pwc-archive/papers-with-abstracts huggingface dataset:
On Identification of Distribution Grids
Authors: Omid Ardakanian, Vincent W. S. Wong, Roel Dobbe, Steven H. Low, Alexandra von Meier, Claire Tomlin, Ye Yuan
Date: 2017-11-05 00:00:00

Large-scale integration of distributed energy resources into residential
distribution feeders necessitates careful control of their operation through
power flow analysis. While the knowledge of the distribution system model is
crucial for this type of analysis, it is often unavailable or outdated. The
recent introduction of synchrophasor technology in low-voltage distribution
grids has created an unprecedented opportunity to learn this model from
high-precision, time-synchronized measurements of voltage and current phasors
at various locations. This paper focuses on joint estimation of model
parameters (admittance values) and operational structure of a poly-phase
distribution network from the available telemetry data via the lasso, a method
for regression shrinkage and selection. We propose tractable convex programs
capable of tackling the low rank structure of the distribution system and
develop an online algorithm for early detection and localization of critical
events that induce a change in the admittance matrix. The efficacy of these
techniques is corroborated through power flow studies on four three-phase
radial distribution systems serving real household demands.
Modal Regression based Atomic Representation for Robust Face Recognition
Authors: Yulong Wang, Yuan Yan Tang, Luoqing Li, Hong Chen
Date: 2017-11-05 00:00:00
Tasks: Face Recognition, General Classification, regression, Robust Face Recognition

Representation based classification (RC) methods such as sparse RC (SRC) have
shown great potential in face recognition in recent years. Most previous RC
methods are based on the conventional regression models, such as lasso
regression, ridge regression or group lasso regression. These regression models
essentially impose a predefined assumption on the distribution of the noise
variable in the query sample, such as the Gaussian or Laplacian distribution.
However, the complicated noises in practice may violate the assumptions and
impede the performance of these RC methods. In this paper, we propose a modal
regression based atomic representation and classification (MRARC) framework to
alleviate such limitation. Unlike previous RC methods, the MRARC framework does
not require the noise variable to follow any specific predefined distributions.
This gives rise to the capability of MRARC in handling various complex noises
in reality. Using MRARC as a general platform, we also develop four novel RC
methods for unimodal and multimodal face recognition, respectively. In
addition, we devise a general optimization algorithm for the unified MRARC
framework based on the alternating direction method of multipliers (ADMM) and
half-quadratic theory. The experiments on real-world data validate the efficacy
of MRARC for robust face recognition.
Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions
Authors: Hossein Soleimani, Adarsh Subbaswamy, Suchi Saria
Date: 2017-04-06 00:00:00
Tasks: counterfactual, Counterfactual Reasoning, Gaussian Processes

Treatment effects can be estimated from observational data as the difference
in potential outcomes. In this paper, we address the challenge of estimating
the potential outcome when treatment-dose levels can vary continuously over
time. Further, the outcome variable may not be measured at a regular frequency.
Our proposed solution represents the treatment response curves using linear
time-invariant dynamical systems---this provides a flexible means for modeling
response over time to highly variable dose curves. Moreover, for multivariate
data, the proposed method: uncovers shared structure in treatment response and
the baseline across multiple markers; and, flexibly models challenging
correlation structure both across and within signals over time. For this, we
build upon the framework of multiple-output Gaussian Processes. On simulated
and a challenging clinical dataset, we show significant gains in accuracy over
state-of-the-art models.
HPX Smart Executors
Authors: Zahra Khatami, Lukas Troska, Hartmut Kaiser, J. Ramanujam, Adrian Serio
Date: 2017-11-05 00:00:00

