pwc-archive/papers-with-abstracts huggingface dataset:
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
Authors: Yen-Chen Lin, Zhang-Wei Hong, Yuan-Hong Liao, Meng-Li Shih, Ming-Yu Liu, Min Sun
Date: 2017-03-08 00:00:00
Tasks: Adversarial Attack, Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce two tactics to attack agents trained by deep reinforcement learning algorithms using adversarial examples, namely the strategically-timed attack and the enchanting attack. In the strategically-timed attack, the adversary aims at minimizing the agent's reward by only attacking the agent at a small subset of time steps in an episode. Limiting the attack activity to this subset helps prevent detection of the attack by the agent. We propose a novel method to determine when an adversarial example should be crafted and applied. In the enchanting attack, the adversary aims at luring the agent to a designated target state. This is achieved by combining a generative model and a planning algorithm: while the generative model predicts the future states, the planning algorithm generates a preferred sequence of actions for luring the agent. A sequence of adversarial examples is then crafted to lure the agent to take the preferred sequence of actions. We apply the two tactics to the agents trained by the state-of-the-art deep reinforcement learning algorithm including DQN and A3C. In 5 Atari games, our strategically timed attack reduces as much reward as the uniform attack (i.e., attacking at every time step) does by attacking the agent 4 times less often. Our enchanting attack lures the agent toward designated target states with a more than 70% success rate. Videos are available at http://yenchenlin.me/adversarial_attack_RL/
Learning deep structured network for weakly supervised change detection
Authors: Salman H. Khan, Xuming He, Fatih Porikli, Mohammed Bennamoun, Ferdous Sohel, Roberto Togneri
Date: 2016-06-07 00:00:00
Tasks: Change Detection

Conventional change detection methods require a large number of images to
learn background models or depend on tedious pixel-level labeling by humans. In
this paper, we present a weakly supervised approach that needs only image-level
labels to simultaneously detect and localize changes in a pair of images. To
this end, we employ a deep neural network with DAG topology to learn patterns
of change from image-level labeled training data. On top of the initial CNN
activations, we define a CRF model to incorporate the local differences and
context with the dense connections between individual pixels. We apply a
constrained mean-field algorithm to estimate the pixel-level labels, and use
the estimated labels to update the parameters of the CNN in an iterative EM
framework. This enables imposing global constraints on the observed foreground
probability mass function. Our evaluations on four benchmark datasets
demonstrate superior detection and localization performance.
Compressing Recurrent Neural Network with Tensor Train
Authors: Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Date: 2017-05-23 00:00:00

Recurrent Neural Network (RNN) are a popular choice for modeling temporal and
sequential tasks and achieve many state-of-the-art performance on various
complex problems. However, most of the state-of-the-art RNNs have millions of
parameters and require many computational resources for training and predicting
new data. This paper proposes an alternative RNN model to reduce the number of
parameters significantly by representing the weight parameters based on Tensor
Train (TT) format. In this paper, we implement the TT-format representation for
several RNN architectures such as simple RNN and Gated Recurrent Unit (GRU). We
compare and evaluate our proposed RNN model with uncompressed RNN model on
sequence classification and sequence prediction tasks. Our proposed RNNs with
TT-format are able to preserve the performance while reducing the number of RNN
parameters significantly up to 40 times smaller.
Wasserstein Learning of Deep Generative Point Process Models
Authors: Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye, Junchi Yan, Le Song, Hongyuan Zha
Date: 2017-05-23 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Point Processes

Point processes are becoming very popular in modeling asynchronous sequential
data due to their sound mathematical foundation and strength in modeling a
variety of real-world phenomena. Currently, they are often characterized via
intensity function which limits model's expressiveness due to unrealistic
assumptions on its parametric form used in practice. Furthermore, they are
learned via maximum likelihood approach which is prone to failure in
multi-modal distributions of sequences. In this paper, we propose an
intensity-free approach for point processes modeling that transforms nuisance
processes to a target one. Furthermore, we train the model using a
likelihood-free leveraging Wasserstein distance between point processes.
Experiments on various synthetic and real-world data substantiate the
superiority of the proposed point process model over conventional ones.
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification
Authors: Wei Li, Xiatian Zhu, Shaogang Gong
Date: 2017-05-12 00:00:00
Tasks: feature selection, General Classification, Person Re-Identification

