pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
Authors: Łukasz Kidziński, Sharada Prasanna Mohanty, Carmichael Ong, Zhewei Huang, Shuchang Zhou, Anton Pechenko, Adam Stelmaszczyk, Piotr Jarosik, Mikhail Pavlov, Sergey Kolesnikov, Sergey Plis, Zhibo Chen, Zhizheng Zhang, Jiale Chen, Jun Shi, Zhuobin Zheng, Chun Yuan, Zhihui Lin, Henryk Michalewski, Piotr Miłoś, Błażej Osiński, Andrew Melnik, Malte Schilling, Helge Ritter, Sean Carroll, Jennifer Hicks, Sergey Levine, Marcel Salathé, Scott Delp
Date: 2018-04-02 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In the NIPS 2017 Learning to Run challenge, participants were tasked with
building a controller for a musculoskeletal model to make it run as fast as
possible through an obstacle course. Top participants were invited to describe
their algorithms. In this work, we present eight solutions that used deep
reinforcement learning approaches, based on algorithms such as Deep
Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region
Policy Optimization. Many solutions use similar relaxations and heuristics,
such as reward shaping, frame skipping, discretization of the action space,
symmetry, and policy blending. However, each of the eight teams implemented
different modifications of the known algorithms.
Artificial Intelligence and its Role in Near Future
Authors: Jahanzaib Shabbir, Tarique Anwer
Date: 2018-04-01 00:00:00

AI technology has a long history which is actively and constantly changing
and growing. It focuses on intelligent agents, which contain devices that
perceive the environment and based on which takes actions in order to maximize
goal success chances. In this paper, we will explain the modern AI basics and
various representative applications of AI. In the context of the modern
digitalized world, AI is the property of machines, computer programs, and
systems to perform the intellectual and creative functions of a person,
independently find ways to solve problems, be able to draw conclusions and make
decisions. Most artificial intelligence systems have the ability to learn,
which allows people to improve their performance over time. The recent research
on AI tools, including machine learning, deep learning and predictive analysis
intended toward increasing the planning, learning, reasoning, thinking and
action taking ability. Based on which, the proposed research intends towards
exploring on how the human intelligence differs from the artificial
intelligence. Moreover, we critically analyze what AI of today is capable of
doing, why it still cannot reach human intelligence and what are the open
challenges existing in front of AI to reach and outperform human level of
intelligence. Furthermore, it will explore the future predictions for
artificial intelligence and based on which potential solution will be
recommended to solve it within next decades.
Is Second-order Information Helpful for Large-scale Visual Recognition?
Authors: Peihua Li, Jiangtao Xie, Qilong Wang, WangMeng Zuo
Date: 2017-03-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object Recognition

By stacking layers of convolution and nonlinearity, convolutional networks
(ConvNets) effectively learn from low-level to high-level features and
discriminative representations. Since the end goal of large-scale recognition
is to delineate complex boundaries of thousands of classes, adequate
exploration of feature distributions is important for realizing full potentials
of ConvNets. However, state-of-the-art works concentrate only on deeper or
wider architecture design, while rarely exploring feature statistics higher
than first-order. We take a step towards addressing this problem. Our method
consists in covariance pooling, instead of the most commonly used first-order
pooling, of high-level convolutional features. The main challenges involved are
robust covariance estimation given a small sample of large-dimensional features
and usage of the manifold structure of covariance matrices. To address these
challenges, we present a Matrix Power Normalized Covariance (MPN-COV) method.
We develop forward and backward propagation formulas regarding the nonlinear
matrix functions such that MPN-COV can be trained end-to-end. In addition, we
analyze both qualitatively and quantitatively its advantage over the well-known
Log-Euclidean metric. On the ImageNet 2012 validation set, by combining MPN-COV
we achieve over 4%, 3% and 2.5% gains for AlexNet, VGG-M and VGG-16,
respectively; integration of MPN-COV into 50-layer ResNet outperforms
ResNet-101 and is comparable to ResNet-152. The source code will be available
on the project page: http://www.peihuali.org/MPN-COV
Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization
Authors: Peihua Li, Jiangtao Xie, Qilong Wang, Zilin Gao
Date: 2017-12-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Fine-Grained Image Classification, Fine-Grained Image Recognition, GPU, Image Classification

