pwc-archive/papers-with-abstracts huggingface dataset:
Aligned to the Object, not to the Image: A Unified Pose-aligned Representation for Fine-grained Recognition
Authors: Pei Guo, Ryan Farrell
Date: 2018-01-27 00:00:00
Tasks: Fine-Grained Image Classification, Object, Pose Estimation

Dramatic appearance variation due to pose constitutes a great challenge in
fine-grained recognition, one which recent methods using attention mechanisms
or second-order statistics fail to adequately address. Modern CNNs typically
lack an explicit understanding of object pose and are instead confused by
entangled pose and appearance. In this paper, we propose a unified object
representation built from a hierarchy of pose-aligned regions. Rather than
representing an object by regions aligned to image axes, the proposed
representation characterizes appearance relative to the object's pose using
pose-aligned patches whose features are robust to variations in pose, scale and
rotation. We propose an algorithm that performs pose estimation and forms the
unified object representation as the concatenation of hierarchical pose-aligned
regions features, which is then fed into a classification network. The proposed
algorithm surpasses the performance of other approaches, increasing the
state-of-the-art by nearly 2% on the widely-used CUB-200 dataset and by more
than 8% on the much larger NABirds dataset. The effectiveness of this paradigm
relative to competing methods suggests the critical importance of disentangling
pose and appearance for continued progress in fine-grained recognition.
Exploring the predictability of range-based volatility estimators using RNNs
Authors: Gábor Petneházi, József Gáll
Date: 2018-03-19 00:00:00

We investigate the predictability of several range-based stock volatility
estimators, and compare them to the standard close-to-close estimator which is
most commonly acknowledged as the volatility. The patterns of volatility
changes are analyzed using LSTM recurrent neural networks, which are a state of
the art method of sequence learning. We implement the analysis on all current
constituents of the Dow Jones Industrial Average index, and report averaged
evaluation results. We find that changes in the values of range-based
estimators are more predictable than that of the estimator using daily closing
values only.
All-but-the-Top: Simple and Effective Postprocessing for Word Representations
Authors: Jiaqi Mu, Suma Bhat, Pramod Viswanath
Date: 2017-02-05 00:00:00
Proceeding: ICLR 2018 1
Tasks: All, General Classification, Sentence, Sentiment Analysis, Subjectivity Analysis, Text Classification, Word Similarity

Real-valued word representations have transformed NLP applications; popular
examples are word2vec and GloVe, recognized for their ability to capture
linguistic regularities. In this paper, we demonstrate a {\em very simple}, and
yet counter-intuitive, postprocessing technique -- eliminate the common mean
vector and a few top dominating directions from the word vectors -- that
renders off-the-shelf representations {\em even stronger}. The postprocessing
is empirically validated on a variety of lexical-level intrinsic tasks (word
similarity, concept categorization, word analogy) and sentence-level tasks
(semantic textural similarity and { text classification}) on multiple datasets
and with a variety of representation methods and hyperparameter choices in
multiple languages; in each case, the processed representations are
consistently better than the original ones.
Who is Smarter? Intelligence Measure of Learning-based Cognitive Radios
Authors: Monireh Dabaghchian, Amir Alipour-Fanid, Songsong Liu, Kai Zeng, Xiaohua LI, Yu Chen
Date: 2017-12-26 00:00:00