The performance of many parallel applications depends on loop-level
parallelism. However, manually parallelizing all loops may result in degrading
parallel performance, as some of them cannot scale desirably to a large number
of threads. In addition, the overheads of manually tuning loop parameters might
prevent an application from reaching its maximum parallel performance. We
illustrate how machine learning techniques can be applied to address these
challenges. In this research, we develop a framework that is able to
automatically capture the static and dynamic information of a loop. Moreover,
we advocate a novel method by introducing HPX smart executors for determining
the execution policy, chunk size, and prefetching distance of an HPX loop to
achieve higher possible performance by feeding static information captured
during compilation and runtime-based dynamic information to our learning model.
Our evaluated execution results show that using these smart executors can speed
up the HPX execution process by around 12%-35% for the Matrix Multiplication,
Stream and $2D$ Stencil benchmarks compared to setting their HPX loop's
execution policy/parameters manually or using HPX auto-parallelization
techniques.
Hierarchical Implicit Models and Likelihood-Free Variational Inference
Authors: Dustin Tran, Rajesh Ranganath, David M. Blei
Date: 2017-02-28 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Generative Adversarial Network, Text Generation, Variational Inference

Implicit probabilistic models are a flexible class of models defined by a
simulation process for data. They form the basis for theories which encompass
our understanding of the physical world. Despite this fundamental nature, the
use of implicit models remains limited due to challenges in specifying complex
latent structure in them, and in performing inferences in such models with
large data sets. In this paper, we first introduce hierarchical implicit models
(HIMs). HIMs combine the idea of implicit densities with hierarchical Bayesian
modeling, thereby defining models via simulators of data with rich hidden
structure. Next, we develop likelihood-free variational inference (LFVI), a
scalable variational inference algorithm for HIMs. Key to LFVI is specifying a
variational family that is also implicit. This matches the model's flexibility
and allows for accurate approximation of the posterior. We demonstrate diverse
applications: a large-scale physical simulator for predator-prey populations in
ecology; a Bayesian generative adversarial network for discrete data; and a
deep implicit model for text generation.
Semantic Web Today: From Oil Rigs to Panama Papers
Authors: Rivindu Perera, Parma Nand, Boris Bacic, Wen-Hsin Yang, Kazuhiro Seki, Radek Burget
Date: 2017-11-05 00:00:00

The next leap on the internet has already started as Semantic Web. At its
core, Semantic Web transforms the document oriented web to a data oriented web
enriched with semantics embedded as metadata. This change in perspective
towards the web offers numerous benefits for vast amount of data intensive
industries that are bound to the web and its related applications. The
industries are diverse as they range from Oil & Gas exploration to the
investigative journalism, and everything in between. This paper discusses eight
different industries which currently reap the benefits of Semantic Web. The
paper also offers a future outlook into Semantic Web applications and discusses
the areas in which Semantic Web would play a key role in the future.
Learning Word Embeddings from Speech
Authors: Yu-An Chung, James Glass
Date: 2017-11-05 00:00:00
Tasks: Decoder, Learning Word Embeddings, Word Embeddings, Word Similarity

In this paper, we propose a novel deep neural network architecture,
Sequence-to-Sequence Audio2Vec, for unsupervised learning of fixed-length
vector representations of audio segments excised from a speech corpus, where
the vectors contain semantic information pertaining to the segments, and are
close to other vectors in the embedding space if their corresponding segments
are semantically similar. The design of the proposed model is based on the RNN
Encoder-Decoder framework, and borrows the methodology of continuous skip-grams
for training. The learned vector representations are evaluated on 13 widely
used word similarity benchmarks, and achieved competitive results to that of
GloVe. The biggest advantage of the proposed model is its capability of
extracting semantic information of audio segments taken directly from raw
speech, without relying on any other modalities such as text or images, which
are challenging and expensive to collect and annotate.
Distribution-Preserving k-Anonymity
Authors: Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney
Date: 2017-11-05 00:00:00
Tasks: Clustering, Quantization, Transfer Learning

