pwc-archive/papers-with-abstracts huggingface dataset:
DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution
Authors: Philip Bontrager, Aditi Roy, Julian Togelius, Nasir Memon, Arun Ross
Date: 2017-05-21 00:00:00
Tasks: Generative Adversarial Network

Recent research has demonstrated the vulnerability of fingerprint recognition
systems to dictionary attacks based on MasterPrints. MasterPrints are real or
synthetic fingerprints that can fortuitously match with a large number of
fingerprints thereby undermining the security afforded by fingerprint systems.
Previous work by Roy et al. generated synthetic MasterPrints at the
feature-level. In this work we generate complete image-level MasterPrints known
as DeepMasterPrints, whose attack accuracy is found to be much superior than
that of previous methods. The proposed method, referred to as Latent Variable
Evolution, is based on training a Generative Adversarial Network on a set of
real fingerprint images. Stochastic search in the form of the Covariance Matrix
Adaptation Evolution Strategy is then used to search for latent input variables
to the generator network that can maximize the number of impostor matches as
assessed by a fingerprint recognizer. Experiments convey the efficacy of the
proposed method in generating DeepMasterPrints. The underlying method is likely
to have broad applications in fingerprint security as well as fingerprint
synthesis.
Bayesian Optimization Using Monotonicity Information and Its Application in Machine Learning Hyperparameter
Authors: Wenyi Wang, William J. Welch
Date: 2018-02-10 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning

We propose an algorithm for a family of optimization problems where the
objective can be decomposed as a sum of functions with monotonicity properties.
The motivating problem is optimization of hyperparameters of machine learning
algorithms, where we argue that the objective, validation error, can be
decomposed as monotonic functions of the hyperparameters. Our proposed
algorithm adapts Bayesian optimization methods to incorporate the monotonicity
constraints. We illustrate the advantages of exploiting monotonicity using
illustrative examples and demonstrate the improvements in optimization
efficiency for some machine learning hyperparameter tuning applications.
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
Authors: Jeffrey Chan, Valerio Perrone, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Yun S. Song
Date: 2018-02-16 00:00:00
Proceeding: NeurIPS 2018 12

An explosion of high-throughput DNA sequencing in the past decade has led to
a surge of interest in population-scale inference with whole-genome data.
Recent work in population genetics has centered on designing inference methods
for relatively simple model classes, and few scalable general-purpose inference
techniques exist for more realistic, complex models. To achieve this, two
inferential challenges need to be addressed: (1) population data are
exchangeable, calling for methods that efficiently exploit the symmetries of
the data, and (2) computing likelihoods is intractable as it requires
integrating over a set of correlated, extremely high-dimensional latent
variables. These challenges are traditionally tackled by likelihood-free
methods that use scientific simulators to generate datasets and reduce them to
hand-designed, permutation-invariant summary statistics, often leading to
inaccurate inference. In this work, we develop an exchangeable neural network
that performs summary statistic-free, likelihood-free inference. Our framework
can be applied in a black-box fashion across a variety of simulation-based
tasks, both within and outside biology. We demonstrate the power of our
approach on the recombination hotspot testing problem, outperforming the
state-of-the-art.
The Square Root Agreement Rule for Incentivizing Truthful Feedback on Online Platforms
Authors: Vijay Kamble, Nihar Shah, David Marn, Abhay Parekh, Kannan Ramachandran
Date: 2015-07-25 00:00:00