Existing person re-identification (re-id) methods rely mostly on either
localised or global feature representation alone. This ignores their joint
benefit and mutual complementary effects. In this work, we show the advantages
of jointly learning local and global features in a Convolutional Neural Network
(CNN) by aiming to discover correlated local and global features in different
context. Specifically, we formulate a method for joint learning of local and
global feature selection losses designed to optimise person re-id when using
only generic matching metrics such as the L2 distance. We design a novel CNN
architecture for Jointly Learning Multi-Loss (JLML) of local and global
discriminative feature optimisation subject concurrently to the same re-id
labelled information. Extensive comparative evaluations demonstrate the
advantages of this new JLML model for person re-id over a wide range of
state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03,
Market-1501).
Neural Network Memory Architectures for Autonomous Robot Navigation
Authors: Steven W. Chen, Nikolay Atanasov, Arbaaz Khan, Konstantinos Karydis, Daniel D. Lee, Vijay Kumar
Date: 2017-05-23 00:00:00
Tasks: Robot Navigation

This paper highlights the significance of including memory structures in
neural networks when the latter are used to learn perception-action loops for
autonomous robot navigation. Traditional navigation approaches rely on global
maps of the environment to overcome cul-de-sacs and plan feasible motions. Yet,
maintaining an accurate global map may be challenging in real-world settings. A
possible way to mitigate this limitation is to use learning techniques that
forgo hand-engineered map representations and infer appropriate control
responses directly from sensed information. An important but unexplored aspect
of such approaches is the effect of memory on their performance. This work is a
first thorough study of memory structures for deep-neural-network-based robot
navigation, and offers novel tools to train such networks from supervision and
quantify their ability to generalize to unseen scenarios. We analyze the
separation and generalization abilities of feedforward, long short-term memory,
and differentiable neural computer networks. We introduce a new method to
evaluate the generalization ability by estimating the VC-dimension of networks
with a final linear readout layer. We validate that the VC estimates are good
predictors of actual test performance. The reported method can be applied to
deep learning problems beyond robotics.
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
Authors: Zheng Wen, Branislav Kveton, Michal Valko, Sharan Vaswani
Date: 2016-05-21 00:00:00
Proceeding: NeurIPS 2017 12

We study the online influence maximization problem in social networks under
the independent cascade model. Specifically, we aim to learn the set of "best
influencers" in a social network online while repeatedly interacting with it.
We address the challenges of (i) combinatorial action space, since the number
of feasible influencer sets grows exponentially with the maximum number of
influencers, and (ii) limited feedback, since only the influenced portion of
the network is observed. Under a stochastic semi-bandit feedback, we propose
and analyze IMLinUCB, a computationally efficient UCB-based algorithm. Our
bounds on the cumulative regret are polynomial in all quantities of interest,
achieve near-optimal dependence on the number of interactions and reflect the
topology of the network and the activation probabilities of its edges, thereby
giving insights on the problem complexity. To the best of our knowledge, these
are the first such results. Our experiments show that in several representative
graph topologies, the regret of IMLinUCB scales as suggested by our upper
bounds. IMLinUCB permits linear generalization and thus is both statistically
and computationally suitable for large-scale problems. Our experiments also
show that IMLinUCB with linear generalization can lead to low regret in
real-world online influence maximization.
Unrolled Optimization with Deep Priors
Authors: Steven Diamond, Vincent Sitzmann, Felix Heide, Gordon Wetzstein
Date: 2017-05-22 00:00:00
Tasks: compressed sensing, Deblurring, Denoising

