pwc-archive/papers-with-abstracts huggingface dataset:
Differentiable Submodular Maximization
Authors: Sebastian Tschiatschek, Aytunc Sahin, Andreas Krause
Date: 2018-03-05 00:00:00
Tasks: Active Learning, Data Summarization, feature selection, Product Recommendation

We consider learning of submodular functions from data. These functions are
important in machine learning and have a wide range of applications, e.g. data
summarization, feature selection and active learning. Despite their
combinatorial nature, submodular functions can be maximized approximately with
strong theoretical guarantees in polynomial time. Typically, learning the
submodular function and optimization of that function are treated separately,
i.e. the function is first learned using a proxy objective and subsequently
maximized. In contrast, we show how to perform learning and optimization
jointly. By interpreting the output of greedy maximization algorithms as
distributions over sequences of items and smoothening these distributions, we
obtain a differentiable objective. In this way, we can differentiate through
the maximization algorithms and optimize the model to work well with the
optimization algorithm. We theoretically characterize the error made by our
approach, yielding insights into the tradeoff of smoothness and accuracy. We
demonstrate the effectiveness of our approach for jointly learning and
optimizing on synthetic maximum cut data, and on real world applications such
as product recommendation and image collection summarization.
Non-asymptotic Identification of LTI Systems from a Single Trajectory
Authors: Samet Oymak, Necmiye Ozay
Date: 2018-06-14 00:00:00

We consider the problem of learning a realization for a linear time-invariant
(LTI) dynamical system from input/output data. Given a single input/output
trajectory, we provide finite time analysis for learning the system's Markov
parameters, from which a balanced realization is obtained using the classical
Ho-Kalman algorithm. By proving a stability result for the Ho-Kalman algorithm
and combining it with the sample complexity results for Markov parameters, we
show how much data is needed to learn a balanced realization of the system up
to a desired accuracy with high probability.
Safe Policy Improvement with Baseline Bootstrapping
Authors: Romain Laroche, Paul Trichelair, Rémi Tachet des Combes
Date: 2017-12-19 00:00:00
Tasks: Reinforcement Learning

This paper considers Safe Policy Improvement (SPI) in Batch Reinforcement Learning (Batch RL): from a fixed dataset and without direct access to the true environment, train a policy that is guaranteed to perform at least as well as the baseline policy used to collect the data. Our approach, called SPI with Baseline Bootstrapping (SPIBB), is inspired by the knows-what-it-knows paradigm: it bootstraps the trained policy with the baseline when the uncertainty is high. Our first algorithm, $\Pi_b$-SPIBB, comes with SPI theoretical guarantees. We also implement a variant, $\Pi_{\leq b}$-SPIBB, that is even more efficient in practice. We apply our algorithms to a motivational stochastic gridworld domain and further demonstrate on randomly generated MDPs the superiority of SPIBB with respect to existing algorithms, not only in safety but also in mean performance. Finally, we implement a model-free version of SPIBB and show its benefits on a navigation task with deep RL implementation called SPIBB-DQN, which is, to the best of our knowledge, the first RL algorithm relying on a neural network representation able to train efficiently and reliably from batch data, without any interaction with the environment.
Towards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine Learning
Authors: Roel Dobbe, Oscar Sondermeijer, David Fridovich-Keil, Daniel Arnold, Duncan Callaway, Claire Tomlin
Date: 2018-06-14 00:00:00
Tasks: BIG-bench Machine Learning

The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require extensive communication. We consider distribution systems with multiple controllable Distributed Energy Resources (DERs) and present a data-driven approach to learn control policies for each DER to reconstruct and mimic the solution to a centralized OPF problem from solely locally available information. Collectively, all local controllers closely match the centralized OPF solution, providing near optimal performance and satisfaction of system constraints. A rate distortion framework enables the analysis of how well the resulting fully decentralized control policies are able to reconstruct the OPF solution. The methodology provides a natural extension to decide what nodes a DER should communicate with to improve the reconstruction of its individual policy. The method is applied on both single- and three-phase test feeder networks using data from real loads and distributed generators, focusing on DERs that do not exhibit inter-temporal dependencies. It provides a framework for Distribution System Operators to efficiently plan and operate the contributions of DERs to achieve Distributed Energy Services in distribution networks.
Multilevel Artificial Neural Network Training for Spatially Correlated Learning
Authors: C. B. Scott, Eric Mjolsness
Date: 2018-06-14 00:00:00

