pwc-archive/papers-with-abstracts huggingface dataset:
Task-based End-to-end Model Learning in Stochastic Optimization
Authors: Priya L. Donti, Brandon Amos, J. Zico Kolter
Date: 2017-03-13 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: BIG-bench Machine Learning, Scheduling, Stochastic Optimization

With the increasing popularity of machine learning techniques, it has become
common to see prediction algorithms operating within some larger process.
However, the criteria by which we train these algorithms often differ from the
ultimate criteria on which we evaluate them. This paper proposes an end-to-end
approach for learning probabilistic machine learning models in a manner that
directly captures the ultimate task-based objective for which they will be
used, within the context of stochastic programming. We present three
experimental evaluations of the proposed approach: a classical inventory stock
problem, a real-world electrical grid scheduling task, and a real-world energy
storage arbitrage task. We show that the proposed approach can outperform both
traditional modeling and purely black-box policy optimization approaches in
these applications.
Analysis on the Nonlinear Dynamics of Deep Neural Networks: Topological Entropy and Chaos
Authors: Husheng Li
Date: 2018-04-03 00:00:00
Tasks: General Classification

The theoretical explanation for deep neural network (DNN) is still an open
problem. In this paper DNN is considered as a discrete-time dynamical system
due to its layered structure. The complexity provided by the nonlinearity in
the dynamics is analyzed in terms of topological entropy and chaos
characterized by Lyapunov exponents. The properties revealed for the dynamics
of DNN are applied to analyze the corresponding capabilities of classification
and generalization.
Predicting Gross Movie Revenue
Authors: Sharmistha Dey
Date: 2018-04-03 00:00:00

'There is no terror in the bang, only is the anticipation of it' - Alfred
Hitchcock.
  Yet there is everything in correctly anticipating the bang a movie would make
in the box-office. Movies make a high profile, billion dollar industry and
prediction of movie revenue can be very lucrative. Predicted revenues can be
used for planning both the production and distribution stages. For example,
projected gross revenue can be used to plan the remuneration of the actors and
crew members as well as other parts of the budget [1].
  Success or failure of a movie can depend on many factors: star-power, release
date, budget, MPAA (Motion Picture Association of America) rating, plot and the
highly unpredictable human reactions. The enormity of the number of exogenous
variables makes manual revenue prediction process extremely difficult. However,
in the era of computer and data sciences, volumes of data can be efficiently
processed and modelled. Hence the tough job of predicting gross revenue of a
movie can be simplified with the help of modern computing power and the
historical data available as movie databases [2].
Self-Organization and Artificial Life: A Review
Authors: Carlos Gershenson, Vito Trianni, Justin Werfel, Hiroki Sayama
Date: 2018-04-03 00:00:00
Tasks: Artificial Life

Self-organization has been an important concept within a number of
disciplines, which Artificial Life (ALife) also has heavily utilized since its
inception. The term and its implications, however, are often confusing or
misinterpreted. In this work, we provide a mini-review of self-organization and
its relationship with ALife, aiming at initiating discussions on this important
topic with the interested audience. We first articulate some fundamental
aspects of self-organization, outline its usage, and review its applications to
ALife within its soft, hard, and wet domains. We also provide perspectives for
further research.
A Modified Image Comparison Algorithm Using Histogram Features
Authors: Anas M. Al-Oraiqat, Natalya S. Kostyukova
Date: 2018-04-03 00:00:00

This article discuss the problem of color image content comparison.
Particularly, methods of image content comparison are analyzed, restrictions of
color histogram are described and a modified method of images content
comparison is proposed. This method uses the color histograms and considers
color locations. Testing and analyzing of based and modified algorithms are
performed. The modified method shows 97% average precision for a collection
containing about 700 images without loss of the advantages of based method,
i.e. scale and rotation invariant.
Homology-Preserving Multi-Scale Graph Skeletonization Using Mapper on Graphs
Authors: Paul Rosen, Mustafa Hajij, Bei Wang
Date: 2018-04-03 00:00:00
Tasks: Clustering, Topological Data Analysis