A broad class of problems at the core of computational imaging, sensing, and
low-level computer vision reduces to the inverse problem of extracting latent
images that follow a prior distribution, from measurements taken under a known
physical image formation model. Traditionally, hand-crafted priors along with
iterative optimization methods have been used to solve such problems. In this
paper we present unrolled optimization with deep priors, a principled framework
for infusing knowledge of the image formation into deep networks that solve
inverse problems in imaging, inspired by classical iterative methods. We show
that instances of the framework outperform the state-of-the-art by a
substantial margin for a wide variety of imaging problems, such as denoising,
deblurring, and compressed sensing magnetic resonance imaging (MRI). Moreover,
we conduct experiments that explain how the framework is best used and why it
outperforms previous methods.
Latent Human Traits in the Language of Social Media: An Open-Vocabulary Approach
Authors: Vivek Kulkarni, Margaret L. Kern, David Stillwell, Michal Kosinski, Sandra Matz, Lyle Ungar, Steven Skiena, H. Andrew Schwartz
Date: 2017-05-22 00:00:00

Over the past century, personality theory and research has successfully
identified core sets of characteristics that consistently describe and explain
fundamental differences in the way people think, feel and behave. Such
characteristics were derived through theory, dictionary analyses, and survey
research using explicit self-reports. The availability of social media data
spanning millions of users now makes it possible to automatically derive
characteristics from language use -- at large scale. Taking advantage of
linguistic information available through Facebook, we study the process of
inferring a new set of potential human traits based on unprompted language use.
We subject these new traits to a comprehensive set of evaluations and compare
them with a popular five factor model of personality. We find that our
language-based trait construct is often more generalizable in that it often
predicts non-questionnaire-based outcomes better than questionnaire-based
traits (e.g. entities someone likes, income and intelligence quotient), while
the factors remain nearly as stable as traditional factors. Our approach
suggests a value in new constructs of personality derived from everyday human
language use.
Consistencies and inconsistencies between model selection and link prediction in networks
Authors: Toni Vallès-Català, Tiago P. Peixoto, Roger Guimerà, Marta Sales-Pardo
Date: 2017-05-22 00:00:00
Tasks: Link Prediction, Model Selection

A principled approach to understand network structures is to formulate
generative models. Given a collection of models, however, an outstanding key
task is to determine which one provides a more accurate description of the
network at hand, discounting statistical fluctuations. This problem can be
approached using two principled criteria that at first may seem equivalent:
selecting the most plausible model in terms of its posterior probability; or
selecting the model with the highest predictive performance in terms of
identifying missing links. Here we show that while these two approaches yield
consistent results in most of cases, there are also notable instances where
they do not, that is, where the most plausible model is not the most
predictive. We show that in the latter case the improvement of predictive
performance can in fact lead to overfitting both in artificial and empirical
settings. Furthermore, we show that, in general, the predictive performance is
higher when we average over collections of models that are individually less
plausible, than when we consider only the single most plausible model.
Rademacher complexity of stationary sequences
Authors: Daniel J. McDonald, Cosma Rohilla Shalizi
Date: 2011-06-03 00:00:00
Tasks: Time Series, Time Series Analysis

We show how to control the generalization error of time series models wherein
past values of the outcome are used to predict future values. The results are
based on a generalization of standard i.i.d. concentration inequalities to
dependent data without the mixing assumptions common in the time series
setting. Our proof and the result are simpler than previous analyses with
dependent data or stochastic adversaries which use sequential Rademacher
complexities rather than the expected Rademacher complexity for i.i.d.
processes. We also derive empirical Rademacher results without mixing
assumptions resulting in fully calculable upper bounds.
Parallel Stochastic Gradient Descent with Sound Combiners
Authors: Saeed Maleki, Madanlal Musuvathi, Todd Mytkowicz
Date: 2017-05-22 00:00:00

