pwc-archive/papers-with-abstracts huggingface dataset:
Learning Dilation Factors for Semantic Segmentation of Street Scenes
Authors: Yang He, Margret Keuper, Bernt Schiele, Mario Fritz
Date: 2017-09-06 00:00:00
Tasks: Segmentation, Semantic Segmentation

Contextual information is crucial for semantic segmentation. However, finding
the optimal trade-off between keeping desired fine details and at the same time
providing sufficiently large receptive fields is non trivial. This is even more
so, when objects or classes present in an image significantly vary in size.
Dilated convolutions have proven valuable for semantic segmentation, because
they allow to increase the size of the receptive field without sacrificing
image resolution. However, in current state-of-the-art methods, dilation
parameters are hand-tuned and fixed. In this paper, we present an approach for
learning dilation parameters adaptively per channel, consistently improving
semantic segmentation results on street-scene datasets like Cityscapes and
Camvid.
"Having 2 hours to write a paper is fun!": Detecting Sarcasm in Numerical Portions of Text
Authors: Lakshya Kumar, Arpan Somani, Pushpak Bhattacharyya
Date: 2017-09-06 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Sarcasm Detection, Sentence

Sarcasm occurring due to the presence of numerical portions in text has been
quoted as an error made by automatic sarcasm detection approaches in the past.
We present a first study in detecting sarcasm in numbers, as in the case of the
sentence 'Love waking up at 4 am'. We analyze the challenges of the problem,
and present Rule-based, Machine Learning and Deep Learning approaches to detect
sarcasm in numerical portions of text. Our Deep Learning approach outperforms
four past works for sarcasm detection and Rule-based and Machine learning
approaches on a dataset of tweets, obtaining an F1-score of 0.93. This shows
that special attention to text containing numbers may be useful to improve
state-of-the-art in sarcasm detection.
Estimation of a Low-rank Topic-Based Model for Information Cascades
Authors: Ming Yu, Varun Gupta, Mladen Kolar
Date: 2017-09-06 00:00:00
Tasks: Recommendation Systems

We consider the problem of estimating the latent structure of a social network based on the observed information diffusion events, or cascades, where the observations for a given cascade consist of only the timestamps of infection for infected nodes but not the source of the infection. Most of the existing work on this problem has focused on estimating a diffusion matrix without any structural assumptions on it. In this paper, we propose a novel model based on the intuition that an information is more likely to propagate among two nodes if they are interested in similar topics which are also prominent in the information content. In particular, our model endows each node with an influence vector (which measures how authoritative the node is on each topic) and a receptivity vector (which measures how susceptible the node is for each topic). We show how this node-topic structure can be estimated from the observed cascades, and prove the consistency of the estimator. Experiments on synthetic and real data demonstrate the improved performance and better interpretability of our model compared to existing state-of-the-art methods.
An Efficient Method for Robust Projection Matrix Design
Authors: Tao Hong, Zhihui Zhu
Date: 2016-09-27 00:00:00
Tasks: Compressive Sensing

Our objective is to efficiently design a robust projection matrix $\Phi$ for
the Compressive Sensing (CS) systems when applied to the signals that are not
exactly sparse. The optimal projection matrix is obtained by mainly minimizing
the average coherence of the equivalent dictionary. In order to drop the
requirement of the sparse representation error (SRE) for a set of training data
as in [15] [16], we introduce a novel penalty function independent of a
particular SRE matrix. Without requiring of training data, we can efficiently
design the robust projection matrix and apply it for most of CS systems, like a
CS system for image processing with a conventional wavelet dictionary in which
the SRE matrix is generally not available. Simulation results demonstrate the
efficiency and effectiveness of the proposed approach compared with the
state-of-the-art methods. In addition, we experimentally demonstrate with
natural images that under similar compression rate, a CS system with a learned
dictionary in high dimensions outperforms the one in low dimensions in terms of
reconstruction accuracy. This together with the fact that our proposed method
can efficiently work in high dimension suggests that a CS system can be
potentially implemented beyond the small patches in sparsity-based image
processing.
Towards Neural Machine Translation with Latent Tree Attention
Authors: James Bradbury, Richard Socher
Date: 2017-09-06 00:00:00
Proceeding: WS 2017 9
Tasks: Decoder, Machine Translation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Segmentation, Translation