Multigrid modeling algorithms are a technique used to accelerate relaxation models running on a hierarchy of similar graphlike structures. We introduce and demonstrate a new method for training neural networks which uses multilevel methods. Using an objective function derived from a graph-distance metric, we perform orthogonally-constrained optimization to find optimal prolongation and restriction maps between graphs. We compare and contrast several methods for performing this numerical optimization, and additionally present some new theoretical results on upper bounds of this type of objective function. Once calculated, these optimal maps between graphs form the core of Multiscale Artificial Neural Network (MsANN) training, a new procedure we present which simultaneously trains a hierarchy of neural network models of varying spatial resolution. Parameter information is passed between members of this hierarchy according to standard coarsening and refinement schedules from the multiscale modelling literature. In our machine learning experiments, these models are able to learn faster than default training, achieving a comparable level of error in an order of magnitude fewer training examples.
Detection limits in the high-dimensional spiked rectangular model
Authors: Ahmed El Alaoui, Michael. I. Jordan
Date: 2018-02-20 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We study the problem of detecting the presence of a single unknown spike in a
rectangular data matrix, in a high-dimensional regime where the spike has fixed
strength and the aspect ratio of the matrix converges to a finite limit. This
setup includes Johnstone's spiked covariance model. We analyze the likelihood
ratio of the spiked model against an "all noise" null model of reference, and
show it has asymptotically Gaussian fluctuations in a region below---but in
general not up to---the so-called BBP threshold from random matrix theory. Our
result parallels earlier findings of Onatski et al.\ (2013) and
Johnstone-Onatski (2015) for spherical spikes. We present a probabilistic
approach capable of treating generic product priors. In particular, sparsity in
the spike is allowed. Our approach is based on Talagrand's interpretation of
the cavity method from spin-glass theory. The question of the maximal parameter
region where asymptotic normality is expected to hold is left open. This region
is shaped by the prior in a non-trivial way. We conjecture that this is the
entire paramagnetic phase of an associated spin-glass model, and is defined by
the vanishing of the replica-symmetric solution of Lesieur et al.\ (2015).
Neural Generative Models for Global Optimization with Gradients
Authors: Louis Faury, Flavian vasile, Clément Calauzènes, Olivier Fercoq
Date: 2018-05-22 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes, global-optimization

The aim of global optimization is to find the global optimum of arbitrary
classes of functions, possibly highly multimodal ones. In this paper we focus
on the subproblem of global optimization for differentiable functions and we
propose an Evolutionary Search-inspired solution where we model point search
distributions via Generative Neural Networks. This approach enables us to model
diverse and complex search distributions based on which we can efficiently
explore complicated objective landscapes. In our experiments we show the
practical superiority of our algorithm versus classical Evolutionary Search and
gradient-based solutions on a benchmark set of multimodal functions, and
demonstrate how it can be used to accelerate Bayesian Optimization with
Gaussian Processes.
Evolving simple programs for playing Atari games
Authors: Dennis G Wilson, Sylvain Cussat-Blanc, Hervé Luga, Julian F. Miller
Date: 2018-06-14 00:00:00
Tasks: Atari Games

Cartesian Genetic Programming (CGP) has previously shown capabilities in
image processing tasks by evolving programs with a function set specialized for
computer vision. A similar approach can be applied to Atari playing. Programs
are evolved using mixed type CGP with a function set suited for matrix
operations, including image processing, but allowing for controller behavior to
emerge. While the programs are relatively small, many controllers are
competitive with state of the art methods for the Atari benchmark set and
require less training time. By evaluating the programs of the best evolved
individuals, simple but effective strategies can be found.
Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning
Authors: Xiao Zhou, Anastasios Noulas, Cecilia Mascoloo, Zhongxiang Zhao
Date: 2018-06-14 00:00:00