Node-link diagrams are a popular method for representing graphs that capture relationships between individuals, businesses, proteins, and telecommunication endpoints. However, node-link diagrams may fail to convey insights regarding graph structures, even for moderately sized data of a few hundred nodes, due to visual clutter. We propose to apply the mapper construction -- a popular tool in topological data analysis -- to graph visualization, which provides a strong theoretical basis for summarizing the data while preserving their core structures. We develop a variation of the mapper construction targeting weighted, undirected graphs, called {\mog}, which generates homology-preserving skeletons of graphs. We further show how the adjustment of a single parameter enables multi-scale skeletonization of the input graph. We provide a software tool that enables interactive explorations of such skeletons and demonstrate the effectiveness of our method for synthetic and real-world data.
Probing Physics Knowledge Using Tools from Developmental Psychology
Authors: Luis Piloto, Ari Weinstein, Dhruva TB, Arun Ahuja, Mehdi Mirza, Greg Wayne, David Amos, Chia-Chun Hung, Matt Botvinick
Date: 2018-04-03 00:00:00

In order to build agents with a rich understanding of their environment, one
key objective is to endow them with a grasp of intuitive physics; an ability to
reason about three-dimensional objects, their dynamic interactions, and
responses to forces. While some work on this problem has taken the approach of
building in components such as ready-made physics engines, other research aims
to extract general physical concepts directly from sensory data. In the latter
case, one challenge that arises is evaluating the learning system. Research on
intuitive physics knowledge in children has long employed a violation of
expectations (VOE) method to assess children's mastery of specific physical
concepts. We take the novel step of applying this method to artificial learning
systems. In addition to introducing the VOE technique, we describe a set of
probe datasets inspired by classic test stimuli from developmental psychology.
We test a baseline deep learning system on this battery, as well as on a
physics learning dataset ("IntPhys") recently posed by another research group.
Our results show how the VOE technique may provide a useful tool for tracking
physics knowledge in future research.
Feature selection in weakly coherent matrices
Authors: Stephane Chretien, Zhen-Wai Olivier HO
Date: 2018-04-03 00:00:00
Tasks: feature selection

A problem of paramount importance in both pure (Restricted Invertibility
problem) and applied mathematics (Feature extraction) is the one of selecting a
submatrix of a given matrix, such that this submatrix has its smallest singular
value above a specified level. Such problems can be addressed using
perturbation analysis. In this paper, we propose a perturbation bound for the
smallest singular value of a given matrix after appending a column, under the
assumption that its initial coherence is not large, and we use this bound to
derive a fast algorithm for feature extraction.
Synthesizing Programs for Images using Reinforced Adversarial Learning
Authors: Yaroslav Ganin, tejas kulkarni, Igor Babuschkin, S. M. Ali Eslami, Oriol Vinyals
Date: 2018-04-03 00:00:00
Proceeding: ICML 2018 7
Tasks: Reinforcement Learning

Advances in deep generative networks have led to impressive results in recent
years. Nevertheless, such models can often waste their capacity on the minutiae
of datasets, presumably due to weak inductive biases in their decoders. This is
where graphics engines may come in handy since they abstract away low-level
details and represent images as high-level programs. Current methods that
combine deep learning and renderers are limited by hand-crafted likelihood or
distance functions, a need for large amounts of supervision, or difficulties in
scaling their inference algorithms to richer datasets. To mitigate these
issues, we present SPIRAL, an adversarially trained agent that generates a
program which is executed by a graphics engine to interpret and sample images.
The goal of this agent is to fool a discriminator network that distinguishes
between real and rendered data, trained with a distributed reinforcement
learning setup without any supervision. A surprising finding is that using the
discriminator's output as a reward signal is the key to allow the agent to make
meaningful progress at matching the desired output rendering. To the best of
our knowledge, this is the first demonstration of an end-to-end, unsupervised
and adversarial inverse graphics agent on challenging real world (MNIST,
Omniglot, CelebA) and synthetic 3D datasets.
Visual Object Categorization Based on Hierarchical Shape Motifs Learned From Noisy Point Cloud Decompositions
Authors: Christian A. Mueller, Andreas Birk
Date: 2018-04-03 00:00:00
Tasks: Object, Object Categorization