Building models that take advantage of the hierarchical structure of language
without a priori annotation is a longstanding goal in natural language
processing. We introduce such a model for the task of machine translation,
pairing a recurrent neural network grammar encoder with a novel attentional
RNNG decoder and applying policy gradient reinforcement learning to induce
unsupervised tree structures on both the source and target. When trained on
character-level datasets with no explicit segmentation or parse annotation, the
model learns a plausible segmentation and shallow parse, obtaining performance
close to an attentional baseline.
A multi-agent reinforcement learning model of common-pool resource appropriation
Authors: Julien Perolat, Joel Z. Leibo, Vinicius Zambaldi, Charles Beattie, Karl Tuyls, Thore Graepel
Date: 2017-07-20 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Humanity faces numerous problems of common-pool resource appropriation. This
class of multi-agent social dilemma includes the problems of ensuring
sustainable use of fresh water, common fisheries, grazing pastures, and
irrigation systems. Abstract models of common-pool resource appropriation based
on non-cooperative game theory predict that self-interested agents will
generally fail to find socially positive equilibria---a phenomenon called the
tragedy of the commons. However, in reality, human societies are sometimes able
to discover and implement stable cooperative solutions. Decades of behavioral
game theory research have sought to uncover aspects of human behavior that make
this possible. Most of that work was based on laboratory experiments where
participants only make a single choice: how much to appropriate. Recognizing
the importance of spatial and temporal resource dynamics, a recent trend has
been toward experiments in more complex real-time video game-like environments.
However, standard methods of non-cooperative game theory can no longer be used
to generate predictions for this case. Here we show that deep reinforcement
learning can be used instead. To that end, we study the emergent behavior of
groups of independently learning agents in a partially observed Markov game
modeling common-pool resource appropriation. Our experiments highlight the
importance of trial-and-error learning in common-pool resource appropriation
and shed light on the relationship between exclusion, sustainability, and
inequality.
Deep and Confident Prediction for Time Series at Uber
Authors: Lingxue Zhu, Nikolay Laptev
Date: 2017-09-06 00:00:00
Tasks: Anomaly Detection, Prediction, Probabilistic Time Series Forecasting, Time Series, Time Series Analysis, Time Series Anomaly Detection, Time Series Forecasting, Time Series Prediction

Reliable uncertainty estimation for time series prediction is critical in
many fields, including physics, biology, and manufacturing. At Uber,
probabilistic time series forecasting is used for robust prediction of number
of trips during special events, driver incentive allocation, as well as
real-time anomaly detection across millions of metrics. Classical time series
models are often used in conjunction with a probabilistic formulation for
uncertainty estimation. However, such models are hard to tune, scale, and add
exogenous variables to. Motivated by the recent resurgence of Long Short Term
Memory networks, we propose a novel end-to-end Bayesian deep model that
provides time series prediction along with uncertainty estimation. We provide
detailed experiments of the proposed solution on completed trips data, and
successfully apply it to large-scale time series anomaly detection at Uber.
Measuring the Similarity of Sentential Arguments in Dialog
Authors: Amita Misra, Brian Ecker, Marilyn A. Walker
Date: 2017-09-06 00:00:00

When people converse about social or political topics, similar arguments are
often paraphrased by different speakers, across many different conversations.
Debate websites produce curated summaries of arguments on such topics; these
summaries typically consist of lists of sentences that represent frequently
paraphrased propositions, or labels capturing the essence of one particular
aspect of an argument, e.g. Morality or Second Amendment. We call these
frequently paraphrased propositions ARGUMENT FACETS. Like these curated sites,
our goal is to induce and identify argument facets across multiple
conversations, and produce summaries. However, we aim to do this automatically.
We frame the problem as consisting of two steps: we first extract sentences
that express an argument from raw social media dialogs, and then rank the
extracted arguments in terms of their similarity to one another. Sets of
similar arguments are used to represent argument facets. We show here that we
can predict ARGUMENT FACET SIMILARITY with a correlation averaging 0.63
compared to a human topline averaging 0.68 over three debate topics, easily
beating several reasonable baselines.
Convolutional Gaussian Processes
Authors: Mark van der Wilk, Carl Edward Rasmussen, James Hensman
Date: 2017-09-06 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Gaussian Processes

We present a practical way of introducing convolutional structure into
Gaussian processes, making them more suited to high-dimensional inputs like
images. The main contribution of our work is the construction of an
inter-domain inducing point approximation that is well-tailored to the
convolutional kernel. This allows us to gain the generalisation benefit of a
convolutional kernel, together with fast but accurate posterior inference. We
investigate several variations of the convolutional kernel, and apply it to
MNIST and CIFAR-10, which have both been known to be challenging for Gaussian
processes. We also show how the marginal likelihood can be used to find an
optimal weighting between convolutional and RBF kernels to further improve
performance. We hope that this illustration of the usefulness of a marginal
likelihood will help automate discovering architectures in larger models.
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
Authors: Quentin Berthet, Vianney Perchet
Date: 2017-02-22 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: BIG-bench Machine Learning, Stochastic Optimization