Preserving the privacy of individuals by protecting their sensitive
attributes is an important consideration during microdata release. However, it
is equally important to preserve the quality or utility of the data for at
least some targeted workloads. We propose a novel framework for privacy
preservation based on the k-anonymity model that is ideally suited for
workloads that require preserving the probability distribution of the
quasi-identifier variables in the data. Our framework combines the principles
of distribution-preserving quantization and k-member clustering, and we
specialize it to two variants that respectively use intra-cluster and Gaussian
dithering of cluster centers to achieve distribution preservation. We perform
theoretical analysis of the proposed schemes in terms of distribution
preservation, and describe their utility in workloads such as covariate shift
and transfer learning where such a property is necessary. Using extensive
experiments on real-world Medical Expenditure Panel Survey data, we demonstrate
the merits of our algorithms over standard k-anonymization for a hallmark
health care application where an insurance company wishes to understand the
risk in entering a new market. Furthermore, by empirically quantifying the
reidentification risk, we also show that the proposed approaches indeed
maintain k-anonymity.
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization
Authors: Jeffrey Regier, Michael. I. Jordan, Jon Mcauliffe
Date: 2017-06-07 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Variational Inference

We introduce TrustVI, a fast second-order algorithm for black-box variational
inference based on trust-region optimization and the reparameterization trick.
At each iteration, TrustVI proposes and assesses a step based on minibatches of
draws from the variational distribution. The algorithm provably converges to a
stationary point. We implemented TrustVI in the Stan framework and compared it
to two alternatives: Automatic Differentiation Variational Inference (ADVI) and
Hessian-free Stochastic Gradient Variational Inference (HFSGVI). The former is
based on stochastic first-order optimization. The latter uses second-order
information, but lacks convergence guarantees. TrustVI typically converged at
least one order of magnitude faster than ADVI, demonstrating the value of
stochastic second-order information. TrustVI often found substantially better
variational distributions than HFSGVI, demonstrating that our convergence
theory can matter in practice.
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains
Authors: Ga Wu, Buser Say, Scott Sanner
Date: 2017-04-25 00:00:00
Proceeding: NeurIPS 2017 12

Given recent deep learning results that demonstrate the ability to
effectively optimize high-dimensional non-convex functions with gradient
descent optimization on GPUs, we ask in this paper whether symbolic gradient
optimization tools such as Tensorflow can be effective for planning in hybrid
(mixed discrete and continuous) nonlinear domains with high dimensional state
and action spaces? To this end, we demonstrate that hybrid planning with
Tensorflow and RMSProp gradient descent is competitive with mixed integer
linear program (MILP) based optimization on piecewise linear planning domains
(where we can compute optimal solutions) and substantially outperforms
state-of-the-art interior point methods for nonlinear planning domains.
Furthermore, we remark that Tensorflow is highly scalable, converging to a
strong plan on a large-scale concurrent domain with a total of 576,000
continuous action parameters distributed over a horizon of 96 time steps and
100 parallel instances in only 4 minutes. We provide a number of insights that
clarify such strong performance including observations that despite long
horizons, RMSProp avoids both the vanishing and exploding gradient problems.
Together these results suggest a new frontier for highly scalable planning in
nonlinear hybrid domains by leveraging GPUs and the power of recent advances in
gradient descent with highly optimized toolkits like Tensorflow.
Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task
Authors: Allyson Ettinger, Sudha Rao, Hal Daumé III, Emily M. Bender
Date: 2017-11-04 00:00:00
Proceeding: WS 2017 9