Cognitive radio (CR) is considered as a key enabling technology for dynamic
spectrum access to improve spectrum efficiency. Although the CR concept was
invented with the core idea of realizing cognition, the research on measuring
CR cognitive capabilities and intelligence is largely open. Deriving the
intelligence measure of CR not only can lead to the development of new CR
technologies, but also makes it possible to better configure the networks by
integrating CRs with different cognitive capabilities. In this paper, for the
first time, we propose a data-driven methodology to quantitatively measure the
intelligence factors of the CR with learning capabilities. The basic idea of
our methodology is to run various tests on the CR in different spectrum
environments under different settings and obtain various performance data on
different metrics. Then we apply factor analysis on the performance data to
identify and quantize the intelligence factors and cognitive capabilities of
the CR. More specifically, we present a case study consisting of 144 different
types of CRs. The CRs are different in terms of learning-based dynamic spectrum
access strategies, number of sensors, sensing accuracy, processing speed, and
algorithmic complexity. Five intelligence factors are identified for the CRs
through our data analysis.We show that these factors comply well with the
nature of the tested CRs, which validates the proposed intelligence measure
methodology.
Online Learning with Randomized Feedback Graphs for Optimal PUE Attacks in Cognitive Radio Networks
Authors: Monireh Dabaghchian, Amir Alipour-Fanid, Kai Zeng, Qingsi Wang, Peter Auer
Date: 2017-09-28 00:00:00

In a cognitive radio network, a secondary user learns the spectrum
environment and dynamically accesses the channel where the primary user is
inactive. At the same time, a primary user emulation (PUE) attacker can send
falsified primary user signals and prevent the secondary user from utilizing
the available channel. The best attacking strategies that an attacker can apply
have not been well studied. In this paper, for the first time, we study optimal
PUE attack strategies by formulating an online learning problem where the
attacker needs to dynamically decide the attacking channel in each time slot
based on its attacking experience. The challenge in our problem is that since
the PUE attack happens in the spectrum sensing phase, the attacker cannot
observe the reward on the attacked channel. To address this challenge, we
utilize the attacker's observation capability. We propose online learning-based
attacking strategies based on the attacker's observation capabilities. Through
our analysis, we show that with no observation within the attacking slot, the
attacker loses on the regret order, and with the observation of at least one
channel, there is a significant improvement on the attacking performance.
Observation of multiple channels does not give additional benefit to the
attacker (only a constant scaling) though it gives insight on the number of
observations required to achieve the minimum constant factor. Our proposed
algorithms are optimal in the sense that their regret upper bounds match their
corresponding regret lower-bounds. We show consistency between simulation and
analytical results under various system parameters.
Statistically Optimal and Computationally Efficient Low Rank Tensor Completion from Noisy Entries
Authors: Dong Xia, Ming Yuan, Cun-Hui Zhang
Date: 2017-11-14 00:00:00

In this article, we develop methods for estimating a low rank tensor from
noisy observations on a subset of its entries to achieve both statistical and
computational efficiencies. There have been a lot of recent interests in this
problem of noisy tensor completion. Much of the attention has been focused on
the fundamental computational challenges often associated with problems
involving higher order tensors, yet very little is known about their
statistical performance. To fill in this void, in this article, we characterize
the fundamental statistical limits of noisy tensor completion by establishing
minimax optimal rates of convergence for estimating a $k$th order low rank
tensor under the general $\ell_p$ ($1\le p\le 2$) norm which suggest
significant room for improvement over the existing approaches. Furthermore, we
propose a polynomial-time computable estimating procedure based upon power
iteration and a second-order spectral initialization that achieves the optimal
rates of convergence. Our method is fairly easy to implement and numerical
experiments are presented to further demonstrate the practical merits of our
estimator.
Visual Psychophysics for Making Face Recognition Algorithms More Explainable
Authors: Brandon RichardWebster, So Yon Kwon, Christopher Clarizio, Samuel E. Anthony, Walter J. Scheirer
Date: 2018-03-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Recognition