We consider the problem of bandit optimization, inspired by stochastic
optimization and online learning problems with bandit feedback. In this
problem, the objective is to minimize a global loss function of all the
actions, not necessarily a cumulative loss. This framework allows us to study a
very general class of problems, with applications in statistics, machine
learning, and other fields. To solve this problem, we analyze the
Upper-Confidence Frank-Wolfe algorithm, inspired by techniques for bandits and
convex optimization. We give theoretical guarantees for the performance of this
algorithm over various classes of functions, and discuss the optimality of
these results.
Proving the Incompatibility of Efficiency and Strategyproofness via SMT Solving
Authors: Florian Brandl, Felix Brandt, Manuel Eberl, Christian Geist
Date: 2016-04-19 00:00:00

Two important requirements when aggregating the preferences of multiple
agents are that the outcome should be economically efficient and the
aggregation mechanism should not be manipulable. In this paper, we provide a
computer-aided proof of a sweeping impossibility using these two conditions for
randomized aggregation mechanisms. More precisely, we show that every efficient
aggregation mechanism can be manipulated for all expected utility
representations of the agents' preferences. This settles an open problem and
strengthens a number of existing theorems, including statements that were shown
within the special domain of assignment. Our proof is obtained by formulating
the claim as a satisfiability problem over predicates from real-valued
arithmetic, which is then checked using an SMT (satisfiability modulo theories)
solver. In order to verify the correctness of the result, a minimal
unsatisfiable set of constraints returned by the SMT solver was translated back
into a proof in higher-order logic, which was automatically verified by an
interactive theorem prover. To the best of our knowledge, this is the first
application of SMT solvers in computational social choice.
Clustering of Data with Missing Entries using Non-convex Fusion Penalties
Authors: Sunrita Poddar, Mathews Jacob
Date: 2017-09-06 00:00:00
Tasks: Clustering

The presence of missing entries in data often creates challenges for pattern
recognition algorithms. Traditional algorithms for clustering data assume that
all the feature values are known for every data point. We propose a method to
cluster data in the presence of missing information. Unlike conventional
clustering techniques where every feature is known for each point, our
algorithm can handle cases where a few feature values are unknown for every
point. For this more challenging problem, we provide theoretical guarantees for
clustering using a $\ell_0$ fusion penalty based optimization problem.
Furthermore, we propose an algorithm to solve a relaxation of this problem
using saturating non-convex fusion penalties. It is observed that this
algorithm produces solutions that degrade gradually with an increase in the
fraction of missing feature values. We demonstrate the utility of the proposed
method using a simulated dataset, the Wine dataset and also an under-sampled
cardiac MRI dataset. It is shown that the proposed method is a promising
clustering technique for datasets with large fractions of missing entries.
The low-rank hurdle model
Authors: Christopher Dienes
Date: 2017-09-06 00:00:00
Tasks: Imputation, Missing Values, model

A composite loss framework is proposed for low-rank modeling of data
consisting of interesting and common values, such as excess zeros or missing
values. The methodology is motivated by the generalized low-rank framework and
the hurdle method which is commonly used to analyze zero-inflated counts. The
model is demonstrated on a manufacturing data set and applied to the problem of
missing value imputation.
Depression and Self-Harm Risk Assessment in Online Forums
Authors: Andrew Yates, Arman Cohan, Nazli Goharian
Date: 2017-09-06 00:00:00
Proceeding: EMNLP 2017 9

Users suffering from mental health conditions often turn to online resources
for support, including specialized online support communities or general
communities such as Twitter and Reddit. In this work, we present a neural
framework for supporting and studying users in both types of communities. We
propose methods for identifying posts in support communities that may indicate
a risk of self-harm, and demonstrate that our approach outperforms strong
previously proposed methods for identifying such posts. Self-harm is closely
related to depression, which makes identifying depressed users on general
forums a crucial related task. We introduce a large-scale general forum dataset
("RSDD") consisting of users with self-reported depression diagnoses matched
with control users. We show how our method can be applied to effectively
identify depressed users from their use of language alone. We demonstrate that
our method outperforms strong baselines on this general forum dataset.
Multiregion Bilinear Convolutional Neural Networks for Person Re-Identification
Authors: Evgeniya Ustinova, Yaroslav Ganin, Victor Lempitsky
Date: 2015-12-16 00:00:00
Tasks: Person Re-Identification

