pwc-archive/papers-with-abstracts huggingface dataset:
Should Robots be Obedient?
Authors: Smitha Milli, Dylan Hadfield-Menell, Anca Dragan, Stuart Russell
Date: 2017-05-28 00:00:00

Intuitively, obedience -- following the order that a human gives -- seems
like a good property for a robot to have. But, we humans are not perfect and we
may give orders that are not best aligned to our preferences. We show that when
a human is not perfectly rational then a robot that tries to infer and act
according to the human's underlying preferences can always perform better than
a robot that simply follows the human's literal order. Thus, there is a
tradeoff between the obedience of a robot and the value it can attain for its
owner. We investigate how this tradeoff is impacted by the way the robot infers
the human's preferences, showing that some methods err more on the side of
obedience than others. We then analyze how performance degrades when the robot
has a misspecified model of the features that the human cares about or the
level of rationality of the human. Finally, we study how robots can start
detecting such model misspecification. Overall, our work suggests that there
might be a middle ground in which robots intelligently decide when to obey
human orders, but err on the side of obedience.
A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams
Authors: Nazli Farajidavar, Sefki Kolozali, Payam Barnaghi
Date: 2017-05-28 00:00:00
Tasks: Event Extraction, Feature Engineering, MULTI-VIEW LEARNING

Cities have been a thriving place for citizens over the centuries due to
their complex infrastructure. The emergence of the Cyber-Physical-Social
Systems (CPSS) and context-aware technologies boost a growing interest in
analysing, extracting and eventually understanding city events which
subsequently can be utilised to leverage the citizen observations of their
cities. In this paper, we investigate the feasibility of using Twitter textual
streams for extracting city events. We propose a hierarchical multi-view deep
learning approach to contextualise citizen observations of various city systems
and services. Our goal has been to build a flexible architecture that can learn
representations useful for tasks, thus avoiding excessive task-specific feature
engineering. We apply our approach on a real-world dataset consisting of event
reports and tweets of over four months from San Francisco Bay Area dataset and
additional datasets collected from London. The results of our evaluations show
that our proposed solution outperforms the existing models and can be used for
extracting city related events with an averaged accuracy of 81% over all
classes. To further evaluate the impact of our Twitter event extraction model,
we have used two sources of authorised reports through collecting road traffic
disruptions data from Transport for London API, and parsing the Time Out London
website for sociocultural events. The analysis showed that 49.5% of the Twitter
traffic comments are reported approximately five hours prior to the authorities
official records. Moreover, we discovered that amongst the scheduled
sociocultural event topics; tweets reporting transportation, cultural and
social events are 31.75% more likely to influence the distribution of the
Twitter comments than sport, weather and crime topics.
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference
Authors: Geoffrey Roeder, Yuhuai Wu, David Duvenaud
Date: 2017-03-27 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Variational Inference

We propose a simple and general variant of the standard reparameterized
gradient estimator for the variational evidence lower bound. Specifically, we
remove a part of the total derivative with respect to the variational
parameters that corresponds to the score function. Removing this term produces
an unbiased gradient estimator whose variance approaches zero as the
approximate posterior approaches the exact posterior. We analyze the behavior
of this gradient estimator theoretically and empirically, and generalize it to
more complex variational distributions such as mixtures and importance-weighted
posteriors.
Attribute-Guided Face Generation Using Conditional CycleGAN
Authors: Yongyi Lu, Yu-Wing Tai, Chi-Keung Tang
Date: 2017-05-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: Attribute, Face Generation, Face Swapping, Face Verification

We are interested in attribute-guided face generation: given a low-res face
input image, an attribute vector that can be extracted from a high-res image
(attribute image), our new method generates a high-res face image for the
low-res input that satisfies the given attributes. To address this problem, we
condition the CycleGAN and propose conditional CycleGAN, which is designed to
1) handle unpaired training data because the training low/high-res and high-res
attribute images may not necessarily align with each other, and to 2) allow
easy control of the appearance of the generated face via the input attributes.
We demonstrate impressive results on the attribute-guided conditional CycleGAN,
which can synthesize realistic face images with appearance easily controlled by
user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using
the attribute image as identity to produce the corresponding conditional vector
and by incorporating a face verification network, the attribute-guided network
becomes the identity-guided conditional CycleGAN which produces impressive and
interesting results on identity transfer. We demonstrate three applications on
identity-guided conditional CycleGAN: identity-preserving face superresolution,
face swapping, and frontal face generation, which consistently show the
advantage of our new method.
L1-norm Error Function Robustness and Outlier Regularization
Authors: Chris Ding, Bo Jiang
Date: 2017-05-28 00:00:00