Cultural activity is an inherent aspect of urban life and the success of a
modern city is largely determined by its capacity to offer generous cultural
entertainment to its citizens. To this end, the optimal allocation of cultural
establishments and related resources across urban regions becomes of vital
importance, as it can reduce financial costs in terms of planning and improve
quality of life in the city, more generally. In this paper, we make use of a
large longitudinal dataset of user location check-ins from the online social
network WeChat to develop a data-driven framework for cultural planning in the
city of Beijing. We exploit rich spatio-temporal representations on user
activity at cultural venues and use a novel extended version of the traditional
latent Dirichlet allocation model that incorporates temporal information to
identify latent patterns of urban cultural interactions. Using the
characteristic typologies of mobile user cultural activities emitted by the
model, we determine the levels of demand for different types of cultural
resources across urban areas. We then compare those with the corresponding
levels of supply as driven by the presence and spatial reach of cultural venues
in local areas to obtain high resolution maps that indicate urban regions with
lack of cultural resources, and thus give suggestions for further urban
cultural planning and investment optimisation.
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
Authors: Siyuan Ma, Raef Bassily, Mikhail Belkin
Date: 2017-12-18 00:00:00
Proceeding: ICML 2018 7

In this paper we aim to formally explain the phenomenon of fast convergence
of SGD observed in modern machine learning. The key observation is that most
modern learning architectures are over-parametrized and are trained to
interpolate the data by driving the empirical loss (classification and
regression) close to zero. While it is still unclear why these interpolated
solutions perform well on test data, we show that these regimes allow for fast
convergence of SGD, comparable in number of iterations to full gradient
descent.
  For convex loss functions we obtain an exponential convergence bound for {\it
mini-batch} SGD parallel to that for full gradient descent. We show that there
is a critical batch size $m^*$ such that: (a) SGD iteration with mini-batch
size $m\leq m^*$ is nearly equivalent to $m$ iterations of mini-batch size $1$
(\emph{linear scaling regime}). (b) SGD iteration with mini-batch $m> m^*$ is
nearly equivalent to a full gradient descent iteration (\emph{saturation
regime}).
  Moreover, for the quadratic loss, we derive explicit expressions for the
optimal mini-batch and step size and explicitly characterize the two regimes
above. The critical mini-batch size can be viewed as the limit for effective
mini-batch parallelization. It is also nearly independent of the data size,
implying $O(n)$ acceleration over GD per unit of computation. We give
experimental evidence on real data which closely follows our theoretical
analyses.
  Finally, we show how our results fit in the recent developments in training
deep neural networks and discuss connections to adaptive rates for SGD and
variance reduction.
IVUS-Net: An Intravascular Ultrasound Segmentation Network
Authors: Ji Yang, Lin Tong, Mehdi Faraji, Anup Basu
Date: 2018-06-10 00:00:00
Tasks: 3D Reconstruction, GPU, Segmentation

