounds on the number of affine linear regions of ReLU feed-forward neural networks
Authors: Peter Hinz, Sara van de Geer
Date: 2018-06-05 00:00:00

We present a framework to derive upper bounds on the number of regions that feed-forward neural networks with ReLU activation functions are affine linear on. It is based on an inductive analysis that keeps track of the number of such regions per dimensionality of their images within the layers. More precisely, the information about the number regions per dimensionality is pushed through the layers starting with one region of the input dimension of the neural network and using a recursion based on an analysis of how many regions per output dimensionality a subsequent layer with a certain width can induce on an input region with a given dimensionality. The final bound on the number of regions depends on the number and widths of the layers of the neural network and on some additional parameters that were used for the recursion. It is stated in terms of the $L1$-norm of the last column of a product of matrices and provides a unifying treatment of several previously known bounds: Depending on the choice of the recursion parameters that determine these matrices, it is possible to obtain the bounds from Mont\'{u}far (2014), (2017) and Serra et. al. (2017) as special cases. For the latter, which is the strongest of these bounds, the formulation in terms of matrices provides new insight. In particular, by using explicit formulas for a Jordan-like decomposition of the involved matrices, we achieve new tighter results for the asymptotic setting, where the number of layers of the same fixed width tends to infinity.
One-Class Adversarial Nets for Fraud Detection
Authors: Panpan Zheng, Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu
Date: 2018-03-05 00:00:00
Tasks: Fraud Detection, One-Class Classification

Many online applications, such as online social networks or knowledge bases,
are often attacked by malicious users who commit different types of actions
such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most
of the fraud detection approaches require a training dataset that contains
records of both benign and malicious users. However, in practice, there are
often no or very few records of malicious users. In this paper, we develop
one-class adversarial nets (OCAN) for fraud detection using training data with
only benign users. OCAN first uses LSTM-Autoencoder to learn the
representations of benign users from their sequences of online activities. It
then detects malicious users by training a discriminator with a complementary
GAN model that is different from the regular GAN model. Experimental results
show that our OCAN outperforms the state-of-the-art one-class classification
models and achieves comparable performance with the latest multi-source LSTM
model that requires both benign and malicious users in the training phase.
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision
Authors: Rakshith Shetty, Mario Fritz, Bernt Schiele
Date: 2018-06-05 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Generative Adversarial Network, Image Manipulation, Object

While great progress has been made recently in automatic image manipulation,
it has been limited to object centric images like faces or structured scene
datasets. In this work, we take a step towards general scene-level image
editing by developing an automatic interaction-free object removal model. Our
model learns to find and remove objects from general scene images using
image-level labels and unpaired data in a generative adversarial network (GAN)
framework. We achieve this with two key contributions: a two-stage editor
architecture consisting of a mask generator and image in-painter that
co-operate to remove objects, and a novel GAN based prior for the mask
generator that allows us to flexibly incorporate knowledge about object shapes.
We experimentally show on two datasets that our method effectively removes a
wide variety of objects using weak supervision only
Probabilistic Deep Learning using Random Sum-Product Networks
Authors: Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani
Date: 2018-06-05 00:00:00
Tasks: Deep Learning, GPU, Probabilistic Deep Learning

The need for consistent treatment of uncertainty has recently triggered
increased interest in probabilistic deep learning methods. However, most
current approaches have severe limitations when it comes to inference, since
many of these models do not even permit to evaluate exact data likelihoods.
Sum-product networks (SPNs), on the other hand, are an excellent architecture
in that regard, as they allow to efficiently evaluate likelihoods, as well as
arbitrary marginalization and conditioning tasks. Nevertheless, SPNs have not
been fully explored as serious deep learning models, likely due to their
special structural requirements, which complicate learning. In this paper, we
make a drastic simplification and use random SPN structures which are trained
in a "classical deep learning manner", i.e. employing automatic
differentiation, SGD, and GPU support. The resulting models, called RAT-SPNs,
yield prediction results comparable to deep neural networks, while still being
interpretable as generative model and maintaining well-calibrated
uncertainties. This property makes them highly robust under missing input
features and enables them to naturally detect outliers and peculiar samples.