Scientific fields that are interested in faces have developed their own sets
of concepts and procedures for understanding how a target model system (be it a
person or algorithm) perceives a face under varying conditions. In computer
vision, this has largely been in the form of dataset evaluation for recognition
tasks where summary statistics are used to measure progress. While aggregate
performance has continued to improve, understanding individual causes of
failure has been difficult, as it is not always clear why a particular face
fails to be recognized, or why an impostor is recognized by an algorithm.
Importantly, other fields studying vision have addressed this via the use of
visual psychophysics: the controlled manipulation of stimuli and careful study
of the responses they evoke in a model system. In this paper, we suggest that
visual psychophysics is a viable methodology for making face recognition
algorithms more explainable. A comprehensive set of procedures is developed for
assessing face recognition algorithm behavior, which is then deployed over
state-of-the-art convolutional neural networks and more basic, yet still widely
used, shallow and handcrafted feature-based approaches.
Meta-Tracker: Fast and Robust Online Adaptation for Visual Object Trackers
Authors: Eunbyung Park, Alexander C. Berg
Date: 2018-01-09 00:00:00
Proceeding: ECCV 2018 9
Tasks: Meta-Learning

This paper improves state-of-the-art visual object trackers that use online
adaptation. Our core contribution is an offline meta-learning-based method to
adjust the initial deep networks used in online adaptation-based tracking. The
meta learning is driven by the goal of deep networks that can quickly be
adapted to robustly model a particular target in future frames. Ideally the
resulting models focus on features that are useful for future frames, and avoid
overfitting to background clutter, small parts of the target, or noise. By
enforcing a small number of update iterations during meta-learning, the
resulting networks train significantly faster. We demonstrate this approach on
top of the high performance tracking approaches: tracking-by-detection based
MDNet and the correlation based CREST. Experimental results on standard
benchmarks, OTB2015 and VOT2016, show that our meta-learned versions of both
trackers improve speed, accuracy, and robustness.
Dynamic Natural Language Processing with Recurrence Quantification Analysis
Authors: Rick Dale, Nicholas D. Duran, Moreno Coco
Date: 2018-03-19 00:00:00
Tasks: Time Series, Time Series Analysis

Writing and reading are dynamic processes. As an author composes a text, a
sequence of words is produced. This sequence is one that, the author hopes,
causes a revisitation of certain thoughts and ideas in others. These processes
of composition and revisitation by readers are ordered in time. This means that
text itself can be investigated under the lens of dynamical systems. A common
technique for analyzing the behavior of dynamical systems, known as recurrence
quantification analysis (RQA), can be used as a method for analyzing sequential
structure of text. RQA treats text as a sequential measurement, much like a
time series, and can thus be seen as a kind of dynamic natural language
processing (NLP). The extension has several benefits. Because it is part of a
suite of time series analysis tools, many measures can be extracted in one
common framework. Secondly, the measures have a close relationship with some
commonly used measures from natural language processing. Finally, using
recurrence analysis offers an opportunity expand analysis of text by developing
theoretical descriptions derived from complex dynamic systems. We showcase an
example analysis on 8,000 texts from the Gutenberg Project, compare it to
well-known NLP approaches, and describe an R package (crqanlp) that can be used
in conjunction with R library crqa.
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
Authors: Arslan Chaudhry, Puneet K. Dokania, Thalaiyasingam Ajanthan, Philip H. S. Torr
Date: 2018-01-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Incremental Learning

Incremental learning (IL) has received a lot of attention recently, however,
the literature lacks a precise problem definition, proper evaluation settings,
and metrics tailored specifically for the IL problem. One of the main
objectives of this work is to fill these gaps so as to provide a common ground
for better understanding of IL. The main challenge for an IL algorithm is to
update the classifier whilst preserving existing knowledge. We observe that, in
addition to forgetting, a known issue while preserving knowledge, IL also
suffers from a problem we call intransigence, inability of a model to update
its knowledge. We introduce two metrics to quantify forgetting and
intransigence that allow us to understand, analyse, and gain better insights
into the behaviour of IL algorithms. We present RWalk, a generalization of
EWC++ (our efficient version of EWC [Kirkpatrick2016EWC]) and Path Integral
[Zenke2017Continual] with a theoretically grounded KL-divergence based
perspective. We provide a thorough analysis of various IL algorithms on MNIST
and CIFAR-100 datasets. In these experiments, RWalk obtains superior results in
terms of accuracy, and also provides a better trade-off between forgetting and
intransigence.
A Survey on Neural Network-Based Summarization Methods
Authors: Yue Dong
Date: 2018-03-19 00:00:00
Tasks: Survey, Text Summarization