Global covariance pooling in convolutional neural networks has achieved
impressive improvement over the classical first-order pooling. Recent works
have shown matrix square root normalization plays a central role in achieving
state-of-the-art performance. However, existing methods depend heavily on
eigendecomposition (EIG) or singular value decomposition (SVD), suffering from
inefficient training due to limited support of EIG and SVD on GPU. Towards
addressing this problem, we propose an iterative matrix square root
normalization method for fast end-to-end training of global covariance pooling
networks. At the core of our method is a meta-layer designed with loop-embedded
directed graph structure. The meta-layer consists of three consecutive
nonlinear structured layers, which perform pre-normalization, coupled matrix
iteration and post-compensation, respectively. Our method is much faster than
EIG or SVD based ones, since it involves only matrix multiplications, suitable
for parallel implementation on GPU. Moreover, the proposed network with ResNet
architecture can converge in much less epochs, further accelerating network
training. On large-scale ImageNet, we achieve competitive performance superior
to existing counterparts. By finetuning our models pre-trained on ImageNet, we
establish state-of-the-art results on three challenging fine-grained
benchmarks. The source code and network models will be available at
http://www.peihuali.org/iSQRT-COV
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
Authors: Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser Nam Lim, Rama Chellappa
Date: 2017-11-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Semantic Segmentation

Visual Domain Adaptation is a problem of immense importance in computer
vision. Previous approaches showcase the inability of even deep neural networks
to learn informative representations across domain shift. This problem is more
severe for tasks where acquiring hand labeled data is extremely hard and
tedious. In this work, we focus on adapting the representations learned by
segmentation networks across synthetic and real domains. Contrary to previous
approaches that use a simple adversarial objective or superpixel information to
aid the process, we propose an approach based on Generative Adversarial
Networks (GANs) that brings the embeddings closer in the learned feature space.
To showcase the generality and scalability of our approach, we show that we can
achieve state of the art results on two challenging scenarios of synthetic to
real domain adaptation. Additional exploratory experiments show that our
approach: (1) generalizes to unseen domains and (2) results in improved
alignment of source and target distributions.
Unsupervised Correlation Analysis
Authors: Yedid Hoshen, Lior Wolf
Date: 2018-04-01 00:00:00
Proceeding: CVPR 2018 6

Linking between two data sources is a basic building block in numerous
computer vision problems. In this paper, we set to answer a fundamental
cognitive question: are prior correspondences necessary for linking between
different domains?
  One of the most popular methods for linking between domains is Canonical
Correlation Analysis (CCA). All current CCA algorithms require correspondences
between the views. We introduce a new method Unsupervised Correlation Analysis
(UCA), which requires no prior correspondences between the two domains. The
correlation maximization term in CCA is replaced by a combination of a
reconstruction term (similar to autoencoders), full cycle loss, orthogonality
and multiple domain confusion terms. Due to lack of supervision, the
optimization leads to multiple alternative solutions with similar scores and we
therefore introduce a consensus-based mechanism that is often able to recover
the desired solution. Remarkably, this suffices in order to link remote domains
such as text and images. We also present results on well accepted CCA
benchmarks, showing that performance far exceeds other unsupervised baselines,
and approaches supervised performance in some cases.
Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series
Authors: Feras A. Saad, Vikash K. Mansinghka
Date: 2017-10-18 00:00:00
Tasks: Clustering, Imputation, Time Series, Time Series Analysis

This article proposes a Bayesian nonparametric method for forecasting,
imputation, and clustering in sparsely observed, multivariate time series data.
The method is appropriate for jointly modeling hundreds of time series with
widely varying, non-stationary dynamics. Given a collection of $N$ time series,
the Bayesian model first partitions them into independent clusters using a
Chinese restaurant process prior. Within a cluster, all time series are modeled
jointly using a novel "temporally-reweighted" extension of the Chinese
restaurant process mixture. Markov chain Monte Carlo techniques are used to
obtain samples from the posterior distribution, which are then used to form
predictive inferences. We apply the technique to challenging forecasting and
imputation tasks using seasonal flu data from the US Center for Disease Control
and Prevention, demonstrating superior forecasting accuracy and competitive
imputation accuracy as compared to multiple widely used baselines. We further
show that the model discovers interpretable clusters in datasets with hundreds
of time series, using macroeconomic data from the Gapminder Foundation.
Sparse Principal Component Analysis via Variable Projection
Authors: N. Benjamin Erichson, Peng Zheng, Krithika Manohar, Steven L. Brunton, J. Nathan Kutz, Aleksandr Y. Aravkin
Date: 2018-04-01 00:00:00
Tasks: Computational Efficiency, Diagnostic

Sparse principal component analysis (SPCA) has emerged as a powerful technique for modern data analysis, providing improved interpretation of low-rank structures by identifying localized spatial structures in the data and disambiguating between distinct time scales. We demonstrate a robust and scalable SPCA algorithm by formulating it as a value-function optimization problem. This viewpoint leads to a flexible and computationally efficient algorithm. Further, we can leverage randomized methods from linear algebra to extend the approach to the large-scale (big data) setting. Our proposed innovation also allows for a robust SPCA formulation which obtains meaningful sparse principal components in spite of grossly corrupted input data. The proposed algorithms are demonstrated using both synthetic and real world data, and show exceptional computational efficiency and diagnostic performance.
Probabilistic Formulations of Regression with Mixed Guidance
Authors: Aubrey Gress, Ian Davidson
Date: 2018-04-01 00:00:00
Tasks: regression