In this work we propose a new architecture for person re-identification. As
the task of re-identification is inherently associated with embedding learning
and non-rigid appearance description, our architecture is based on the deep
bilinear convolutional network (Bilinear-CNN) that has been proposed recently
for fine-grained classification of highly non-rigid objects. While the last
stages of the original Bilinear-CNN architecture completely removes the
geometric information from consideration by performing orderless pooling, we
observe that a better embedding can be learned by performing bilinear pooling
in a more local way, where each pooling is confined to a predefined region. Our
architecture thus represents a compromise between traditional convolutional
networks and bilinear CNNs and strikes a balance between rigid matching and
completely ignoring spatial information.
  We perform the experimental validation of the new architecture on the three
popular benchmark datasets (Market-1501, CUHK01, CUHK03), comparing it to
baselines that include Bilinear-CNN as well as prior art. The new architecture
outperforms the baseline on all three datasets, while performing better than
state-of-the-art on two out of three. The code and the pretrained models of the
approach can be found at https://github.com/madkn/MultiregionBilinearCNN-ReId.
Soft Proposal Networks for Weakly Supervised Object Localization
Authors: Yi Zhu, Yanzhao Zhou, Qixiang Ye, Qiang Qiu, Jianbin Jiao
Date: 2017-09-06 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, Object Localization, Weakly Supervised Object Detection, Weakly-Supervised Object Localization

Weakly supervised object localization remains challenging, where only image
labels instead of bounding boxes are available during training. Object proposal
is an effective component in localization, but often computationally expensive
and incapable of joint optimization with some of the remaining modules. In this
paper, to the best of our knowledge, we for the first time integrate weakly
supervised object proposal into convolutional neural networks (CNNs) in an
end-to-end learning manner. We design a network component, Soft Proposal (SP),
to be plugged into any standard convolutional architecture to introduce the
nearly cost-free object proposal, orders of magnitude faster than
state-of-the-art methods. In the SP-augmented CNNs, referred to as Soft
Proposal Networks (SPNs), iteratively evolved object proposals are generated
based on the deep feature maps then projected back, and further jointly
optimized with network parameters, with image-level supervision only. Through
the unified learning process, SPNs learn better object-centric filters,
discover more discriminative visual evidence, and suppress background
interference, significantly boosting both weakly supervised object localization
and classification performance. We report the best results on popular
benchmarks, including PASCAL VOC, MS COCO, and ImageNet.
A Dual-Source Approach for 3D Human Pose Estimation from a Single Image
Authors: Umar Iqbal, Andreas Doering, Hashim Yasin, Björn Krüger, Andreas Weber, Juergen Gall
Date: 2017-05-08 00:00:00
Tasks: 2D Pose Estimation, 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation, Pose Retrieval, Retrieval

In this work we address the challenging problem of 3D human pose estimation
from single images. Recent approaches learn deep neural networks to regress 3D
pose directly from images. One major challenge for such methods, however, is
the collection of training data. Specifically, collecting large amounts of
training data containing unconstrained images annotated with accurate 3D poses
is infeasible. We therefore propose to use two independent training sources.
The first source consists of accurate 3D motion capture data, and the second
source consists of unconstrained images with annotated 2D poses. To integrate
both sources, we propose a dual-source approach that combines 2D pose
estimation with efficient 3D pose retrieval. To this end, we first convert the
motion capture data into a normalized 2D pose space, and separately learn a 2D
pose estimation model from the image data. During inference, we estimate the 2D
pose and efficiently retrieve the nearest 3D poses. We then jointly estimate a
mapping from the 3D pose space to the image and reconstruct the 3D pose. We
provide a comprehensive evaluation of the proposed method and experimentally
demonstrate the effectiveness of our approach, even when the skeleton
structures of the two sources differ substantially.
Towards Automated Cadastral Boundary Delineation from UAV Data
Authors: Sophie Crommelinck, Michael Ying Yang, Mila Koeva, Markus Gerke, Rohan Bennett, George Vosselman
Date: 2017-09-06 00:00:00
Tasks: Contour Detection, Superpixels

Unmanned aerial vehicles (UAV) are evolving as an alternative tool to acquire
land tenure data. UAVs can capture geospatial data at high quality and
resolution in a cost-effective, transparent and flexible manner, from which
visible land parcel boundaries, i.e., cadastral boundaries are delineable. This
delineation is to no extent automated, even though physical objects
automatically retrievable through image analysis methods mark a large portion
of cadastral boundaries. This study proposes (i) a workflow that automatically
extracts candidate cadastral boundaries from UAV orthoimages and (ii) a tool
for their semi-automatic processing to delineate final cadastral boundaries.
The workflow consists of two state-of-the-art computer vision methods, namely
gPb contour detection and SLIC superpixels that are transferred to remote
sensing in this study. The tool combines the two methods, allows a
semi-automatic final delineation and is implemented as a publicly available
QGIS plugin. The approach does not yet aim to provide a comparable alternative
to manual cadastral mapping procedures. However, the methodological development
of the tool towards this goal is developed in this paper. A study with 13
volunteers investigates the design and implementation of the approach and
gathers initial qualitative as well as quantitate results. The study revealed
points for improvement, which are prioritized based on the study results and
which will be addressed in future work.