Automatic text summarization, the automated process of shortening a text
while reserving the main ideas of the document(s), is a critical research area
in natural language processing. The aim of this literature review is to survey
the recent work on neural-based models in automatic text summarization. We
examine in detail ten state-of-the-art neural-based summarizers: five
abstractive models and five extractive models. In addition, we discuss the
related techniques that can be applied to the summarization tasks and present
promising paths for future research in neural-based summarization.
Zero-Shot Detection
Authors: Pengkai Zhu, Hanxiao Wang, Venkatesh Saligrama
Date: 2018-03-19 00:00:00
Tasks: Attribute, Object, object-detection, Object Detection

As we move towards large-scale object detection, it is unrealistic to expect
annotated training data, in the form of bounding box annotations around
objects, for all object classes at sufficient scale, and so methods capable of
unseen object detection are required. We propose a novel zero-shot method based
on training an end-to-end model that fuses semantic attribute prediction with
visual features to propose object bounding boxes for seen and unseen classes.
While we utilize semantic features during training, our method is agnostic to
semantic information for unseen classes at test-time. Our method retains the
efficiency and effectiveness of YOLOv2 for objects seen during training, while
improving its performance for novel and unseen objects. The ability of
state-of-art detection methods to learn discriminative object features to
reject background proposals also limits their performance for unseen objects.
We posit that, to detect unseen objects, we must incorporate semantic
information into the visual domain so that the learned visual features reflect
this information and leads to improved recall rates for unseen objects. We test
our method on PASCAL VOC and MS COCO dataset and observed significant
improvements on the average precision of unseen classes.
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process
Authors: Gonzalo Rios, Felipe Tobar
Date: 2018-03-19 00:00:00
Tasks: Gaussian Processes, Time Series, Time Series Analysis

Gaussian processes (GPs) are Bayesian nonparametric generative models that
provide interpretability of hyperparameters, admit closed-form expressions for
training and inference, and are able to accurately represent uncertainty. To
model general non-Gaussian data with complex correlation structure, GPs can be
paired with an expressive covariance kernel and then fed into a nonlinear
transformation (or warping). However, overparametrising the kernel and the
warping is known to, respectively, hinder gradient-based training and make the
predictions computationally expensive. We remedy this issue by (i) training the
model using derivative-free global-optimisation techniques so as to find
meaningful maxima of the model likelihood, and (ii) proposing a warping
function based on the celebrated Box-Cox transformation that requires minimal
numerical approximations---unlike existing warped GP models. We validate the
proposed approach by first showing that predictions can be computed
analytically, and then on a learning, reconstruction and forecasting experiment
using real-world datasets.
VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition
Authors: Jiawei Chen, Janusz Konrad, Prakash Ishwar
Date: 2018-03-19 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Generative Adversarial Network, Image Generation, Privacy Preserving, Representation Learning

Reliable facial expression recognition plays a critical role in human-machine
interactions. However, most of the facial expression analysis methodologies
proposed to date pay little or no attention to the protection of a user's
privacy. In this paper, we propose a Privacy-Preserving Representation-Learning
Variational Generative Adversarial Network (PPRL-VGAN) to learn an image
representation that is explicitly disentangled from the identity information.
At the same time, this representation is discriminative from the standpoint of
facial expression recognition and generative as it allows expression-equivalent
face image synthesis. We evaluate the proposed model on two public datasets
under various threat scenarios. Quantitative and qualitative results
demonstrate that our approach strikes a balance between the preservation of
privacy and data utility. We further demonstrate that our model can be
effectively applied to other tasks such as expression morphing and image
completion.
Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks
Authors: Qi Liu, Tao Liu, Zihao Liu, Yanzhi Wang, Yier Jin, Wujie Wen
Date: 2018-02-14 00:00:00
Tasks: Adversarial Attack