Regression problems assume every instance is annotated (labeled) with a real
value, a form of annotation we call \emph{strong guidance}. In order for these
annotations to be accurate, they must be the result of a precise experiment or
measurement. However, in some cases additional \emph{weak guidance} might be
given by imprecise measurements, a domain expert or even crowd sourcing.
Current formulations of regression are unable to use both types of guidance. We
propose a regression framework that can also incorporate weak guidance based on
relative orderings, bounds, neighboring and similarity relations. Consider
learning to predict ages from portrait images, these new types of guidance
allow weaker forms of guidance such as stating a person is in their 20s or two
people are similar in age. These types of annotations can be easier to generate
than strong guidance. We introduce a probabilistic formulation for these forms
of weak guidance and show that the resulting optimization problems are convex.
Our experimental results show the benefits of these formulations on several
data sets.
Toward Intelligent Vehicular Networks: A Machine Learning Framework
Authors: Le Liang, Hao Ye, Geoffrey Ye Li
Date: 2018-04-01 00:00:00
Tasks: BIG-bench Machine Learning, Reinforcement Learning

As wireless networks evolve towards high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies. Future intelligent vehicles, which are at the heart of high mobility networks, are increasingly equipped with multiple advanced onboard sensors and keep generating large volumes of data. Machine learning, as an effective approach to artificial intelligence, can provide a rich set of tools to exploit such data for the benefit of the networks. In this article, we first identify the distinctive characteristics of high mobility vehicular networks and motivate the use of machine learning to address the resulting challenges. After a brief introduction of the major concepts of machine learning, we discuss its applications to learn the dynamics of vehicular networks and make informed decisions to optimize network performance. In particular, we discuss in greater detail the application of reinforcement learning in managing network resources as an alternative to the prevalent optimization approach. Finally, some open issues worth further investigation are highlighted.
Person Re-identification Using Visual Attention
Authors: Alireza Rahimpour, Liu Liu, Ali Taalimi, Yang song, Hairong Qi
Date: 2017-07-23 00:00:00
Tasks: Person Re-Identification

Despite recent attempts for solving the person re-identification problem, it
remains a challenging task since a person's appearance can vary significantly
when large variations in view angle, human pose, and illumination are involved.
In this paper, we propose a novel approach based on using a gradient-based
attention mechanism in deep convolution neural network for solving the person
re-identification problem. Our model learns to focus selectively on parts of
the input image for which the networks' output is most sensitive to and
processes them with high resolution while perceiving the surrounding image in
low resolution. Extensive comparative evaluations demonstrate that the proposed
method outperforms state-of-the-art approaches on the challenging CUHK01,
CUHK03, and Market 1501 datasets.
Online learning with graph-structured feedback against adaptive adversaries
Authors: Zhili Feng, Po-Ling Loh
Date: 2018-04-01 00:00:00

We derive upper and lower bounds for the policy regret of $T$-round online
learning problems with graph-structured feedback, where the adversary is
nonoblivious but assumed to have a bounded memory. We obtain upper bounds of
$\widetilde O(T^{2/3})$ and $\widetilde O(T^{3/4})$ for strongly-observable and
weakly-observable graphs, respectively, based on analyzing a variant of the
Exp3 algorithm. When the adversary is allowed a bounded memory of size 1, we
show that a matching lower bound of $\widetilde\Omega(T^{2/3})$ is achieved in
the case of full-information feedback. We also study the particular loss
structure of an oblivious adversary with switching costs, and show that in such
a setting, non-revealing strongly-observable feedback graphs achieve a lower
bound of $\widetilde\Omega(T^{2/3})$, as well.
Substitute Teacher Networks: Learning with Almost No Supervision
Authors: Samuel Albanie, James Thewlis, Joao F. Henriques
Date: 2018-04-01 00:00:00

Learning through experience is time-consuming, inefficient and often bad for
your cortisol levels. To address this problem, a number of recently proposed
teacher-student methods have demonstrated the benefits of private tuition, in
which a single model learns from an ensemble of more experienced tutors.
Unfortunately, the cost of such supervision restricts good representations to a
privileged minority. Unsupervised learning can be used to lower tuition fees,
but runs the risk of producing networks that require extracurriculum learning
to strengthen their CVs and create their own LinkedIn profiles. Inspired by the
logo on a promotional stress ball at a local recruitment fair, we make the
following three contributions. First, we propose a novel almost no supervision
training algorithm that is effective, yet highly scalable in the number of
student networks being supervised, ensuring that education remains affordable.
Second, we demonstrate our approach on a typical use case: learning to bake,
developing a method that tastily surpasses the current state of the art.
Finally, we provide a rigorous quantitive analysis of our method, proving that
we have access to a calculator. Our work calls into question the long-held
dogma that life is the best teacher.
DeepFense: Online Accelerated Defense Against Adversarial Deep Learning
Authors: Bita Darvish Rouhani, Mohammad Samragh, Mojan Javaheripi, Tara Javidi, Farinaz Koushanfar
Date: 2017-09-08 00:00:00
Tasks: Deep Learning

