pwc-archive/papers-with-abstracts huggingface dataset:
Fine-grained acceleration control for autonomous intersection management using deep reinforcement learning
Authors: Hamid Mirzaei, Tony Givargis
Date: 2017-05-30 00:00:00
Tasks: Autonomous Vehicles, Deep Reinforcement Learning, Feature Engineering, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent advances in combining deep learning and Reinforcement Learning have
shown a promising path for designing new control agents that can learn optimal
policies for challenging control tasks. These new methods address the main
limitations of conventional Reinforcement Learning methods such as customized
feature engineering and small action/state space dimension requirements. In
this paper, we leverage one of the state-of-the-art Reinforcement Learning
methods, known as Trust Region Policy Optimization, to tackle intersection
management for autonomous vehicles. We show that using this method, we can
perform fine-grained acceleration control of autonomous vehicles in a grid
street plan to achieve a global design objective.
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification
Authors: Xiang Bai, Mingkun Yang, Pengyuan Lyu, Yongchao Xu, Jiebo Luo
Date: 2017-04-15 00:00:00
Tasks: Classification, Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Retrieval

Text in natural images contains rich semantics that are often highly relevant
to objects or scene. In this paper, we focus on the problem of fully exploiting
scene text for visual understanding. The main idea is combining word
representations and deep visual features into a globally trainable deep
convolutional neural network. First, the recognized words are obtained by a
scene text reading system. Then, we combine the word embedding of the
recognized words and the deep visual features into a single representation,
which is optimized by a convolutional neural network for fine-grained image
classification. In our framework, the attention mechanism is adopted to reveal
the relevance between each recognized word and the given image, which further
enhances the recognition performance. We have performed experiments on two
datasets: Con-Text dataset and Drink Bottle dataset, that are proposed for
fine-grained classification of business places and drink bottles, respectively.
The experimental results consistently demonstrate that the proposed method
combining textual and visual cues significantly outperforms classification with
only visual representations. Moreover, we have shown that the learned
representation improves the retrieval performance on the drink bottle images by
a large margin, making it potentially useful in product search.
Discriminatively Learned Hierarchical Rank Pooling Networks
Authors: Basura Fernando, Stephen Gould
Date: 2017-05-30 00:00:00
Tasks: Activity Recognition, Bilevel Optimization, General Classification

In this work, we present novel temporal encoding methods for action and
activity classification by extending the unsupervised rank pooling temporal
encoding method in two ways. First, we present "discriminative rank pooling" in
which the shared weights of our video representation and the parameters of the
action classifiers are estimated jointly for a given training dataset of
labelled vector sequences using a bilevel optimization formulation of the
learning problem. When the frame level features vectors are obtained from a
convolutional neural network (CNN), we rank pool the network activations and
jointly estimate all parameters of the model, including CNN filters and
fully-connected weights, in an end-to-end manner which we coined as "end-to-end
trainable rank pooled CNN". Importantly, this model can make use of any
existing convolutional neural network architecture (e.g., AlexNet or VGG)
without modification or introduction of additional parameters. Then, we extend
rank pooling to a high capacity video representation, called "hierarchical rank
pooling". Hierarchical rank pooling consists of a network of rank pooling
functions, which encode temporal semantics over arbitrary long video clips
based on rich frame level features. By stacking non-linear feature functions
and temporal sub-sequence encoders one on top of the other, we build a high
capacity encoding network of the dynamic behaviour of the video. The resulting
video representation is a fixed-length feature vector describing the entire
video clip that can be used as input to standard machine learning classifiers.
We demonstrate our approach on the task of action and activity recognition.
Obtained results are comparable to state-of-the-art methods on three important
activity recognition benchmarks with classification performance of 76.7% mAP on
Hollywood2, 69.4% on HMDB51, and 93.6% on UCF101.
Uncovering Causality from Multivariate Hawkes Integrated Cumulants
Authors: Massil Achab, Emmanuel Bacry, Stéphane Gaïffas, Iacopo Mastromatteo, Jean-Francois Muzy
Date: 2016-07-21 00:00:00
Proceeding: ICML 2017 8

