pwc-archive/papers-with-abstracts huggingface dataset:
CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps
Authors: Paul Hongsuck Seo, Tobias Weyand, Jack Sim, Bohyung Han
Date: 2018-08-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Photo geolocation estimation

Image geolocalization is the task of identifying the location depicted in a
photo based only on its visual information. This task is inherently challenging
since many photos have only few, possibly ambiguous cues to their geolocation.
Recent work has cast this task as a classification problem by partitioning the
earth into a set of discrete cells that correspond to geographic regions. The
granularity of this partitioning presents a critical trade-off; using fewer but
larger cells results in lower location accuracy while using more but smaller
cells reduces the number of training examples per class and increases model
size, making the model prone to overfitting. To tackle this issue, we propose a
simple but effective algorithm, combinatorial partitioning, which generates a
large number of fine-grained output classes by intersecting multiple
coarse-grained partitionings of the earth. Each classifier votes for the
fine-grained classes that overlap with their respective coarse-grained ones.
This technique allows us to predict locations at a fine scale while maintaining
sufficient training examples per class. Our algorithm achieves the
state-of-the-art performance in location recognition on multiple benchmark
datasets.
Probabilistic Causal Analysis of Social Influence
Authors: Francesco Bonchi, Francesco Gullo, Bud Mishra, Daniele Ramazzotti
Date: 2018-08-06 00:00:00

Mastering the dynamics of social influence requires separating, in a database
of information propagation traces, the genuine causal processes from temporal
correlation, i.e., homophily and other spurious causes. However, most studies
to characterize social influence, and, in general, most data-science analyses
focus on correlations, statistical independence, or conditional independence.
Only recently, there has been a resurgence of interest in "causal data
science", e.g., grounded on causality theories. In this paper we adopt a
principled causal approach to the analysis of social influence from
information-propagation data, rooted in the theory of probabilistic causation.
  Our approach consists of two phases. In the first one, in order to avoid the
pitfalls of misinterpreting causation when the data spans a mixture of several
subtypes ("Simpson's paradox"), we partition the set of propagation traces into
groups, in such a way that each group is as less contradictory as possible in
terms of the hierarchical structure of information propagation. To achieve this
goal, we borrow the notion of "agony" and define the Agony-bounded Partitioning
problem, which we prove being hard, and for which we develop two efficient
algorithms with approximation guarantees. In the second phase, for each group
from the first phase, we apply a constrained MLE approach to ultimately learn a
minimal causal topology. Experiments on synthetic data show that our method is
able to retrieve the genuine causal arcs w.r.t. a ground-truth generative
model. Experiments on real data show that, by focusing only on the extracted
causal structures instead of the whole social graph, the effectiveness of
predicting influence spread is significantly improved.
Attentive Semantic Alignment with Offset-Aware Correlation Kernels
Authors: Paul Hongsuck Seo, Jongmin Lee, Deunsol Jung, Bohyung Han, Minsu Cho
Date: 2018-08-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Semantic correspondence, Translation

Semantic correspondence is the problem of establishing correspondences across
images depicting different instances of the same object or scene class. One of
recent approaches to this problem is to estimate parameters of a global
transformation model that densely aligns one image to the other. Since an
entire correlation map between all feature pairs across images is typically
used to predict such a global transformation, noisy features from different
backgrounds, clutter, and occlusion distract the predictor from correct
estimation of the alignment. This is a challenging issue, in particular, in the
problem of semantic correspondence where a large degree of image variations is
often involved. In this paper, we introduce an attentive semantic alignment
method that focuses on reliable correlations, filtering out distractors. For
effective attention, we also propose an offset-aware correlation kernel that
learns to capture translation-invariant local transformations in computing
correlation values over spatial locations. Experiments demonstrate the
effectiveness of the attentive model and offset-aware kernel, and the proposed
model combining both techniques achieves the state-of-the-art performance.
Structure Learning for Relational Logistic Regression: An Ensemble Approach
Authors: Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan
Date: 2018-08-06 00:00:00
Tasks: regression