This paper presents a summary of the first Workshop on Building
Linguistically Generalizable Natural Language Processing Systems, and the
associated Build It Break It, The Language Edition shared task. The goal of
this workshop was to bring together researchers in NLP and linguistics with a
shared task aimed at testing the generalizability of NLP systems beyond the
distributions of their training data. We describe the motivation, setup, and
participation of the shared task, provide discussion of some highlighted
results, and discuss lessons learned.
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning
Authors: Richard Liaw, Sanjay Krishnan, Animesh Garg, Daniel Crankshaw, Joseph E. Gonzalez, Ken Goldberg
Date: 2017-11-04 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Rather than learning new control policies for each new task, it is possible,
when tasks share some structure, to compose a "meta-policy" from previously
learned policies. This paper reports results from experiments using Deep
Reinforcement Learning on a continuous-state, discrete-action autonomous
driving simulator. We explore how Deep Neural Networks can represent
meta-policies that switch among a set of previously learned policies,
specifically in settings where the dynamics of a new scenario are composed of a
mixture of previously learned dynamics and where the state observation is
possibly corrupted by sensing noise. We also report the results of experiments
varying dynamics mixes, distractor policies, magnitudes/distributions of
sensing noise, and obstacles. In a fully observed experiment, the meta-policy
learning algorithm achieves 2.6x the reward achieved by the next best policy
composition technique with 80% less exploration. In a partially observed
experiment, the meta-policy learning algorithm converges after 50 iterations
while a direct application of RL fails to converge even after 200 iterations.
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Authors: Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov
Date: 2017-05-20 00:00:00
Proceeding: NeurIPS 2017 12

Dropout-based regularization methods can be regarded as injecting random
noise with pre-defined magnitude to different parts of the neural network
during training. It was recently shown that Bayesian dropout procedure not only
improves generalization but also leads to extremely sparse neural architectures
by automatically setting the individual noise magnitude per weight. However,
this sparsity can hardly be used for acceleration since it is unstructured. In
the paper, we propose a new Bayesian model that takes into account the
computational structure of neural networks and provides structured sparsity,
e.g. removes neurons and/or convolutional channels in CNNs. To do this we
inject noise to the neurons outputs while keeping the weights unregularized. We
establish the probabilistic model with a proper truncated log-uniform prior
over the noise and truncated log-normal variational approximation that ensures
that the KL-term in the evidence lower bound is computed in closed-form. The
model leads to structured sparsity by removing elements with a low SNR from the
computation graph and provides significant acceleration on a number of deep
neural architectures. The model is easy to implement as it can be formulated as
a separate dropout-like layer.
Training Deep Networks without Learning Rates Through Coin Betting
Authors: Francesco Orabona, Tatiana Tommasi
Date: 2017-05-22 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Stochastic Optimization

Deep learning methods achieve state-of-the-art performance in many
application scenarios. Yet, these methods require a significant amount of
hyperparameters tuning in order to achieve the best results. In particular,
tuning the learning rates in the stochastic optimization process is still one
of the main bottlenecks. In this paper, we propose a new stochastic gradient
descent procedure for deep networks that does not require any learning rate
setting. Contrary to previous methods, we do not adapt the learning rates nor
we make use of the assumed curvature of the objective function. Instead, we
reduce the optimization process to a game of betting on a coin and propose a
learning-rate-free optimal algorithm for this scenario. Theoretical convergence
is proven for convex and quasi-convex functions and empirical evidence shows
the advantage of our algorithm over popular stochastic gradient algorithms.
Multi-output Polynomial Networks and Factorization Machines
Authors: Mathieu Blondel, Vlad Niculae, Takuma Otsuka, Naonori Ueda
Date: 2017-05-22 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: General Classification

Factorization machines and polynomial networks are supervised polynomial
models based on an efficient low-rank decomposition. We extend these models to
the multi-output setting, i.e., for learning vector-valued functions, with
application to multi-class or multi-task problems. We cast this as the problem
of learning a 3-way tensor whose slices share a common basis and propose a
convex formulation of that problem. We then develop an efficient conditional
gradient algorithm and prove its global convergence, despite the fact that it
involves a non-convex basis selection step. On classification tasks, we show
that our algorithm achieves excellent accuracy with much sparser models than
existing methods. On recommendation system tasks, we show how to combine our
algorithm with a reduction from ordinal regression to multi-output
classification and show that the resulting algorithm outperforms simple
baselines in terms of ranking accuracy.
Object-Centric Photometric Bundle Adjustment with Deep Shape Prior
Authors: Rui Zhu, Chaoyang Wang, Chen-Hsuan Lin, Ziyan Wang, Simon Lucey
Date: 2017-11-04 00:00:00
Tasks: Object