Object shape is a key cue that contributes to the semantic understanding of
objects. In this work we focus on the categorization of real-world object point
clouds to particular shape types. Therein surface description and
representation of object shape structure have significant influence on shape
categorization accuracy, when dealing with real-world scenes featuring noisy,
partial and occluded object observations. An unsupervised hierarchical learning
procedure is utilized here to symbolically describe surface characteristics on
multiple semantic levels. Furthermore, a constellation model is proposed that
hierarchically decomposes objects. The decompositions are described as
constellations of symbols (shape motifs) in a gradual order, hence reflecting
shape structure from local to global, i.e., from parts over groups of parts to
entire objects. The combination of this multi-level description of surfaces and
the hierarchical decomposition of shapes leads to a representation which allows
to conceptualize shapes. An object discrimination has been observed in
experiments with seven categories featuring instances with sensor noise,
occlusions as well as inter-category and intra-category similarities.
Experiments include the evaluation of the proposed description and shape
decomposition approach, and comparisons to Fast Point Feature Histograms, a
Vocabulary Tree and a neural network-based Deep Learning method. Furthermore,
experiments are conducted with alternative datasets which analyze the
generalization capability of the proposed approach.
Renewal Monte Carlo: Renewal theory based reinforcement learning
Authors: Jayakumar Subramanian, Aditya Mahajan
Date: 2018-04-03 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we present an online reinforcement learning algorithm, called
Renewal Monte Carlo (RMC), for infinite horizon Markov decision processes with
a designated start state. RMC is a Monte Carlo algorithm and retains the
advantages of Monte Carlo methods including low bias, simplicity, and ease of
implementation while, at the same time, circumvents their key drawbacks of high
variance and delayed (end of episode) updates. The key ideas behind RMC are as
follows. First, under any reasonable policy, the reward process is ergodic. So,
by renewal theory, the performance of a policy is equal to the ratio of
expected discounted reward to the expected discounted time over a regenerative
cycle. Second, by carefully examining the expression for performance gradient,
we propose a stochastic approximation algorithm that only requires estimates of
the expected discounted reward and discounted time over a regenerative cycle
and their gradients. We propose two unbiased estimators for evaluating
performance gradients---a likelihood ratio based estimator and a simultaneous
perturbation based estimator---and show that for both estimators, RMC converges
to a locally optimal policy. We generalize the RMC algorithm to post-decision
state models and also present a variant that converges faster to an
approximately optimal policy. We conclude by presenting numerical experiments
on a randomly generated MDP, event-triggered communication, and inventory
management.
Deformable Shape Completion with Graph Convolutional Autoencoders
Authors: Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia
Date: 2017-12-01 00:00:00
Proceeding: CVPR 2018 6

The availability of affordable and portable depth sensors has made scanning
objects and people simpler than ever. However, dealing with occlusions and
missing parts is still a significant challenge. The problem of reconstructing a
(possibly non-rigidly moving) 3D object from a single or multiple partial scans
has received increasing attention in recent years. In this work, we propose a
novel learning-based method for the completion of partial shapes. Unlike the
majority of existing approaches, our method focuses on objects that can undergo
non-rigid deformations. The core of our method is a variational autoencoder
with graph convolutional operations that learns a latent space for complete
realistic shapes. At inference, we optimize to find the representation in this
latent space that best fits the generated shape to the known partial input. The
completed shape exhibits a realistic appearance on the unknown part. We show
promising results towards the completion of synthetic and real scans of human
body and face meshes exhibiting different styles of articulation and
partiality.
Quantifying multivariate redundancy with maximum entropy decompositions of mutual information
Authors: Daniel Chicharro
Date: 2017-08-13 00:00:00