In many real-world applications, data come with corruptions, large errors or
outliers. One popular approach is to use L1-norm function. However, the
robustness of L1-norm function is not well understood so far. In this paper, we
present a new outlier regularization framework to understand and analyze the
robustness of L1-norm function. There are two main features for the proposed
outlier regularization. (1) A key property of outlier regularization is that
how far an outlier lies away from its theoretically predicted value does not
affect the final regularization and analysis results. (2) Another important
feature of outlier regularization is that it has an equivalent continuous
representation that closely relates to L1 function. This provides a new way to
understand and analyze the robustness of L1 function. We apply our outlier
regularization framework to PCA and propose an outlier regularized PCA (ORPCA)
model. Comparing to the trace-normbased robust PCA, ORPCA has several benefits:
(1) It does not suffer singular value suppression. (2) It can retain small high
rank components which help retain fine details of data. (3) ORPCA can be
computed more efficiently.
Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection
Authors: Xianzhi Du, Mostafa El-Khamy, Jungwon Lee, Larry S. Davis
Date: 2016-10-11 00:00:00
Tasks: Pedestrian Detection, Semantic Segmentation

We propose a deep neural network fusion architecture for fast and robust
pedestrian detection. The proposed network fusion architecture allows for
parallel processing of multiple networks for speed. A single shot deep
convolutional network is trained as a object detector to generate all possible
pedestrian candidates of different sizes and occlusions. This network outputs a
large variety of pedestrian candidates to cover the majority of ground-truth
pedestrians while also introducing a large number of false positives. Next,
multiple deep neural networks are used in parallel for further refinement of
these pedestrian candidates. We introduce a soft-rejection based network fusion
method to fuse the soft metrics from all networks together to generate the
final confidence scores. Our method performs better than existing
state-of-the-arts, especially when detecting small-size and occluded
pedestrians. Furthermore, we propose a method for integrating pixel-wise
semantic segmentation network into the network fusion architecture as a
reinforcement to the pedestrian detector. The approach outperforms
state-of-the-art methods on most protocols on Caltech Pedestrian dataset, with
significant boosts on several protocols. It is also faster than all other
methods.
Learning Data Manifolds with a Cutting Plane Method
Authors: SueYeon Chung, Uri Cohen, Haim Sompolinsky, Daniel D. Lee
Date: 2017-05-28 00:00:00
Tasks: Data Augmentation

We consider the problem of classifying data manifolds where each manifold
represents invariances that are parameterized by continuous degrees of freedom.
Conventional data augmentation methods rely upon sampling large numbers of
training examples from these manifolds; instead, we propose an iterative
algorithm called M_{CP} based upon a cutting-plane approach that efficiently
solves a quadratic semi-infinite programming problem to find the maximum margin
solution. We provide a proof of convergence as well as a polynomial bound on
the number of iterations required for a desired tolerance in the objective
function. The efficiency and performance of M_{CP} are demonstrated in
high-dimensional simulations and on image manifolds generated from the ImageNet
dataset. Our results indicate that M_{CP} is able to rapidly learn good
classifiers and shows superior generalization performance compared with
conventional maximum margin methods using data augmentation methods.
Mining of health and disease events on Twitter: validating search protocols within the setting of Indonesia
Authors: Aditya L. Ramadona, Rendra Agusta, Sulistyawati, Lutfan Lazuardi, Anwar D. Cahyono, Åsa Holmner, Fatwa S. T. Dewi, Hari Kusnanto, Joacim Röcklov
Date: 2016-08-21 00:00:00
Tasks: General Classification

This study seeks to validate a search protocol of ill health-related terms
using Twitter data which can later be used to understand if, and how, Twitter
can reveal information on the current health situation. We extracted
conversations related to health and disease postings on Twitter using a set of
pre-defined keywords, assessed the prevalence, frequency, and timing of such
content in these conversations, and validated how this search protocol was able
to detect relevant disease tweets. Classification and Regression Trees (CART)
algorithm was used to train and test search protocols of disease and health
hits comparing to those identified by our team. The accuracy of predictions
showed a good validity with AUC beyond 0.8. Our study shows that monitoring of
public sentiment on Twitter can be used as a real-time proxy for health events.
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
Authors: Ahmed M. Alaa, Mihaela van der Schaar
Date: 2017-04-10 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Bayesian Inference, counterfactual, Gaussian Processes, Multi-Task Learning, Selection bias