A major challenge in obtaining evaluations of products or services on e-commerce platforms is eliciting informative responses in the absence of verifiability. This paper proposes the Square Root Agreement Rule (SRA): a simple reward mechanism that incentivizes truthful responses to objective evaluations on such platforms. In this mechanism, an agent gets a reward for an evaluation only if her answer matches that of her peer, where this reward is inversely proportional to a popularity index of the answer. This index is defined to be the square root of the empirical frequency at which any two agents performing the same evaluation agree on the particular answer across evaluations of similar entities operating on the platform. Rarely agreed-upon answers thus earn a higher reward than answers for which agreements are relatively more common. We show that in the many tasks regime, the truthful equilibrium under SRA is strictly payoff-dominant across large classes of natural equilibria that could arise in these settings, thus increasing the likelihood of its adoption. While there exist other mechanisms achieving such guarantees, they either impose additional assumptions on the response distribution that are not generally satisfied for objective evaluations or they incentivize truthful behavior only if each agent performs a prohibitively large number of evaluations and commits to using the same strategy for each evaluation. SRA is the first known incentive mechanism satisfying such guarantees without imposing any such requirements. Moreover, our empirical findings demonstrate the robustness of the incentive properties of SRA in the presence of mild subjectivity or observational biases in the responses. These properties make SRA uniquely attractive for administering reward-based incentive schemes (e.g., rebates, discounts, reputation scores, etc.) on online platforms.
ActionFlowNet: Learning Motion Representation for Action Recognition
Authors: Joe Yue-Hei Ng, Jonghyun Choi, Jan Neumann, Larry S. Davis
Date: 2016-12-09 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

Even with the recent advances in convolutional neural networks (CNN) in
various visual recognition tasks, the state-of-the-art action recognition
system still relies on hand crafted motion feature such as optical flow to
achieve the best performance. We propose a multitask learning model
ActionFlowNet to train a single stream network directly from raw pixels to
jointly estimate optical flow while recognizing actions with convolutional
neural networks, capturing both appearance and motion in a single model. We
additionally provide insights to how the quality of the learned optical flow
affects the action recognition. Our model significantly improves action
recognition accuracy by a large margin 31% compared to state-of-the-art
CNN-based action recognition models trained without external large scale data
and additional optical flow input. Without pretraining on large external
labeled datasets, our model, by well exploiting the motion information,
achieves competitive recognition accuracy to the models trained with large
labeled datasets such as ImageNet and Sport-1M.
Real-Time 3D Shape of Micro-Details
Authors: Maryam Khanian, Ali Sharifi Boroujerdi, Michael Breuss
Date: 2018-02-16 00:00:00
Tasks: 3D Reconstruction, 3D Shape Reconstruction

Motivated by the growing demand for interactive environments, we propose an
accurate real-time 3D shape reconstruction technique. To provide a reliable 3D
reconstruction which is still a challenging task when dealing with real-world
applications, we integrate several components including (i) Photometric Stereo
(PS), (ii) perspective Cook-Torrance reflectance model that enables PS to deal
with a broad range of possible real-world object reflections, (iii) realistic
lightening situation, (iv) a Recurrent Optimization Network (RON) and finally
(v) heuristic Dijkstra Gaussian Mean Curvature (DGMC) initialization approach.
We demonstrate the potential benefits of our hybrid model by providing 3D shape
with highly-detailed information from micro-prints for the first time. All
real-world images are taken by a mobile phone camera under a simple setup as a
consumer-level equipment. In addition, complementary synthetic experiments
confirm the beneficial properties of our novel method and its superiority over
the state-of-the-art approaches.
Reactive Reinforcement Learning in Asynchronous Environments
Authors: Jaden B. Travnik, Kory W. Mathewson, Richard S. Sutton, Patrick M. Pilarski
Date: 2018-02-16 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The relationship between a reinforcement learning (RL) agent and an
asynchronous environment is often ignored. Frequently used models of the
interaction between an agent and its environment, such as Markov Decision
Processes (MDP) or Semi-Markov Decision Processes (SMDP), do not capture the
fact that, in an asynchronous environment, the state of the environment may
change during computation performed by the agent. In an asynchronous
environment, minimizing reaction time---the time it takes for an agent to react
to an observation---also minimizes the time in which the state of the
environment may change following observation. In many environments, the
reaction time of an agent directly impacts task performance by permitting the
environment to transition into either an undesirable terminal state or a state
where performing the chosen action is inappropriate. We propose a class of
reactive reinforcement learning algorithms that address this problem of
asynchronous environments by immediately acting after observing new state
information. We compare a reactive SARSA learning algorithm with the
conventional SARSA learning algorithm on two asynchronous robotic tasks
(emergency stopping and impact prevention), and show that the reactive RL
algorithm reduces the reaction time of the agent by approximately the duration
of the algorithm's learning update. This new class of reactive algorithms may
facilitate safer control and faster decision making without any change to
standard learning guarantees.
Detecting Social Influence in Event Cascades by Comparing Discriminative Rankers
Authors: Sandeep Soni, Shawn Ling Ramirez, Jacob Eisenstein
Date: 2018-02-16 00:00:00