Williams and Beer (2010) proposed a nonnegative mutual information
decomposition, based on the construction of redundancy lattices, which allows
separating the information that a set of variables contains about a target
variable into nonnegative components interpretable as the unique information of
some variables not provided by others as well as redundant and synergistic
components. However, the definition of multivariate measures of redundancy that
comply with nonnegativity and conform to certain axioms that capture
conceptually desirable properties of redundancy has proven to be elusive. We
here present a procedure to determine nonnegative multivariate redundancy
measures, within the maximum entropy framework. In particular, we generalize
existing bivariate maximum entropy measures of redundancy and unique
information, defining measures of the redundant information that a group of
variables has about a target, and of the unique redundant information that a
group of variables has about a target that is not redundant with information
from another group. The two key ingredients for this approach are: First, the
identification of a type of constraints on entropy maximization that allows
isolating components of redundancy and unique redundancy by mirroring them to
synergy components. Second, the construction of rooted tree-based
decompositions of the mutual information, which conform to the axioms of the
redundancy lattice by the local implementation at each tree node of binary
unfoldings of the information using hierarchically related maximum entropy
constraints. Altogether, the proposed measures quantify the different
multivariate redundancy contributions of a nonnegative mutual information
decomposition consistent with the redundancy lattice.
Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation
Authors: Helge Rhodin, Mathieu Salzmann, Pascal Fua
Date: 2018-04-03 00:00:00
Proceeding: ECCV 2018 9
Tasks: 3D geometry, 3D Human Pose Estimation, Decoder, Pose Estimation, Weakly-supervised 3D Human Pose Estimation

Modern 3D human pose estimation techniques rely on deep networks, which
require large amounts of training data. While weakly-supervised methods require
less supervision, by utilizing 2D poses or multi-view imagery without
annotations, they still need a sufficiently large set of samples with 3D
annotations for learning to succeed.
  In this paper, we propose to overcome this problem by learning a
geometry-aware body representation from multi-view images without annotations.
To this end, we use an encoder-decoder that predicts an image from one
viewpoint given an image from another viewpoint. Because this representation
encodes 3D geometry, using it in a semi-supervised setting makes it easier to
learn a mapping from it to 3D human pose. As evidenced by our experiments, our
approach significantly outperforms fully-supervised methods given the same
amount of labeled data, and improves over other semi-supervised methods while
using as little as 1% of the labeled data.
You Must Have Clicked on this Ad by Mistake! Data-Driven Identification of Accidental Clicks on Mobile Ads with Applications to Advertiser Cost Discounting and Click-Through Rate Prediction
Authors: Gabriele Tolomei, Mounia Lalmas, Ayman Farahat, Andrew Haines
Date: 2018-04-03 00:00:00
Tasks: Click-Through Rate Prediction

In the cost per click (CPC) pricing model, an advertiser pays an ad network
only when a user clicks on an ad; in turn, the ad network gives a share of that
revenue to the publisher where the ad was impressed. Still, advertisers may be
unsatisfied with ad networks charging them for "valueless" clicks, or so-called
accidental clicks. [...] Charging advertisers for such clicks is detrimental in
the long term as the advertiser may decide to run their campaigns on other ad
networks. In addition, machine-learned click models trained to predict which ad
will bring the highest revenue may overestimate an ad click-through rate, and
as a consequence negatively impacting revenue for both the ad network and the
publisher. In this work, we propose a data-driven method to detect accidental
clicks from the perspective of the ad network. We collect observations of time
spent by users on a large set of ad landing pages - i.e., dwell time. We notice
that the majority of per-ad distributions of dwell time fit to a mixture of
distributions, where each component may correspond to a particular type of
clicks, the first one being accidental. We then estimate dwell time thresholds
of accidental clicks from that component. Using our method to identify
accidental clicks, we then propose a technique that smoothly discounts the
advertiser's cost of accidental clicks at billing time. Experiments conducted
on a large dataset of ads served on Yahoo mobile apps confirm that our
thresholds are stable over time, and revenue loss in the short term is
marginal. We also compare the performance of an existing machine-learned click
model trained on all ad clicks with that of the same model trained only on
non-accidental clicks. There, we observe an increase in both ad click-through
rate (+3.9%) and revenue (+0.2%) on ads served by the Yahoo Gemini network when
using the latter. [...]
DOCK: Detecting Objects by transferring Common-sense Knowledge
Authors: Krishna Kumar Singh, Santosh Divvala, Ali Farhadi, Yong Jae Lee
Date: 2018-04-03 00:00:00
Proceeding: ECCV 2018 9
Tasks: Attribute, Common Sense Reasoning, Semantic Similarity, Semantic Textual Similarity, Transfer Learning