Stochastic gradient descent (SGD) is a well known method for regression and
classification tasks. However, it is an inherently sequential algorithm at each
step, the processing of the current example depends on the parameters learned
from the previous examples. Prior approaches to parallelizing linear learners
using SGD, such as HOGWILD! and ALLREDUCE, do not honor these dependencies
across threads and thus can potentially suffer poor convergence rates and/or
poor scalability. This paper proposes SYMSGD, a parallel SGD algorithm that, to
a first-order approximation, retains the sequential semantics of SGD. Each
thread learns a local model in addition to a model combiner, which allows local
models to be combined to produce the same result as what a sequential SGD would
have produced. This paper evaluates SYMSGD's accuracy and performance on 6
datasets on a shared-memory machine shows upto 11x speedup over our heavily
optimized sequential baseline on 16 cores and 2.2x, on average, faster than
HOGWILD!.
Use of Knowledge Graph in Rescoring the N-Best List in Automatic Speech Recognition
Authors: Ashwini Jaya Kumar, Camilo Morales, Maria-Esther Vidal, Christoph Schmidt, Sören Auer
Date: 2017-05-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modeling, Language Modelling, Sentence, speech-recognition, Speech Recognition

With the evolution of neural network based methods, automatic speech
recognition (ASR) field has been advanced to a level where building an
application with speech interface is a reality. In spite of these advances,
building a real-time speech recogniser faces several problems such as low
recognition accuracy, domain constraint, and out-of-vocabulary words. The low
recognition accuracy problem is addressed by improving the acoustic model,
language model, decoder and by rescoring the N-best list at the output of the
decoder. We are considering the N-best list rescoring approach to improve the
recognition accuracy. Most of the methods in the literature use the
grammatical, lexical, syntactic and semantic connection between the words in a
recognised sentence as a feature to rescore. In this paper, we have tried to
see the semantic relatedness between the words in a sentence to rescore the
N-best list. Semantic relatedness is computed using
TransE~\cite{bordes2013translating}, a method for low dimensional embedding of
a triple in a knowledge graph. The novelty of the paper is the application of
semantic web to automatic speech recognition.
Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices
Authors: Tayfun Gokmen, O. Murat Onen, Wilfried Haensch
Date: 2017-05-22 00:00:00
Tasks: Management

In a previous work we have detailed the requirements to obtain a maximal
performance benefit by implementing fully connected deep neural networks (DNN)
in form of arrays of resistive devices for deep learning. This concept of
Resistive Processing Unit (RPU) devices we extend here towards convolutional
neural networks (CNNs). We show how to map the convolutional layers to RPU
arrays such that the parallelism of the hardware can be fully utilized in all
three cycles of the backpropagation algorithm. We find that the noise and bound
limitations imposed due to analog nature of the computations performed on the
arrays effect the training accuracy of the CNNs. Noise and bound management
techniques are presented that mitigate these problems without introducing any
additional complexity in the analog circuits and can be addressed by the
digital circuits. In addition, we discuss digitally programmable update
management and device variability reduction techniques that can be used
selectively for some of the layers in a CNN. We show that combination of all
those techniques enables a successful application of the RPU concept for
training CNNs. The techniques discussed here are more general and can be
applied beyond CNN architectures and therefore enables applicability of RPU
approach for large class of neural network architectures.
Diminishing Batch Normalization
Authors: Yintai Ma, Diego Klabjan
Date: 2017-05-22 00:00:00
Proceeding: ICLR 2019 5

In this paper, we propose a generalization of the Batch Normalization (BN)
algorithm, diminishing batch normalization (DBN), where we update the BN
parameters in a diminishing moving average way. BN is very effective in
accelerating the convergence of a neural network training phase that it has
become a common practice. Our proposed DBN algorithm remains the overall
structure of the original BN algorithm while introduces a weighted averaging
update to some trainable parameters. We provide an analysis of the convergence
of the DBN algorithm that converges to a stationary point with respect to
trainable parameters. Our analysis can be easily generalized for original BN
algorithm by setting some parameters to constant. To the best knowledge of
authors, this analysis is the first of its kind for convergence with Batch
Normalization introduced. We analyze a two-layer model with arbitrary
activation function. The primary challenge of the analysis is the fact that
some parameters are updated by gradient while others are not. The convergence
analysis applies to any activation function that satisfies our common
assumptions. In the numerical experiments, we test the proposed algorithm on
complex modern CNN models with stochastic gradients and ReLU activation. We
observe that DBN outperforms the original BN algorithm on MNIST, NI and
CIFAR-10 datasets with reasonable complex FNN and CNN models.
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint
Authors: Nguyen Viet Cuong, Huan Xu
Date: 2016-03-30 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Active Learning