The global dynamics of event cascades are often governed by the local dynamics of peer influence. However, detecting social influence from observational data is challenging due to confounds like homophily and practical issues like missing data. We propose a simple discriminative method to detect influence from observational data. The core of the approach is to train a ranking algorithm to predict the source of the next event in a cascade, and compare its out-of-sample accuracy against a competitive baseline which lacks access to features corresponding to social influence. We analyze synthetically generated data to show that this method correctly identifies influence in the presence of confounds, and is robust to both missing data and misspecification --- unlike well-known alternatives. We apply the method to two real-world datasets: (1) the co-sponsorship of legislation in the U.S. House of Representatives on a social network of shared campaign donors; (2) rumors about the Higgs boson discovery on a follower network of $10^5$ Twitter accounts. Our model identifies the role of social influence in these scenarios and uses it to make more accurate predictions about the future trajectory of cascades.
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks
Authors: Tengyuan Liang, James Stokes
Date: 2018-02-16 00:00:00

Motivated by the pursuit of a systematic computational and algorithmic
understanding of Generative Adversarial Networks (GANs), we present a simple
yet unified non-asymptotic local convergence theory for smooth two-player
games, which subsumes several discrete-time gradient-based saddle point
dynamics. The analysis reveals the surprising nature of the off-diagonal
interaction term as both a blessing and a curse. On the one hand, this
interaction term explains the origin of the slow-down effect in the convergence
of Simultaneous Gradient Ascent (SGA) to stable Nash equilibria. On the other
hand, for the unstable equilibria, exponential convergence can be proved thanks
to the interaction term, for four modified dynamics proposed to stabilize GAN
training: Optimistic Mirror Descent (OMD), Consensus Optimization (CO),
Implicit Updates (IU) and Predictive Method (PM). The analysis uncovers the
intimate connections among these stabilizing techniques, and provides detailed
characterization on the choice of learning rate. As a by-product, we present a
new analysis for OMD proposed in Daskalakis, Ilyas, Syrgkanis, and Zeng [2017]
with improved rates.
Fast, Trainable, Multiscale Denoising
Authors: Sungjoon Choi, John Isidoro, Pascal Getreuer, Peyman Milanfar
Date: 2018-02-16 00:00:00
Tasks: Denoising

Denoising is a fundamental imaging problem. Versatile but fast filtering has
been demanded for mobile camera systems. We present an approach to multiscale
filtering which allows real-time applications on low-powered devices. The key
idea is to learn a set of kernels that upscales, filters, and blends patches of
different scales guided by local structure analysis. This approach is trainable
so that learned filters are capable of treating diverse noise patterns and
artifacts. Experimental results show that the presented approach produces
comparable results to state-of-the-art algorithms while processing time is
orders of magnitude faster.
How Well Can Generative Adversarial Networks Learn Densities: A Nonparametric View
Authors: Tengyuan Liang
Date: 2017-12-21 00:00:00
Tasks: Generalization Bounds

We study in this paper the rate of convergence for learning densities under
the Generative Adversarial Networks (GAN) framework, borrowing insights from
nonparametric statistics. We introduce an improved GAN estimator that achieves
a faster rate, through simultaneously leveraging the level of smoothness in the
target density and the evaluation metric, which in theory remedies the mode
collapse problem reported in the literature. A minimax lower bound is
constructed to show that when the dimension is large, the exponent in the rate
for the new GAN estimator is near optimal. One can view our results as
answering in a quantitative way how well GAN learns a wide range of densities
with different smoothness properties, under a hierarchy of evaluation metrics.
As a byproduct, we also obtain improved generalization bounds for GAN with
deeper ReLU discriminator network.
Scenarios: A New Representation for Complex Scene Understanding
Authors: Zachary A. Daniels, Dimitris N. Metaxas
Date: 2018-02-16 00:00:00
Tasks: Image Retrieval, Object Recognition, Retrieval, Scene Classification, Scene Understanding