IntraVascular UltraSound (IVUS) is one of the most effective imaging
modalities that provides assistance to experts in order to diagnose and treat
cardiovascular diseases. We address a central problem in IVUS image analysis
with Fully Convolutional Network (FCN): automatically delineate the lumen and
media-adventitia borders in IVUS images, which is crucial to shorten the
diagnosis process or benefits a faster and more accurate 3D reconstruction of
the artery. Particularly, we propose an FCN architecture, called IVUS-Net,
followed by a post-processing contour extraction step, in order to
automatically segments the interior (lumen) and exterior (media-adventitia)
regions of the human arteries. We evaluated our IVUS-Net on the test set of a
standard publicly available dataset containing 326 IVUS B-mode images with two
measurements, namely Jaccard Measure (JM) and Hausdorff Distances (HD). The
evaluation result shows that IVUS-Net outperforms the state-of-the-art lumen
and media segmentation methods by 4% to 20% in terms of HD distance. IVUS-Net
performs well on images in the test set that contain a significant amount of
major artifacts such as bifurcations, shadows, and side branches that are not
common in the training set. Furthermore, using a modern GPU, IVUS-Net segments
each IVUS frame only in 0.15 seconds. The proposed work, to the best of our
knowledge, is the first deep learning based method for segmentation of both the
lumen and the media vessel walls in 20 MHz IVUS B-mode images that achieves the
best results without any manual intervention. Code is available at
https://github.com/Kulbear/ivus-segmentation-icsm2018
Learning Human Optical Flow
Authors: Anurag Ranjan, Javier Romero, Michael J. Black
Date: 2018-06-14 00:00:00
Tasks: Optical Flow Estimation

The optical flow of humans is well known to be useful for the analysis of
human action. Given this, we devise an optical flow algorithm specifically for
human motion and show that it is superior to generic flow methods. Designing a
method by hand is impractical, so we develop a new training database of image
sequences with ground truth optical flow. For this we use a 3D model of the
human body and motion capture data to synthesize realistic flow fields. We then
train a convolutional neural network to estimate human flow fields from pairs
of images. Since many applications in human motion analysis depend on speed,
and we anticipate mobile applications, we base our method on SpyNet with
several modifications. We demonstrate that our trained network is more accurate
than a wide range of top methods on held-out test data and that it generalizes
well to real image sequences. When combined with a person detector/tracker, the
approach provides a full solution to the problem of 2D human flow estimation.
Both the code and the dataset are available for research.
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Authors: Furong Huang, Jordan Ash, John Langford, Robert Schapire
Date: 2017-06-15 00:00:00
Proceeding: ICML 2018 7

Deep neural networks are known to be difficult to train due to the
instability of back-propagation. A deep \emph{residual network} (ResNet) with
identity loops remedies this by stabilizing gradient computations. We prove a
boosting theory for the ResNet architecture. We construct $T$ weak module
classifiers, each contains two of the $T$ layers, such that the combined strong
learner is a ResNet. Therefore, we introduce an alternative Deep ResNet
training algorithm, \emph{BoostResNet}, which is particularly suitable in
non-differentiable architectures. Our proposed algorithm merely requires a
sequential training of $T$ "shallow ResNets" which are inexpensive. We prove
that the training error decays exponentially with the depth $T$ if the
\emph{weak module classifiers} that we train perform slightly better than some
weak baseline. In other words, we propose a weak learning condition and prove a
boosting theory for ResNet under the weak learning condition. Our results apply
to general multi-class ResNets. A generalization error bound based on margin
theory is proved and suggests ResNet's resistant to overfitting under network
with $l_1$ norm bounded weights.
Interactive Classification for Deep Learning Interpretation
Authors: Ángel Alexander Cabrera, Fred Hohman, Jason Lin, Duen Horng Chau
Date: 2018-06-14 00:00:00
Tasks: Classification, Deep Learning, General Classification

We present an interactive system enabling users to manipulate images to
explore the robustness and sensitivity of deep learning image classifiers.
Using modern web technologies to run in-browser inference, users can remove
image features using inpainting algorithms and obtain new classifications in
real time, which allows them to ask a variety of "what if" questions by
experimentally modifying images and seeing how the model reacts. Our system
allows users to compare and contrast what image regions humans and machine
learning models use for classification, revealing a wide range of surprising
results ranging from spectacular failures (e.g., a "water bottle" image becomes
a "concert" when removing a person) to impressive resilience (e.g., a "baseball
player" image remains correctly classified even without a glove or base). We
demonstrate our system at The 2018 Conference on Computer Vision and Pattern
Recognition (CVPR) for the audience to try it live. Our system is open-sourced
at https://github.com/poloclub/interactive-classification. A video demo is
available at https://youtu.be/llub5GcOF6w.
Structure-Infused Copy Mechanisms for Abstractive Summarization
Authors: Kaiqiang Song, Lin Zhao, Fei Liu
Date: 2018-06-14 00:00:00
Proceeding: COLING 2018 8
Tasks: Abstractive Text Summarization, Sentence