Reconstructing 3D shapes from a sequence of images has long been a problem of
interest in computer vision. Classical Structure from Motion (SfM) methods have
attempted to solve this problem through projected point displacement \& bundle
adjustment. More recently, deep methods have attempted to solve this problem by
directly learning a relationship between geometry and appearance. There is,
however, a significant gap between these two strategies. SfM tackles the
problem from purely a geometric perspective, taking no account of the object
shape prior. Modern deep methods more often throw away geometric constraints
altogether, rendering the results unreliable. In this paper we make an effort
to bring these two seemingly disparate strategies together. We introduce
learned shape prior in the form of deep shape generators into Photometric
Bundle Adjustment (PBA) and propose to accommodate full 3D shape generated by
the shape prior within the optimization-based inference framework,
demonstrating impressive results.
Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Authors: Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante, Steven McDonagh, Matthew Sinclair, Nick Pawlowski, Martin Rajchl, Matthew Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker
Date: 2017-11-04 00:00:00
Tasks: Deep Learning, Position, Semantic Segmentation

Deep learning approaches such as convolutional neural nets have consistently
outperformed previous methods on challenging tasks such as dense, semantic
segmentation. However, the various proposed networks perform differently, with
behaviour largely influenced by architectural choices and training settings.
This paper explores Ensembles of Multiple Models and Architectures (EMMA) for
robust performance through aggregation of predictions from a wide range of
methods. The approach reduces the influence of the meta-parameters of
individual models and the risk of overfitting the configuration to a particular
database. EMMA can be seen as an unbiased, generic deep learning model which is
shown to yield excellent performance, winning the first position in the BRATS
2017 competition among 50+ participating teams.
Gaussian Kernel in Quantum Learning
Authors: Arit Kumar Bishwas, Ashish Mani, Vasile Palade
Date: 2017-11-04 00:00:00

The Gaussian kernel is a very popular kernel function used in many machine learning algorithms, especially in support vector machines (SVMs). It is more often used than polynomial kernels when learning from nonlinear datasets, and is usually employed in formulating the classical SVM for nonlinear problems. In [3], Rebentrost et al. discussed an elegant quantum version of a least square support vector machine using quantum polynomial kernels, which is exponentially faster than the classical counterpart. This paper demonstrates a quantum version of the Gaussian kernel and analyzes its runtime complexity using the quantum random access memory (QRAM) in the context of quantum SVM. Our analysis shows that the runtime computational complexity of the quantum Gaussian kernel seems to be significantly faster as compared to its classical version.
DDD17: End-To-End DAVIS Driving Dataset
Authors: Jonathan Binas, Daniel Neil, Shih-Chii Liu, Tobi Delbruck
Date: 2017-11-04 00:00:00
Tasks: Autonomous Driving

Event cameras, such as dynamic vision sensors (DVS), and dynamic and
active-pixel vision sensors (DAVIS) can supplement other autonomous driving
sensors by providing a concurrent stream of standard active pixel sensor (APS)
images and DVS temporal contrast events. The APS stream is a sequence of
standard grayscale global-shutter image sensor frames. The DVS events represent
brightness changes occurring at a particular moment, with a jitter of about a
millisecond under most lighting conditions. They have a dynamic range of >120
dB and effective frame rates >1 kHz at data rates comparable to 30 fps
(frames/second) image sensors. To overcome some of the limitations of current
image acquisition technology, we investigate in this work the use of the
combined DVS and APS streams in end-to-end driving applications. The dataset
DDD17 accompanying this paper is the first open dataset of annotated DAVIS
driving recordings. DDD17 has over 12 h of a 346x260 pixel DAVIS sensor
recording highway and city driving in daytime, evening, night, dry and wet
weather conditions, along with vehicle speed, GPS position, driver steering,
throttle, and brake captured from the car's on-board diagnostics interface. As
an example application, we performed a preliminary end-to-end learning study of
using a convolutional neural network that is trained to predict the
instantaneous steering angle from DVS and APS visual data.