Recent advances in adversarial Deep Learning (DL) have opened up a largely
unexplored surface for malicious attacks jeopardizing the integrity of
autonomous DL systems. With the wide-spread usage of DL in critical and
time-sensitive applications, including unmanned vehicles, drones, and video
surveillance systems, online detection of malicious inputs is of utmost
importance. We propose DeepFense, the first end-to-end automated framework that
simultaneously enables efficient and safe execution of DL models. DeepFense
formalizes the goal of thwarting adversarial attacks as an optimization problem
that minimizes the rarely observed regions in the latent feature space spanned
by a DL network. To solve the aforementioned minimization problem, a set of
complementary but disjoint modular redundancies are trained to validate the
legitimacy of the input samples in parallel with the victim DL model. DeepFense
leverages hardware/software/algorithm co-design and customized acceleration to
achieve just-in-time performance in resource-constrained settings. The proposed
countermeasure is unsupervised, meaning that no adversarial sample is leveraged
to train modular redundancies. We further provide an accompanying API to reduce
the non-recurring engineering cost and ensure automated adaptation to various
platforms. Extensive evaluations on FPGAs and GPUs demonstrate up to two orders
of magnitude performance improvement while enabling online adversarial sample
detection.
Aggregated Momentum: Stability Through Passive Damping
Authors: James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse
Date: 2018-04-01 00:00:00
Proceeding: ICLR 2019 5

Momentum is a simple and widely used trick which allows gradient-based
optimizers to pick up speed along low curvature directions. Its performance
depends crucially on a damping coefficient $\beta$. Large $\beta$ values can
potentially deliver much larger speedups, but are prone to oscillations and
instability; hence one typically resorts to small values such as 0.5 or 0.9. We
propose Aggregated Momentum (AggMo), a variant of momentum which combines
multiple velocity vectors with different $\beta$ parameters. AggMo is trivial
to implement, but significantly dampens oscillations, enabling it to remain
stable even for aggressive $\beta$ values such as 0.999. We reinterpret
Nesterov's accelerated gradient descent as a special case of AggMo and analyze
rates of convergence for quadratic objectives. Empirically, we find that AggMo
is a suitable drop-in replacement for other momentum methods, and frequently
delivers faster convergence.
Domain transfer convolutional attribute embedding
Authors: Fang Su, Jing-Yan Wang
Date: 2018-03-26 00:00:00
Tasks: Attribute, General Classification, Person Re-Identification, Transfer Learning

In this paper, we study the problem of transfer learning with the attribute
data. In the transfer learning problem, we want to leverage the data of the
auxiliary and the target domains to build an effective model for the
classification problem in the target domain. Meanwhile, the attributes are
naturally stable cross different domains. This strongly motives us to learn
effective domain transfer attribute representations. To this end, we proposed
to embed the attributes of the data to a common space by using the powerful
convolutional neural network (CNN) model. The convolutional representations of
the data points are mapped to the corresponding attributes so that they can be
effective embedding of the attributes. We also represent the data of different
domains by a domain-independent CNN, ant a domain-specific CNN, and combine
their outputs with the attribute embedding to build the classification model.
An joint learning framework is constructed to minimize the classification
errors, the attribute mapping error, the mismatching of the domain-independent
representations cross different domains, and to encourage the the neighborhood
smoothness of representations in the target domain. The minimization problem is
solved by an iterative algorithm based on gradient descent. Experiments over
benchmark data sets of person re-identification, bankruptcy prediction, and
spam email detection, show the effectiveness of the proposed method.
Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension
Authors: Chia-Hsuan Li, Szu-Lin Wu, Chi-Liang Liu, Hung-Yi Lee
Date: 2018-04-01 00:00:00
Tasks: Question Answering, Reading Comprehension, speech-recognition, Speech Recognition, Spoken Language Understanding

Reading comprehension has been widely studied. One of the most representative
reading comprehension tasks is Stanford Question Answering Dataset (SQuAD), on
which machine is already comparable with human. On the other hand, accessing
large collections of multimedia or spoken content is much more difficult and
time-consuming than plain text content for humans. It's therefore highly
attractive to develop machines which can automatically understand spoken
content. In this paper, we propose a new listening comprehension task - Spoken
SQuAD. On the new task, we found that speech recognition errors have
catastrophic impact on machine comprehension, and several approaches are
proposed to mitigate the impact.