Seq2seq learning has produced promising results on summarization. However, in
many cases, system summaries still struggle to keep the meaning of the original
intact. They may miss out important words or relations that play critical roles
in the syntactic structure of source sentences. In this paper, we present
structure-infused copy mechanisms to facilitate copying important words and
relations from the source sentence to summary sentence. The approach naturally
combines source dependency structure with the copy mechanism of an abstractive
sentence summarizer. Experimental results demonstrate the effectiveness of
incorporating source-side syntactic information in the system, and our proposed
approach compares favorably to state-of-the-art methods.
Adding New Tasks to a Single Network with Weight Transformations using Binary Masks
Authors: Massimiliano Mancini, Elisa Ricci, Barbara Caputo, Samuel Rota Bulò
Date: 2018-05-28 00:00:00

Visual recognition algorithms are required today to exhibit adaptive
abilities. Given a deep model trained on a specific, given task, it would be
highly desirable to be able to adapt incrementally to new tasks, preserving
scalability as the number of new tasks increases, while at the same time
avoiding catastrophic forgetting issues. Recent work has shown that masking the
internal weights of a given original conv-net through learned binary variables
is a promising strategy. We build upon this intuition and take into account
more elaborated affine transformations of the convolutional weights that
include learned binary masks. We show that with our generalization it is
possible to achieve significantly higher levels of adaptation to new tasks,
enabling the approach to compete with fine tuning strategies by requiring
slightly more than 1 bit per network parameter per additional task. Experiments
on two popular benchmarks showcase the power of our approach, that achieves the
new state of the art on the Visual Decathlon Challenge.
Abstract Meaning Representation for Multi-Document Summarization
Authors: Kexin Liao, Logan Lebanoff, Fei Liu
Date: 2018-06-14 00:00:00
Proceeding: COLING 2018 8
Tasks: Abstract Meaning Representation, Document Summarization, Multi-Document Summarization

Generating an abstract from a collection of documents is a desirable
capability for many real-world applications. However, abstractive approaches to
multi-document summarization have not been thoroughly investigated. This paper
studies the feasibility of using Abstract Meaning Representation (AMR), a
semantic representation of natural language grounded in linguistic theory, as a
form of content representation. Our approach condenses source documents to a
set of summary graphs following the AMR formalism. The summary graphs are then
transformed to a set of summary sentences in a surface realization step. The
framework is fully data-driven and flexible. Each component can be optimized
independently using small-scale, in-domain training data. We perform
experiments on benchmark summarization datasets and report promising results.
We also describe opportunities and challenges for advancing this line of
research.
HGR-Net: A Fusion Network for Hand Gesture Segmentation and Recognition
Authors: Amirhossein Dadashzadeh, Alireza Tavakoli Targhi, Maryam Tahmasbi, Majid Mirmehdi
Date: 2018-06-14 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Hand Gesture Segmentation, Hand Segmentation, Segmentation, Semantic Segmentation

We propose a two-stage convolutional neural network (CNN) architecture for robust recognition of hand gestures, called HGR-Net, where the first stage performs accurate semantic segmentation to determine hand regions, and the second stage identifies the gesture. The segmentation stage architecture is based on the combination of fully convolutional residual network and atrous spatial pyramid pooling. Although the segmentation sub-network is trained without depth information, it is particularly robust against challenges such as illumination variations and complex backgrounds. The recognition stage deploys a two-stream CNN, which fuses the information from the red-green-blue and segmented images by combining their deep representations in a fully connected layer before classification. Extensive experiments on public datasets show that our architecture achieves almost as good as state-of-the-art performance in segmentation and recognition of static hand gestures, at a fraction of training time, run time, and model size. Our method can operate at an average of 23 ms per frame.