We design a new nonparametric method that allows one to estimate the matrix
of integrated kernels of a multivariate Hawkes process. This matrix not only
encodes the mutual influences of each nodes of the process, but also
disentangles the causality relationships between them. Our approach is the
first that leads to an estimation of this matrix without any parametric
modeling and estimation of the kernels themselves. A consequence is that it can
give an estimation of causality relationships between nodes (or users), based
on their activity timestamps (on a social network for instance), without
knowing or estimating the shape of the activities lifetime. For that purpose,
we introduce a moment matching method that fits the third-order integrated
cumulants of the process. We show on numerical experiments that our approach is
indeed very robust to the shape of the kernels, and gives appealing results on
the MemeTracker database.
On the "Calligraphy" of Books
Authors: Vanessa Q. Marinho, Henrique F. de Arruda, Thales S. Lima, Luciano F. Costa, Diego R. Amancio
Date: 2017-05-29 00:00:00
Tasks: Authorship Attribution

Authorship attribution is a natural language processing task that has been
widely studied, often by considering small order statistics. In this paper, we
explore a complex network approach to assign the authorship of texts based on
their mesoscopic representation, in an attempt to capture the flow of the
narrative. Indeed, as reported in this work, such an approach allowed the
identification of the dominant narrative structure of the studied authors. This
has been achieved due to the ability of the mesoscopic approach to take into
account relationships between different, not necessarily adjacent, parts of the
text, which is able to capture the story flow. The potential of the proposed
approach has been illustrated through principal component analysis, a
comparison with the chance baseline method, and network visualization. Such
visualizations reveal individual characteristics of the authors, which can be
understood as a kind of calligraphy.
Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet
Authors: Jianwen Xie, Song-Chun Zhu, Ying Nian Wu
Date: 2016-06-03 00:00:00
Proceeding: CVPR 2017 7

Video sequences contain rich dynamic patterns, such as dynamic texture
patterns that exhibit stationarity in the temporal domain, and action patterns
that are non-stationary in either spatial or temporal domain. We show that a
spatial-temporal generative ConvNet can be used to model and synthesize dynamic
patterns. The model defines a probability distribution on the video sequence,
and the log probability is defined by a spatial-temporal ConvNet that consists
of multiple layers of spatial-temporal filters to capture spatial-temporal
patterns of different scales. The model can be learned from the training video
sequences by an "analysis by synthesis" learning algorithm that iterates the
following two steps. Step 1 synthesizes video sequences from the currently
learned model. Step 2 then updates the model parameters based on the difference
between the synthesized video sequences and the observed training sequences. We
show that the learning algorithm can synthesize realistic dynamic patterns.
Learning to Generate Chairs with Generative Adversarial Nets
Authors: Evgeny Zamyatin, Andrey Filchenkov
Date: 2017-05-29 00:00:00

Generative adversarial networks (GANs) has gained tremendous popularity
lately due to an ability to reinforce quality of its predictive model with
generated objects and the quality of the generative model with and supervised
feedback. GANs allow to synthesize images with a high degree of realism.
However, the learning process of such models is a very complicated optimization
problem and certain limitation for such models were found. It affects the
choice of certain layers and nonlinearities when designing architectures. In
particular, it does not allow to train convolutional GAN models with
fully-connected hidden layers. In our work, we propose a modification of the
previously described set of rules, as well as new approaches to designing
architectures that will allow us to train more powerful GAN models. We show the
effectiveness of our methods on the problem of synthesizing projections of 3D
objects with the possibility of interpolation by class and view point.
PCM-TV-TFV: A Novel Two Stage Framework for Image Reconstruction from Fourier Data
Authors: Weihong Guo, Guohui Song, Yue Zhang
Date: 2017-05-29 00:00:00
Tasks: Image Reconstruction