We present a scalable approach for Detecting Objects by transferring
Common-sense Knowledge (DOCK) from source to target categories. In our setting,
the training data for the source categories have bounding box annotations,
while those for the target categories only have image-level annotations.
Current state-of-the-art approaches focus on image-level visual or semantic
similarity to adapt a detector trained on the source categories to the new
target categories. In contrast, our key idea is to (i) use similarity not at
the image-level, but rather at the region-level, and (ii) leverage richer
common-sense (based on attribute, spatial, etc.) to guide the algorithm towards
learning the correct detections. We acquire such common-sense cues
automatically from readily-available knowledge bases without any extra human
effort. On the challenging MS COCO dataset, we find that common-sense knowledge
can substantially improve detection performance over existing transfer-learning
baselines.
Average performance analysis of the stochastic gradient method for online PCA
Authors: Stephane Chretien, Christophe Guyeux, Zhen-Wai Olivier HO
Date: 2018-04-03 00:00:00

This paper studies the complexity of the stochastic gradient algorithm for
PCA when the data are observed in a streaming setting. We also propose an
online approach for selecting the learning rate. Simulation experiments confirm
the practical relevance of the plain stochastic gradient approach and that
drastic improvements can be achieved by learning the learning rate.
Compact Neural Networks based on the Multiscale Entanglement Renormalization Ansatz
Authors: Andrew Hallam, Edward Grant, Vid Stojevic, Simone Severini, Andrew G. Green
Date: 2017-11-09 00:00:00
Proceeding: ICLR 2018 1

This paper demonstrates a method for tensorizing neural networks based upon
an efficient way of approximating scale invariant quantum states, the
Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a
replacement for the fully connected layers in a convolutional neural network
and test this implementation on the CIFAR-10 and CIFAR-100 datasets. The
proposed method outperforms factorization using tensor trains, providing
greater compression for the same level of accuracy and greater accuracy for the
same level of compression. We demonstrate MERA layers with 14000 times fewer
parameters and a reduction in accuracy of less than 1% compared to the
equivalent fully connected layers, scaling like O(N).
Weakly Supervised Action Localization by Sparse Temporal Pooling Network
Authors: Phuc Nguyen, Ting Liu, Gautam Prasad, Bohyung Han
Date: 2017-12-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Classification, Action Localization, Temporal Action Localization, Temporal Localization, Weakly Supervised Action Localization, Weakly-supervised Temporal Action Localization

We propose a weakly supervised temporal action localization algorithm on
untrimmed videos using convolutional neural networks. Our algorithm learns from
video-level class labels and predicts temporal intervals of human actions with
no requirement of temporal localization annotations. We design our network to
identify a sparse subset of key segments associated with target actions in a
video using an attention module and fuse the key segments through adaptive
temporal pooling. Our loss function is comprised of two terms that minimize the
video-level action classification error and enforce the sparsity of the segment
selection. At inference time, we extract and score temporal proposals using
temporal class activations and class-agnostic attentions to estimate the time
intervals that correspond to target actions. The proposed algorithm attains
state-of-the-art results on the THUMOS14 dataset and outstanding performance on
ActivityNet1.3 even with its weak supervision.