The ability for computational agents to reason about the high-level content
of real world scene images is important for many applications. Existing
attempts at addressing the problem of complex scene understanding lack
representational power, efficiency, and the ability to create robust
meta-knowledge about scenes. In this paper, we introduce scenarios as a new way
of representing scenes. The scenario is a simple, low-dimensional, data-driven
representation consisting of sets of frequently co-occurring objects and is
useful for a wide range of scene understanding tasks. We learn scenarios from
data using a novel matrix factorization method which we integrate into a new
neural network architecture, the ScenarioNet. Using ScenarioNet, we can recover
semantic information about real world scene images at three levels of
granularity: 1) scene categories, 2) scenarios, and 3) objects. Training a
single ScenarioNet model enables us to perform scene classification, scenario
recognition, multi-object recognition, content-based scene image retrieval, and
content-based image comparison. In addition to solving many tasks in a single,
unified framework, ScenarioNet is more computationally efficient than other
CNNs because it requires significantly fewer parameters while achieving similar
performance on benchmark tasks and is more interpretable because it produces
explanations when making decisions. We validate the utility of scenarios and
ScenarioNet on a diverse set of scene understanding tasks on several benchmark
datasets.
Modeling the Formation of Social Conventions from Embodied Real-Time Interactions
Authors: Ismael T. Freire, Clement Moulin-Frier, Marti Sanchez-Fibla, Xerxes D. Arsiwalla, Paul Verschure
Date: 2018-02-16 00:00:00
Tasks: Decision Making, Fairness, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

What is the role of real-time control and learning in the formation of social conventions? To answer this question, we propose a computational model that matches human behavioral data in a social decision-making game that was analyzed both in discrete-time and continuous-time setups. Furthermore, unlike previous approaches, our model takes into account the role of sensorimotor control loops in embodied decision-making scenarios. For this purpose, we introduce the Control-based Reinforcement Learning (CRL) model. CRL is grounded in the Distributed Adaptive Control (DAC) theory of mind and brain, where low-level sensorimotor control is modulated through perceptual and behavioral learning in a layered structure. CRL follows these principles by implementing a feedback control loop handling the agent's reactive behaviors (pre-wired reflexes), along with an adaptive layer that uses reinforcement learning to maximize long-term reward. We test our model in a multi-agent game-theoretic task in which coordination must be achieved to find an optimal solution. We show that CRL is able to reach human-level performance on standard game-theoretic metrics such as efficiency in acquiring rewards and fairness in reward distribution.
Mining Sub-Interval Relationships In Time Series Data
Authors: Saurabh Agrawal, Saurabh Verma, Gowtham Atluri, Anuj Karpatne, Stefan Liess, Angus Macdonald III, Snigdhansu Chatterjee, Vipin Kumar
Date: 2018-02-16 00:00:00
Tasks: Computational Efficiency, Time Series, Time Series Analysis

Time-series data is being increasingly collected and stud- ied in several
areas such as neuroscience, climate science, transportation, and social media.
Discovery of complex patterns of relationships between individual time-series,
using data-driven approaches can improve our understanding of real-world
systems. While traditional approaches typically study relationships between two
entire time series, many interesting relationships in real-world applications
exist in small sub-intervals of time while remaining absent or feeble during
other sub-intervals. In this paper, we define the notion of a sub-interval
relationship (SIR) to capture inter- actions between two time series that are
prominent only in certain sub-intervals of time. We propose a novel and
efficient approach to find most interesting SIR in a pair of time series. We
evaluate our proposed approach on two real-world datasets from climate science
and neuroscience domain and demonstrated the scalability and computational
efficiency of our proposed approach. We further evaluated our discovered SIRs
based on a randomization based procedure. Our results indicated the existence
of several such relationships that are statistically significant, some of which
were also found to have physical interpretation.
Bridging Cognitive Programs and Machine Learning
Authors: Amir Rosenfeld, John K. Tsotsos
Date: 2018-02-16 00:00:00
Tasks: BIG-bench Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