We propose in this paper a novel two-stage Projection Correction Modeling
(PCM) framework for image reconstruction from (non-uniform) Fourier
measurements. PCM consists of a projection stage (P-stage) motivated by the
multi-scale Galerkin method and a correction stage (C-stage) with an edge
guided regularity fusing together the advantages of total variation (TV) and
total fractional variation (TFV). The P-stage allows for continuous modeling of
the underlying image of interest. The given measurements are projected onto a
space in which the image is well represented. We then enhance the
reconstruction result at the C-stage that minimizes an energy functional
consisting of a fidelity in the transformed domain and a novel edge guided
regularity. We further develop efficient proximal algorithms to solve the
corresponding optimization problem. Various numerical results in both 1D
signals and 2D images have also been presented to demonstrate the superior
performance of the proposed two-stage method to other classical one-stage
methods.
Solving Almost all Systems of Random Quadratic Equations
Authors: Gang Wang, Georgios B. Giannakis, Yousef Saad, Jie Chen
Date: 2017-05-29 00:00:00
Tasks: All, Computational Efficiency, Retrieval

This paper deals with finding an $n$-dimensional solution $x$ to a system of
quadratic equations of the form $y_i=|\langle{a}_i,x\rangle|^2$ for $1\le i \le
m$, which is also known as phase retrieval and is NP-hard in general. We put
forth a novel procedure for minimizing the amplitude-based least-squares
empirical loss, that starts with a weighted maximal correlation initialization
obtainable with a few power or Lanczos iterations, followed by successive
refinements based upon a sequence of iteratively reweighted (generalized)
gradient iterations. The two (both the initialization and gradient flow) stages
distinguish themselves from prior contributions by the inclusion of a fresh
(re)weighting regularization technique. The overall algorithm is conceptually
simple, numerically scalable, and easy-to-implement. For certain random
measurement models, the novel procedure is shown capable of finding the true
solution $x$ in time proportional to reading the data $\{(a_i;y_i)\}_{1\le i
\le m}$. This holds with high probability and without extra assumption on the
signal $x$ to be recovered, provided that the number $m$ of equations is some
constant $c>0$ times the number $n$ of unknowns in the signal vector, namely,
$m>cn$. Empirically, the upshots of this contribution are: i) (almost) $100\%$
perfect signal recovery in the high-dimensional (say e.g., $n\ge 2,000$) regime
given only an information-theoretic limit number of noiseless equations,
namely, $m=2n-1$ in the real-valued Gaussian case; and, ii) (nearly) optimal
statistical accuracy in the presence of additive noise of bounded support.
Finally, substantial numerical tests using both synthetic data and real images
corroborate markedly improved signal recovery performance and computational
efficiency of our novel procedure relative to state-of-the-art approaches.
Distributed SAGA: Maintaining linear convergence rate with limited communication
Authors: Clément Calauzènes, Nicolas Le Roux
Date: 2017-05-29 00:00:00

In recent years, variance-reducing stochastic methods have shown great
practical performance, exhibiting linear convergence rate when other stochastic
methods offered a sub-linear rate. However, as datasets grow ever bigger and
clusters become widespread, the need for fast distribution methods is pressing.
We propose here a distribution scheme for SAGA which maintains a linear
convergence rate, even when communication between nodes is limited.
Scalable Robust Matrix Recovery: Frank-Wolfe Meets Proximal Methods
Authors: Cun Mu, Yuqian Zhang, John Wright, Donald Goldfarb
Date: 2014-03-29 00:00:00