Predicated on the increasing abundance of electronic health records, we
investi- gate the problem of inferring individualized treatment effects using
observational data. Stemming from the potential outcomes model, we propose a
novel multi- task learning framework in which factual and counterfactual
outcomes are mod- eled as the outputs of a function in a vector-valued
reproducing kernel Hilbert space (vvRKHS). We develop a nonparametric Bayesian
method for learning the treatment effects using a multi-task Gaussian process
(GP) with a linear coregion- alization kernel as a prior over the vvRKHS. The
Bayesian approach allows us to compute individualized measures of confidence in
our estimates via pointwise credible intervals, which are crucial for realizing
the full potential of precision medicine. The impact of selection bias is
alleviated via a risk-based empirical Bayes method for adapting the multi-task
GP prior, which jointly minimizes the empirical error in factual outcomes and
the uncertainty in (unobserved) counter- factual outcomes. We conduct
experiments on observational datasets for an inter- ventional social program
applied to premature infants, and a left ventricular assist device applied to
cardiac patients wait-listed for a heart transplant. In both experi- ments, we
show that our method significantly outperforms the state-of-the-art.
Expectation Propagation for t-Exponential Family Using Q-Algebra
Authors: Futoshi Futami, Issei Sato, Masashi Sugiyama
Date: 2017-05-25 00:00:00
Proceeding: NeurIPS 2017 12

Exponential family distributions are highly useful in machine learning since
their calculation can be performed efficiently through natural parameters. The
exponential family has recently been extended to the t-exponential family,
which contains Student-t distributions as family members and thus allows us to
handle noisy data well. However, since the t-exponential family is denied by
the deformed exponential, we cannot derive an efficient learning algorithm for
the t-exponential family such as expectation propagation (EP). In this paper,
we borrow the mathematical tools of q-algebra from statistical physics and show
that the pseudo additivity of distributions allows us to perform calculation of
t-exponential family distributions through natural parameters. We then develop
an expectation propagation (EP) algorithm for the t-exponential family, which
provides a deterministic approximation to the posterior or predictive
distribution with simple moment matching. We finally apply the proposed EP
algorithm to the Bayes point machine and Student-t process classication, and
demonstrate their performance numerically.
Learning Fast Sparsifying Transforms
Authors: Cristian Rusu, John Thompson
Date: 2016-11-24 00:00:00
Tasks: Dictionary Learning

Given a dataset, the task of learning a transform that allows sparse
representations of the data bears the name of dictionary learning. In many
applications, these learned dictionaries represent the data much better than
the static well-known transforms (Fourier, Hadamard etc.). The main downside of
learned transforms is that they lack structure and therefore they are not
computationally efficient, unlike their classical counterparts. These posse
several difficulties especially when using power limited hardware such as
mobile devices, therefore discouraging the application of sparsity techniques
in such scenarios. In this paper we construct orthogonal and non-orthogonal
dictionaries that are factorized as a product of a few basic transformations.
In the orthogonal case, we solve exactly the dictionary update problem for one
basic transformation, which can be viewed as a generalized Givens rotation, and
then propose to construct orthogonal dictionaries that are a product of these
transformations, guaranteeing their fast manipulation. We also propose a method
to construct fast square but non-orthogonal dictionaries that are factorized as
a product of few transforms that can be viewed as a further generalization of
Givens rotations to the non-orthogonal setting. We show how the proposed
transforms can balance very well data representation performance and
computational complexity. We also compare with classical fast and learned
general and orthogonal transforms.
Causal Effect Identification in Acyclic Directed Mixed Graphs and Gated Models
Authors: Jose M. Peña, Marcus Bendtsen
Date: 2016-12-22 00:00:00

We introduce a new family of graphical models that consists of graphs with
possibly directed, undirected and bidirected edges but without directed cycles.
We show that these models are suitable for representing causal models with
additive error terms. We provide a set of sufficient graphical criteria for the
identification of arbitrary causal effects when the new models contain directed
and undirected edges but no bidirected edge. We also provide a necessary and
sufficient graphical criterion for the identification of the causal effect of a
single variable on the rest of the variables. Moreover, we develop an exact
algorithm for learning the new models from observational and interventional
data via answer set programming. Finally, we introduce gated models for causal
effect identification, a new family of graphical models that exploits context
specific independences to identify additional causal effects.
Bayesian Unification of Gradient and Bandit-based Learning for Accelerated Global Optimisation
Authors: Ole-Christoffer Granmo
Date: 2017-05-28 00:00:00
Tasks: Navigate