We consider the problem of learning Relational Logistic Regression (RLR).
Unlike standard logistic regression, the features of RLRs are first-order
formulae with associated weight vectors instead of scalar weights. We turn the
problem of learning RLR to learning these vector-weighted formulae and develop
a learning algorithm based on the recently successful functional-gradient
boosting methods for probabilistic logic models. We derive the functional
gradients and show how weights can be learned simultaneously in an efficient
manner. Our empirical evaluation on standard and novel data sets demonstrates
the superiority of our approach over other methods for learning RLR.
Non-Learning based Deep Parallel MRI Reconstruction (NLDpMRI)
Authors: Ali Pour Yazdanpanah, Onur Afacan, Simon K. Warfield
Date: 2018-08-06 00:00:00
Tasks: Deep Learning, MRI Reconstruction

Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand
and scan time directly depends on the number of acquired k-space samples.
Recently, the deep learning-based MRI reconstruction techniques were suggested
to accelerate MR image acquisition. The most common issues in any deep
learning-based MRI reconstruction approaches are generalizability and
transferability. For different MRI scanner configurations using these
approaches, the network must be trained from scratch every time with new
training dataset, acquired under new configurations, to be able to provide good
reconstruction performance. Here, we propose a new generalized parallel imaging
method based on deep neural networks called NLDpMRI to reduce any structured
aliasing ambiguities related to the different k-space undersampling patterns
for accelerated data acquisition. Two loss functions including non-regularized
and regularized are proposed for parallel MRI reconstruction using deep network
optimization and we reconstruct MR images by optimizing the proposed loss
functions over the network parameters. Unlike any deep learning-based MRI
reconstruction approaches, our method doesn't include any training step that
the network learns from a large number of training samples and it only needs
the single undersampled multi-coil k-space data for reconstruction. Also, the
proposed method can handle k-space data with different undersampling patterns,
and the different number of coils. Experimental results show that the proposed
method outperforms the current state-of-the-art GRAPPA method and the deep
learning-based variational network method.
Active Learning based on Data Uncertainty and Model Sensitivity
Authors: Nutan Chen, Alexej Klushyn, Alexandros Paraschos, Djalel Benbouzid, Patrick van der Smagt
Date: 2018-08-06 00:00:00
Tasks: Active Learning, Metric Learning, Sensitivity

Robots can rapidly acquire new skills from demonstrations. However, during
generalisation of skills or transitioning across fundamentally different
skills, it is unclear whether the robot has the necessary knowledge to perform
the task. Failing to detect missing information often leads to abrupt movements
or to collisions with the environment. Active learning can quantify the
uncertainty of performing the task and, in general, locate regions of missing
information. We introduce a novel algorithm for active learning and demonstrate
its utility for generating smooth trajectories. Our approach is based on deep
generative models and metric learning in latent spaces. It relies on the
Jacobian of the likelihood to detect non-smooth transitions in the latent
space, i.e., transitions that lead to abrupt changes in the movement of the
robot. When non-smooth transitions are detected, our algorithm asks for an
additional demonstration from that specific region. The newly acquired
knowledge modifies the data manifold and allows for learning a latent
representation for generating smooth movements. We demonstrate the efficacy of
our approach on generalising elementary skills, transitioning across different
skills, and implicitly avoiding collisions with the environment. For our
experiments, we use a simulated pendulum where we observe its motion from
images and a 7-DoF anthropomorphic arm.
Paying Attention to Attention: Highlighting Influential Samples in Sequential Analysis
Authors: Cynthia Freeman, Jonathan Merriman, Abhinav Aggarwal, Ian Beaver, Abdullah Mueen
Date: 2018-08-06 00:00:00
Tasks: Document Classification