Recovering matrices from compressive and grossly corrupted observations is a
fundamental problem in robust statistics, with rich applications in computer
vision and machine learning. In theory, under certain conditions, this problem
can be solved in polynomial time via a natural convex relaxation, known as
Compressive Principal Component Pursuit (CPCP). However, all existing provable
algorithms for CPCP suffer from superlinear per-iteration cost, which severely
limits their applicability to large scale problems. In this paper, we propose
provable, scalable and efficient methods to solve CPCP with (essentially)
linear per-iteration cost. Our method combines classical ideas from Frank-Wolfe
and proximal methods. In each iteration, we mainly exploit Frank-Wolfe to
update the low-rank component with rank-one SVD and exploit the proximal step
for the sparse term. Convergence results and implementation details are also
discussed. We demonstrate the scalability of the proposed approach with
promising numerical experiments on visual data.
Model Selection in Bayesian Neural Networks via Horseshoe Priors
Authors: Soumya Ghosh, Finale Doshi-Velez
Date: 2017-05-29 00:00:00
Tasks: Model Selection, Open-Ended Question Answering

Bayesian Neural Networks (BNNs) have recently received increasing attention
for their ability to provide well-calibrated posterior uncertainties. However,
model selection---even choosing the number of nodes---remains an open question.
In this work, we apply a horseshoe prior over node pre-activations of a
Bayesian neural network, which effectively turns off nodes that do not help
explain the data. We demonstrate that our prior prevents the BNN from
under-fitting even when the number of nodes required is grossly over-estimated.
Moreover, this model selection over the number of nodes doesn't come at the
expense of predictive or computational performance; in fact, we learn smaller
networks with comparable predictive performance to current approaches.
Collaborative Deep Learning for Speech Enhancement: A Run-Time Model Selection Method Using Autoencoders
Authors: Minje Kim
Date: 2017-05-29 00:00:00
Tasks: Model Selection, Speech Enhancement

We show that a Modular Neural Network (MNN) can combine various speech
enhancement modules, each of which is a Deep Neural Network (DNN) specialized
on a particular enhancement job. Differently from an ordinary ensemble
technique that averages variations in models, the propose MNN selects the best
module for the unseen test signal to produce a greedy ensemble. We see this as
Collaborative Deep Learning (CDL), because it can reuse various already-trained
DNN models without any further refining. In the proposed MNN selecting the best
module during run time is challenging. To this end, we employ a speech
AutoEncoder (AE) as an arbitrator, whose input and output are trained to be as
similar as possible if its input is clean speech. Therefore, the AE can gauge
the quality of the module-specific denoised result by seeing its AE
reconstruction error, e.g. low error means that the module output is similar to
clean speech. We propose an MNN structure with various modules that are
specialized on dealing with a specific noise type, gender, and input
Signal-to-Noise Ratio (SNR) value, and empirically prove that it almost always
works better than an arbitrarily chosen DNN module and sometimes as good as an
oracle result.
Universal Scalable Robust Solvers from Computational Information Games and fast eigenspace adapted Multiresolution Analysis
Authors: Houman Owhadi, Clint Scovel
Date: 2017-03-31 00:00:00

We show how the discovery of robust scalable numerical solvers for arbitrary
bounded linear operators can be automated as a Game Theory problem by
reformulating the process of computing with partial information and limited
resources as that of playing underlying hierarchies of adversarial information
games. When the solution space is a Banach space $B$ endowed with a quadratic
norm $\|\cdot\|$, the optimal measure (mixed strategy) for such games (e.g. the
adversarial recovery of $u\in B$, given partial measurements $[\phi_i, u]$ with
$\phi_i\in B^*$, using relative error in $\|\cdot\|$-norm as a loss) is a
centered Gaussian field $\xi$ solely determined by the norm $\|\cdot\|$, whose
conditioning (on measurements) produces optimal bets. When measurements are
hierarchical, the process of conditioning this Gaussian field produces a
hierarchy of elementary bets (gamblets). These gamblets generalize the notion
of Wavelets and Wannier functions in the sense that they are adapted to the
norm $\|\cdot\|$ and induce a multi-resolution decomposition of $B$ that is
adapted to the eigensubspaces of the operator defining the norm $\|\cdot\|$.
When the operator is localized, we show that the resulting gamblets are
localized both in space and frequency and introduce the Fast Gamblet Transform
(FGT) with rigorous accuracy and (near-linear) complexity estimates. As the FFT
can be used to solve and diagonalize arbitrary PDEs with constant coefficients,
the FGT can be used to decompose a wide range of continuous linear operators
(including arbitrary continuous linear bijections from $H^s_0$ to $H^{-s}$ or
to $L^2$) into a sequence of independent linear systems with uniformly bounded
condition numbers and leads to $\mathcal{O}(N \operatorname{polylog} N)$
solvers and eigenspace adapted Multiresolution Analysis (resulting in near
linear complexity approximation of all eigensubspaces).
DNN-based uncertainty estimation for weighted DNN-HMM ASR
Authors: José Novoa, Josué Fredes, Néstor Becerra Yoma
Date: 2017-05-29 00:00:00
Tasks: speech-recognition, Speech Recognition