Bandit based optimisation has a remarkable advantage over gradient based
approaches due to their global perspective, which eliminates the danger of
getting stuck at local optima. However, for continuous optimisation problems or
problems with a large number of actions, bandit based approaches can be
hindered by slow learning. Gradient based approaches, on the other hand,
navigate quickly in high-dimensional continuous spaces through local
optimisation, following the gradient in fine grained steps. Yet, apart from
being susceptible to local optima, these schemes are less suited for online
learning due to their reliance on extensive trial-and-error before the optimum
can be identified. In this paper, we propose a Bayesian approach that unifies
the above two paradigms in one single framework, with the aim of combining
their advantages. At the heart of our approach we find a stochastic linear
approximation of the function to be optimised, where both the gradient and
values of the function are explicitly captured. This allows us to learn from
both noisy function and gradient observations, and predict these properties
across the action space to support optimisation. We further propose an
accompanying bandit driven exploration scheme that uses Bayesian credible
bounds to trade off exploration against exploitation. Our empirical results
demonstrate that by unifying bandit and gradient based learning, one obtains
consistently improved performance across a wide spectrum of problem
environments. Furthermore, even when gradient feedback is unavailable, the
flexibility of our model, including gradient prediction, still allows us
outperform competing approaches, although with a smaller margin. Due to the
pervasiveness of bandit based optimisation, our scheme opens up for improved
performance both in meta-optimisation and in applications where gradient
related information is readily available.
Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development
Authors: Fang Liu, Likai Du, Dongju Zhang, Jun Gao
Date: 2017-05-28 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

The excited states of polyatomic systems are rather complex, and often
exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway
often fails to sufficiently characterize excited state motions due to their
highly non-equilibrium nature. Here, we proposed a time series guided
clustering algorithm to generate most relevant meta-stable patterns directly
from ab initio dynamic trajectories. Based on the knowledge of these
meta-stable patterns, we suggested an interpolation scheme with only a concrete
and finite set of known patterns to accurately predict the ground and excited
state properties of the entire dynamics trajectories. As illustrated with the
example of sinapic acids, the estimation error for both ground and excited
state is very close, which indicates one could predict the ground and excited
state molecular properties with similar accuracy. These results may provide us
some insights to construct an excited state force field with compatible energy
terms as traditional ones.
A proposal for ethically traceable artificial intelligence
Authors: Christopher A. Tucker
Date: 2017-03-06 00:00:00

Although the problem of a critique of robotic behavior in near-unanimous
agreement to human norms seems intractable, a starting point of such an
ambition is a framework of the collection of knowledge a priori and experience
a posteriori categorized as a set of synthetical judgments available to the
intelligence, translated into computer code. If such a proposal were
successful, an algorithm with ethically traceable behavior and cogent
equivalence to human cognition is established. This paper will propose the
application of Kant's critique of reason to current programming constructs of
an autonomous intelligent system.
Dilated Residual Networks
Authors: Fisher Yu, Vladlen Koltun, Thomas Funkhouser
Date: 2017-05-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: Classification, General Classification, image-classification, Image Classification, Object Localization, Scene Understanding, Semantic Segmentation

Convolutional networks for image classification progressively reduce
resolution until the image is represented by tiny feature maps in which the
spatial structure of the scene is no longer discernible. Such loss of spatial
acuity can limit image classification accuracy and complicate the transfer of
the model to downstream applications that require detailed scene understanding.
These problems can be alleviated by dilation, which increases the resolution of
output feature maps without reducing the receptive field of individual neurons.
We show that dilated residual networks (DRNs) outperform their non-dilated
counterparts in image classification without increasing the model's depth or
complexity. We then study gridding artifacts introduced by dilation, develop an
approach to removing these artifacts (`degridding'), and show that this further
increases the performance of DRNs. In addition, we show that the accuracy
advantage of DRNs is further magnified in downstream applications such as
object localization and semantic segmentation.
Multi-channel Weighted Nuclear Norm Minimization for Real Color Image Denoising
Authors: Jun Xu, Lei Zhang, David Zhang, Xiangchu Feng
Date: 2017-05-28 00:00:00
Proceeding: ICCV 2017 10
Tasks: Color Image Denoising, Denoising, Image Denoising

Most of the existing denoising algorithms are developed for grayscale images,
while it is not a trivial work to extend them for color image denoising because
the noise statistics in R, G, B channels can be very different for real noisy
images. In this paper, we propose a multi-channel (MC) optimization model for
real color image denoising under the weighted nuclear norm minimization (WNNM)
framework. We concatenate the RGB patches to make use of the channel
redundancy, and introduce a weight matrix to balance the data fidelity of the
three channels in consideration of their different noise statistics. The
proposed MC-WNNM model does not have an analytical solution. We reformulate it
into a linear equality-constrained problem and solve it with the alternating
direction method of multipliers. Each alternative updating step has closed-form
solution and the convergence can be guaranteed. Extensive experiments on both
synthetic and real noisy image datasets demonstrate the superiority of the
proposed MC-WNNM over state-of-the-art denoising methods.