In (Yang et al. 2016), a hierarchical attention network (HAN) is created for
document classification. The attention layer can be used to visualize text
influential in classifying the document, thereby explaining the model's
prediction. We successfully applied HAN to a sequential analysis task in the
form of real-time monitoring of turn taking in conversations. However, we
discovered instances where the attention weights were uniform at the stopping
point (indicating all turns were equivalently influential to the classifier),
preventing meaningful visualization for real-time human review or classifier
improvement. We observed that attention weights for turns fluctuated as the
conversations progressed, indicating turns had varying influence based on
conversation state. Leveraging this observation, we develop a method to create
more informative real-time visuals (as confirmed by human reviewers) in cases
of uniform attention weights using the changes in turn importance as a
conversation progresses over time.
Inner Space Preserving Generative Pose Machine
Authors: Shuangjun Liu, Sarah Ostadabbas
Date: 2018-08-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image-to-Image Translation

Image-based generative methods, such as generative adversarial networks
(GANs) have already been able to generate realistic images with much context
control, specially when they are conditioned. However, most successful
frameworks share a common procedure which performs an image-to-image
translation with pose of figures in the image untouched. When the objective is
reposing a figure in an image while preserving the rest of the image, the
state-of-the-art mainly assumes a single rigid body with simple background and
limited pose shift, which can hardly be extended to the images under normal
settings. In this paper, we introduce an image "inner space" preserving model
that assigns an interpretable low-dimensional pose descriptor (LDPD) to an
articulated figure in the image. Figure reposing is then generated by passing
the LDPD and the original image through multi-stage augmented hourglass
networks in a conditional GAN structure, called inner space preserving
generative pose machine (ISP-GPM). We evaluated ISP-GPM on reposing human
figures, which are highly articulated with versatile variations. Test of a
state-of-the-art pose estimator on our reposed dataset gave an accuracy over
80% on PCK0.5 metric. The results also elucidated that our ISP-GPM is able to
preserve the background with high accuracy while reasonably recovering the area
blocked by the figure to be reposed.
Learning to Share and Hide Intentions using Information Regularization
Authors: DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matt Botvinick, David Schwab
Date: 2018-08-06 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning to cooperate with friends and compete with foes is a key component
of multi-agent reinforcement learning. Typically to do so, one requires access
to either a model of or interaction with the other agent(s). Here we show how
to learn effective strategies for cooperation and competition in an asymmetric
information game with no such model or interaction. Our approach is to
encourage an agent to reveal or hide their intentions using an
information-theoretic regularizer. We consider both the mutual information
between goal and action given state, as well as the mutual information between
goal and state. We show how to optimize these regularizers in a way that is
easy to integrate with policy gradient reinforcement learning. Finally, we
demonstrate that cooperative (competitive) policies learned with our approach
lead to more (less) reward for a second agent in two simple asymmetric
information games.
Deep Generative Modeling for Scene Synthesis via Hybrid Representations
Authors: Zaiwei Zhang, Zhenpei Yang, Chongyang Ma, Linjie Luo, Alexander Huth, Etienne Vouga, Qi-Xing Huang
Date: 2018-08-06 00:00:00

We present a deep generative scene modeling technique for indoor
environments. Our goal is to train a generative model using a feed-forward
neural network that maps a prior distribution (e.g., a normal distribution) to
the distribution of primary objects in indoor scenes. We introduce a 3D object
arrangement representation that models the locations and orientations of
objects, based on their size and shape attributes. Moreover, our scene
representation is applicable for 3D objects with different multiplicities
(repetition counts), selected from a database. We show a principled way to
train this model by combining discriminator losses for both a 3D object
arrangement representation and a 2D image-based representation. We demonstrate
the effectiveness of our scene representation and the deep learning method on
benchmark datasets. We also show the applications of this generative model in
scene interpolation and scene completion.
Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching
Authors: Andrea Giovannucci, Victor Minden, Cengiz Pehlevan, Dmitri B. Chklovskii
Date: 2018-08-06 00:00:00
Tasks: Dimensionality Reduction