In this paper, the uncertainty is defined as the mean square error between a
given enhanced noisy observation vector and the corresponding clean one. Then,
a DNN is trained by using enhanced noisy observation vectors as input and the
uncertainty as output with a training database. In testing, the DNN receives an
enhanced noisy observation vector and delivers the estimated uncertainty. This
uncertainty in employed in combination with a weighted DNN-HMM based speech
recognition system and compared with an existing estimation of the noise
cancelling uncertainty variance based on an additive noise model. Experiments
were carried out with Aurora-4 task. Results with clean, multi-noise and
multi-condition training are presented.
Free energy-based reinforcement learning using a quantum processor
Authors: Anna Levit, Daniel Crawford, Navid Ghadermarzy, Jaspreet S. Oberoi, Ehsan Zahedinejad, Pooya Ronagh
Date: 2017-05-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent theoretical and experimental results suggest the possibility of using
current and near-future quantum hardware in challenging sampling tasks. In this
paper, we introduce free energy-based reinforcement learning (FERL) as an
application of quantum hardware. We propose a method for processing a quantum
annealer's measured qubit spin configurations in approximating the free energy
of a quantum Boltzmann machine (QBM). We then apply this method to perform
reinforcement learning on the grid-world problem using the D-Wave 2000Q quantum
annealer. The experimental results show that our technique is a promising
method for harnessing the power of quantum sampling in reinforcement learning
tasks.
Neural Embeddings of Graphs in Hyperbolic Space
Authors: Benjamin Paul Chamberlain, James Clough, Marc Peter Deisenroth
Date: 2017-05-29 00:00:00
Tasks: Word Similarity

Neural embeddings have been used with great success in Natural Language
Processing (NLP). They provide compact representations that encapsulate word
similarity and attain state-of-the-art performance in a range of linguistic
tasks. The success of neural embeddings has prompted significant amounts of
research into applications in domains other than language. One such domain is
graph-structured data, where embeddings of vertices can be learned that
encapsulate vertex similarity and improve performance on tasks including edge
prediction and vertex labelling. For both NLP and graph based tasks, embeddings
have been learned in high-dimensional Euclidean spaces. However, recent work
has shown that the appropriate isometric space for embedding complex networks
is not the flat Euclidean space, but negatively curved, hyperbolic space. We
present a new concept that exploits these recent insights and propose learning
neural embeddings of graphs in hyperbolic space. We provide experimental
evidence that embedding graphs in their natural geometry significantly improves
performance on downstream tasks for several real-world public datasets.
Deep Learning for Ontology Reasoning
Authors: Patrick Hohenecker, Thomas Lukasiewicz
Date: 2017-05-29 00:00:00
Tasks: Deep Learning, Relational Reasoning

In this work, we present a novel approach to ontology reasoning that is based
on deep learning rather than logic-based formal reasoning. To this end, we
introduce a new model for statistical relational learning that is built upon
deep recursive neural networks, and give experimental evidence that it can
easily compete with, or even outperform, existing logic-based reasoners on the
task of ontology reasoning. More precisely, we compared our implemented system
with one of the best logic-based ontology reasoners at present, RDFox, on a
number of large standard benchmark datasets, and found that our system attained
high reasoning quality, while being up to two orders of magnitude faster.