We study the worst-case adaptive optimization problem with budget constraint
that is useful for modeling various practical applications in artificial
intelligence and machine learning. We investigate the near-optimality of greedy
algorithms for this problem with both modular and non-modular cost functions.
In both cases, we prove that two simple greedy algorithms are not near-optimal
but the best between them is near-optimal if the utility function satisfies
pointwise submodularity and pointwise cost-sensitive submodularity
respectively. This implies a combined algorithm that is near-optimal with
respect to the optimal algorithm that uses half of the budget. We discuss
applications of our theoretical results and also report experiments comparing
the greedy algorithms on the active learning problem.
Living Together: Mind and Machine Intelligence
Authors: Neil D. Lawrence
Date: 2017-05-22 00:00:00

In this paper we consider the nature of the machine intelligences we have
created in the context of our human intelligence. We suggest that the
fundamental difference between human and machine intelligence comes down to
\emph{embodiment factors}. We define embodiment factors as the ratio between an
entity's ability to communicate information vs compute information. We
speculate on the role of embodiment factors in driving our own intelligence and
consciousness. We briefly review dual process models of cognition and cast
machine intelligence within that framework, characterising it as a dominant
System Zero, which can drive behaviour through interfacing with us
subconsciously. Driven by concerns about the consequence of such a system we
suggest prophylactic courses of action that could be considered. Our main
conclusion is that it is \emph{not} sentient intelligence we should fear but
\emph{non-sentient} intelligence.
Graphons, mergeons, and so on!
Authors: Justin Eldridge, Mikhail Belkin, Yusu Wang
Date: 2016-07-06 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Clustering, Graph Clustering

In this work we develop a theory of hierarchical clustering for graphs. Our
modeling assumption is that graphs are sampled from a graphon, which is a
powerful and general model for generating graphs and analyzing large networks.
Graphons are a far richer class of graph models than stochastic blockmodels,
the primary setting for recent progress in the statistical theory of graph
clustering. We define what it means for an algorithm to produce the "correct"
clustering, give sufficient conditions in which a method is statistically
consistent, and provide an explicit algorithm satisfying these properties.
Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations
Authors: Joshua J. Engelsma, Sunpreet S. Arora, Anil K. Jain, Nicholas G. Paulter Jr
Date: 2017-05-22 00:00:00

We present the design and manufacturing of high fidelity universal 3D
fingerprint targets, which can be imaged on a variety of fingerprint sensing
technologies, namely capacitive, contact-optical, and contactless-optical.
Universal 3D fingerprint targets enable, for the first time, not only a
repeatable and controlled evaluation of fingerprint readers, but also the
ability to conduct fingerprint reader interoperability studies. Fingerprint
reader interoperability refers to how robust fingerprint recognition systems
are to variations in the images acquired by different types of fingerprint
readers. To build universal 3D fingerprint targets, we adopt a molding and
casting framework consisting of (i) digital mapping of fingerprint images to a
negative mold, (ii) CAD modeling a scaffolding system to hold the negative
mold, (iii) fabricating the mold and scaffolding system with a high resolution
3D printer, (iv) producing or mixing a material with similar electrical,
optical, and mechanical properties to that of the human finger, and (v)
fabricating a 3D fingerprint target using controlled casting. Our experiments
conducted with PIV and Appendix F certified optical (contact and contactless)
and capacitive fingerprint readers demonstrate the usefulness of universal 3D
fingerprint targets for controlled and repeatable fingerprint reader
evaluations and also fingerprint reader interoperability studies.