Big data problems frequently require processing datasets in a streaming
fashion, either because all data are available at once but collectively are
larger than available memory or because the data intrinsically arrive one data
point at a time and must be processed online. Here, we introduce a
computationally efficient version of similarity matching, a framework for
online dimensionality reduction that incrementally estimates the top
K-dimensional principal subspace of streamed data while keeping in memory only
the last sample and the current iterate. To assess the performance of our
approach, we construct and make public a test suite containing both a synthetic
data generator and the infrastructure to test online dimensionality reduction
algorithms on real datasets, as well as performant implementations of our
algorithm and competing algorithms with similar aims. Among the algorithms
considered we find our approach to be competitive, performing among the best on
both synthetic and real data.
Unbiased Implicit Variational Inference
Authors: Michalis K. Titsias, Francisco J. R. Ruiz
Date: 2018-08-06 00:00:00
Tasks: regression, Variational Inference

We develop unbiased implicit variational inference (UIVI), a method that
expands the applicability of variational inference by defining an expressive
variational family. UIVI considers an implicit variational distribution
obtained in a hierarchical manner using a simple reparameterizable distribution
whose variational parameters are defined by arbitrarily flexible deep neural
networks. Unlike previous works, UIVI directly optimizes the evidence lower
bound (ELBO) rather than an approximation to the ELBO. We demonstrate UIVI on
several models, including Bayesian multinomial logistic regression and
variational autoencoders, and show that UIVI achieves both tighter ELBO and
better predictive performance than existing approaches at a similar
computational cost.
Semblance: A Rank-Based Kernel on Probability Spaces for Niche Detection
Authors: Divyansh Agarwal, Nancy R. Zhang
Date: 2018-08-06 00:00:00
Tasks: Clustering, General Classification, Image Compression, Image Reconstruction, Information Retrieval, Retrieval, valid

In data science, determining proximity between observations is critical to many downstream analyses such as clustering, information retrieval and classification. However, when the underlying structure of the data probability space is unclear, the function used to compute similarity between data points is often arbitrarily chosen. Here, we present a novel concept of proximity, Semblance, that uses the empirical distribution across all observations to inform the similarity between each pair. The advantage of Semblance lies in its distribution free formulation and its ability to detect niche features by placing greater emphasis on similarity between observation pairs that fall at the outskirts of the data distribution, as opposed to those that fall towards the center. We prove that Semblance is a valid Mercer kernel, thus allowing its principled use in kernel based learning machines. Semblance can be applied to any data modality, and we demonstrate its consistently improved performance against conventional methods through simulations and three real case studies from very different applications, viz. cell type classification using single cell RNA sequencing, selecting predictors of positive return on real estate investments, and image compression.
Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning
Authors: Jiasha Liu, Xiang Li, Hui Ren, Quanzheng Li
Date: 2018-08-06 00:00:00
Tasks: Ensemble Learning, Management

Cardiovascular disease accounts for 1 in every 4 deaths in United States.
Accurate estimation of structural and functional cardiac parameters is crucial
for both diagnosis and disease management. In this work, we develop an ensemble
learning framework for more accurate and robust left ventricle (LV)
quantification. The framework combines two 1st-level modules: direct estimation
module and a segmentation module. The direct estimation module utilizes
Convolutional Neural Network (CNN) to achieve end-to-end quantification. The
CNN is trained by taking 2D cardiac images as input and cardiac parameters as
output. The segmentation module utilizes a U-Net architecture for obtaining
pixel-wise prediction of the epicardium and endocardium of LV from the
background. The binary U-Net output is then analyzed by a separate CNN for
estimating the cardiac parameters. We then employ linear regression between the
1st-level predictor and ground truth to learn a 2nd-level predictor that
ensembles the results from 1st-level modules for the final estimation.
Preliminary results by testing the proposed framework on the LVQuan18 dataset
show superior performance of the ensemble learning model over the two base
modules.
Outlier detection on network flow analysis
Authors: Quang-Vinh Dang
Date: 2018-08-06 00:00:00
Tasks: Classification, General Classification, Outlier Detection, Traffic Classification