DNN is presenting human-level performance for many complex intelligent tasks
in real-world applications. However, it also introduces ever-increasing
security concerns. For example, the emerging adversarial attacks indicate that
even very small and often imperceptible adversarial input perturbations can
easily mislead the cognitive function of deep learning systems (DLS). Existing
DNN adversarial studies are narrowly performed on the ideal software-level DNN
models with a focus on single uncertainty factor, i.e. input perturbations,
however, the impact of DNN model reshaping on adversarial attacks, which is
introduced by various hardware-favorable techniques such as hash-based weight
compression during modern DNN hardware implementation, has never been
discussed. In this work, we for the first time investigate the multi-factor
adversarial attack problem in practical model optimized deep learning systems
by jointly considering the DNN model-reshaping (e.g. HashNet based deep
compression) and the input perturbations. We first augment adversarial example
generating method dedicated to the compressed DNN models by incorporating the
software-based approaches and mathematical modeled DNN reshaping. We then
conduct a comprehensive robustness and vulnerability analysis of deep
compressed DNN models under derived adversarial attacks. A defense technique
named "gradient inhibition" is further developed to ease the generating of
adversarial examples thus to effectively mitigate adversarial attacks towards
both software and hardware-oriented DNNs. Simulation results show that
"gradient inhibition" can decrease the average success rate of adversarial
attacks from 87.99% to 4.77% (from 86.74% to 4.64%) on MNIST (CIFAR-10)
benchmark with marginal accuracy degradation across various DNNs.
Setting up a Reinforcement Learning Task with a Real-World Robot
Authors: A. Rupam Mahmood, Dmytro Korenkevych, Brent J. Komer, James Bergstra
Date: 2018-03-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning is a promising approach to developing hard-to-engineer
adaptive solutions for complex and diverse robotic tasks. However, learning
with real-world robots is often unreliable and difficult, which resulted in
their low adoption in reinforcement learning research. This difficulty is
worsened by the lack of guidelines for setting up learning tasks with robots.
In this work, we develop a learning task with a UR5 robotic arm to bring to
light some key elements of a task setup and study their contributions to the
challenges with robots. We find that learning performance can be highly
sensitive to the setup, and thus oversights and omissions in setup details can
make effective learning, reproducibility, and fair comparison hard. Our study
suggests some mitigating steps to help future experimenters avoid difficulties
and pitfalls. We show that highly reliable and repeatable experiments can be
performed in our setup, indicating the possibility of reinforcement learning
research extensively based on real-world robots.
Learning Region Features for Object Detection
Authors: Jiayuan Gu, Han Hu, Li-Wei Wang, Yichen Wei, Jifeng Dai
Date: 2018-03-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, object-detection, Object Detection

While most steps in the modern object detection methods are learnable, the
region feature extraction step remains largely hand-crafted, featured by RoI
pooling methods. This work proposes a general viewpoint that unifies existing
region feature extraction methods and a novel method that is end-to-end
learnable. The proposed method removes most heuristic choices and outperforms
its RoI pooling counterparts. It moves further towards fully learnable object
detection.
Optimal Auctions through Deep Learning: Advances in Differentiable Economics
Authors: Paul Dütting, Zhe Feng, Harikrishna Narasimhan, David C. Parkes, Sai Srivatsa Ravindranath
Date: 2017-06-12 00:00:00
Tasks: Deep Learning, Generalization Bounds

Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981, but more than 40 years later a full analytical understanding of the optimal design still remains elusive for settings with two or more items. In this work, we initiate the exploration of the use of tools from deep learning for the automated design of optimal auctions. We model an auction as a multi-layer neural network, frame optimal auction design as a constrained learning problem, and show how it can be solved using standard machine learning pipelines. In addition to providing generalization bounds, we present extensive experimental results, recovering essentially all known solutions that come from the theoretical analysis of optimal auction design problems and obtaining novel mechanisms for settings in which the optimal mechanism is unknown.