While great advances are made in pattern recognition and machine learning,
the successes of such fields remain restricted to narrow applications and seem
to break down when training data is scarce, a shift in domain occurs, or when
intelligent reasoning is required for rapid adaptation to new environments. In
this work, we list several of the shortcomings of modern machine-learning
solutions, specifically in the contexts of computer vision and in reinforcement
learning and suggest directions to explore in order to try to ameliorate these
weaknesses.
Artificial intelligence and pediatrics: A synthetic mini review
Authors: Peter Kokol, Jernej Završnik, Helena Blažun Vošner
Date: 2018-02-16 00:00:00
Tasks: Decision Making, Diagnostic

The use of artificial intelligence intelligencein medicine can be traced back
to 1968 when Paycha published his paper Le diagnostic a l'aide d'intelligences
artificielle, presentation de la premiere machine diagnostri. Few years later
Shortliffe et al. presented an expert system named Mycin which was able to
identify bacteria causing severe blood infections and to recommend antibiotics.
Despite the fact that Mycin outperformed members of the Stanford medical school
in the reliability of diagnosis it was never used in practice due to a legal
issue who do you sue if it gives a wrong diagnosis?. However only in 2016 when
the artificial intelligence software built into the IBM Watson AI platform
correctly diagnosed and proposed an effective treatment for a 60-year-old
womans rare form of leukemia the AI use in medicine become really popular.On of
first papers presenting the use of AI in paediatrics was published in 1984. The
paper introduced a computer-assisted medical decision making system called
SHELP.
Multi-Task Pharmacovigilance Mining from Social Media Posts
Authors: Shaika Chowdhury, Chenwei Zhang, Philip S. Yu
Date: 2018-01-19 00:00:00
Tasks: Pharmacovigilance

Social media has grown to be a crucial information source for
pharmacovigilance studies where an increasing number of people post adverse
reactions to medical drugs that are previously unreported. Aiming to
effectively monitor various aspects of Adverse Drug Reactions (ADRs) from
diversely expressed social medical posts, we propose a multi-task neural
network framework that learns several tasks associated with ADR monitoring with
different levels of supervisions collectively. Besides being able to correctly
classify ADR posts and accurately extract ADR mentions from online posts, the
proposed framework is also able to further understand reasons for which the
drug is being taken, known as 'indication', from the given social media post. A
coverage-based attention mechanism is adopted in our framework to help the
model properly identify 'phrasal' ADRs and Indications that are attentive to
multiple words in a post. Our framework is applicable in situations where
limited parallel data for different pharmacovigilance tasks are available.We
evaluate the proposed framework on real-world Twitter datasets, where the
proposed model outperforms the state-of-the-art alternatives of each individual
task consistently.
Near-optimal Sample Complexity Bounds for Robust Learning of Gaussians Mixtures via Compression Schemes
Authors: Hassan Ashtiani, Shai Ben-David, Nick Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan
Date: 2017-10-14 00:00:00

We prove that $\tilde{\Theta}(k d^2 / \varepsilon^2)$ samples are necessary and sufficient for learning a mixture of $k$ Gaussians in $\mathbb{R}^d$, up to error $\varepsilon$ in total variation distance. This improves both the known upper bounds and lower bounds for this problem. For mixtures of axis-aligned Gaussians, we show that $\tilde{O}(k d / \varepsilon^2)$ samples suffice, matching a known lower bound. Moreover, these results hold in the agnostic-learning/robust-estimation setting as well, where the target distribution is only approximately a mixture of Gaussians. The upper bound is shown using a novel technique for distribution learning based on a notion of `compression.' Any class of distributions that allows such a compression scheme can also be learned with few samples. Moreover, if a class of distributions has such a compression scheme, then so do the classes of products and mixtures of those distributions. The core of our main result is showing that the class of Gaussians in $\mathbb{R}^d$ admits a small-sized compression scheme.