It is important to be able to detect and classify malicious network traffic
flows such as DDoS attacks from benign flows. Normally the task is performed by
using supervised classification algorithms. In this paper we analyze the usage
of outlier detection algorithms for the network traffic classification problem.
Principles for Developing a Knowledge Graph of InterlinkedEvents from News Headlines on Twitter
Authors: Saeedeh Shekarpour, Ankita Saxena, Krishnaprasad Thirunarayan, Valerie L. Shalin, Amit Sheth
Date: 2018-08-06 00:00:00
Tasks: General Classification

The ever-growing datasets published on Linked Open Data mainly contain
encyclopedic information. However, there is a lack of quality structured and
semantically annotated datasets extracted from unstructured real-time sources.
In this paper, we present principles for developing a knowledge graph of
interlinked events using the case study of news headlines published on Twitter
which is a real-time and eventful source of fresh information. We represent the
essential pipeline containing the required tasks ranging from choosing
background data model, event annotation (i.e., event recognition and
classification), entity annotation and eventually interlinking events. The
state-of-the-art is limited to domain-specific scenarios for recognizing and
classifying events, whereas this paper plays the role of a domain-agnostic
road-map for developing a knowledge graph of interlinked events.
Autoencoder Based Sample Selection for Self-Taught Learning
Authors: Siwei Feng, Han Yu, Marco F. Duarte
Date: 2018-08-05 00:00:00
Tasks: Transfer Learning

Self-taught learning is a technique that uses a large number of unlabeled data as source samples to improve the task performance on target samples. Compared with other transfer learning techniques, self-taught learning can be applied to a broader set of scenarios due to the loose restrictions on the source data. However, knowledge transferred from source samples that are not sufficiently related to the target domain may negatively influence the target learner, which is referred to as negative transfer. In this paper, we propose a metric for the relevance between a source sample and the target samples. To be more specific, both source and target samples are reconstructed through a single-layer autoencoder with a linear relationship between source samples and reconstructed target samples being simultaneously enforced. An $\ell_{2,1}$-norm sparsity constraint is imposed on the transformation matrix to identify source samples relevant to the target domain. Source domain samples that are deemed relevant are assigned pseudo-labels reflecting their relevance to target domain samples, and are combined with target samples in order to provide an expanded training set for classifier training. Local data structures are also preserved during source sample selection through spectral graph analysis. Promising results in extensive experiments show the advantages of the proposed approach.
Improving Deep Visual Representation for Person Re-identification by Global and Local Image-language Association
Authors: Dapeng Chen, Hongsheng Li, Xihui Liu, Yantao Shen, Zejian yuan, Xiaogang Wang
Date: 2018-08-05 00:00:00
Proceeding: ECCV 2018 9
Tasks: Person Re-Identification, Text based Person Retrieval

Person re-identification is an important task that requires learning
discriminative visual features for distinguishing different person identities.
Diverse auxiliary information has been utilized to improve the visual feature
learning. In this paper, we propose to exploit natural language description as
additional training supervisions for effective visual features. Compared with
other auxiliary information, language can describe a specific person from more
compact and semantic visual aspects, thus is complementary to the pixel-level
image data. Our method not only learns better global visual feature with the
supervision of the overall description but also enforces semantic consistencies
between local visual and linguistic features, which is achieved by building
global and local image-language associations. The global image-language
association is established according to the identity labels, while the local
association is based upon the implicit correspondences between image regions
and noun phrases. Extensive experiments demonstrate the effectiveness of
employing language as training supervisions with the two association schemes.
Our method achieves state-of-the-art performance without utilizing any
auxiliary information during testing and shows better performance than other
joint embedding methods for the image-language association.