pwc-archive/papers-with-abstracts huggingface dataset:
Flow-Guided Feature Aggregation for Video Object Detection
Authors: Xizhou Zhu, Yujie Wang, Jifeng Dai, Lu Yuan, Yichen Wei
Date: 2017-03-29 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, object-detection, Object Detection, Video Object Detection, Video Recognition

Extending state-of-the-art object detectors from image to video is
challenging. The accuracy of detection suffers from degenerated object
appearances in videos, e.g., motion blur, video defocus, rare poses, etc.
Existing work attempts to exploit temporal information on box level, but such
methods are not trained end-to-end. We present flow-guided feature aggregation,
an accurate and end-to-end learning framework for video object detection. It
leverages temporal coherence on feature level instead. It improves the
per-frame features by aggregation of nearby features along the motion paths,
and thus improves the video recognition accuracy. Our method significantly
improves upon strong single-frame baselines in ImageNet VID, especially for
more challenging fast moving objects. Our framework is principled, and on par
with the best engineered systems winning the ImageNet VID challenges 2016,
without additional bells-and-whistles. The proposed method, together with Deep
Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The
code is available at
https://github.com/msracver/Flow-Guided-Feature-Aggregation.
Using GPI-2 for Distributed Memory Paralleliziation of the Caffe Toolbox to Speed up Deep Neural Network Training
Authors: Martin Kuehn, Janis Keuper, Franz-Josef Pfreundt
Date: 2017-05-31 00:00:00
Tasks: Blocking, valid

Deep Neural Network (DNN) are currently of great inter- est in research and
application. The training of these net- works is a compute intensive and time
consuming task. To reduce training times to a bearable amount at reasonable
cost we extend the popular Caffe toolbox for DNN with an efficient distributed
memory communication pattern. To achieve good scalability we emphasize the
overlap of computation and communication and prefer fine granu- lar
synchronization patterns over global barriers. To im- plement these
communication patterns we rely on the the Global address space Programming
Interface version 2 (GPI-2) communication library. This interface provides a
light-weight set of asynchronous one-sided communica- tion primitives
supplemented by non-blocking fine gran- ular data synchronization mechanisms.
Therefore, Caf- feGPI is the name of our parallel version of Caffe. First
benchmarks demonstrate better scaling behavior com- pared with other
extensions, e.g., the Intel TM Caffe. Even within a single symmetric
multiprocessing machine with four graphics processing units, the CaffeGPI
scales bet- ter than the standard Caffe toolbox. These first results
demonstrate that the use of standard High Performance Computing (HPC) hardware
is a valid cost saving ap- proach to train large DDNs. I/O is an other
bottleneck to work with DDNs in a standard parallel HPC setting, which we will
consider in more detail in a forthcoming paper.
Two provably consistent divide and conquer clustering algorithms for large networks
Authors: Soumendu Sundar Mukherjee, Purnamrita Sarkar, Peter J. Bickel
Date: 2017-08-18 00:00:00
Tasks: Clustering, Community Detection, Vocal Bursts Valence Prediction

In this article, we advance divide-and-conquer strategies for solving the
community detection problem in networks. We propose two algorithms which
perform clustering on a number of small subgraphs and finally patches the
results into a single clustering. The main advantage of these algorithms is
that they bring down significantly the computational cost of traditional
algorithms, including spectral clustering, semi-definite programs, modularity
based methods, likelihood based methods etc., without losing on accuracy and
even improving accuracy at times. These algorithms are also, by nature,
parallelizable. Thus, exploiting the facts that most traditional algorithms are
accurate and the corresponding optimization problems are much simpler in small
problems, our divide-and-conquer methods provide an omnibus recipe for scaling
traditional algorithms up to large networks. We prove consistency of these
algorithms under various subgraph selection procedures and perform extensive
simulations and real-data analysis to understand the advantages of the
divide-and-conquer approach in various settings.
Community detection in networks via nonlinear modularity eigenvectors
Authors: Francesco Tudisco, Pedro Mercado, Matthias Hein
Date: 2017-08-18 00:00:00
Tasks: Community Detection

Revealing a community structure in a network or dataset is a central problem
arising in many scientific areas. The modularity function $Q$ is an established
measure quantifying the quality of a community, being identified as a set of
nodes having high modularity. In our terminology, a set of nodes with positive
modularity is called a \textit{module} and a set that maximizes $Q$ is thus
called \textit{leading module}. Finding a leading module in a network is an
important task, however the dimension of real-world problems makes the
maximization of $Q$ unfeasible. This poses the need of approximation techniques
which are typically based on a linear relaxation of $Q$, induced by the
spectrum of the modularity matrix $M$. In this work we propose a nonlinear
relaxation which is instead based on the spectrum of a nonlinear modularity
operator $\mathcal M$. We show that extremal eigenvalues of $\mathcal M$
provide an exact relaxation of the modularity measure $Q$, however at the price
of being more challenging to be computed than those of $M$. Thus we extend the
work made on nonlinear Laplacians, by proposing a computational scheme, named
\textit{generalized RatioDCA}, to address such extremal eigenvalues. We show
monotonic ascent and convergence of the method. We finally apply the new method
to several synthetic and real-world data sets, showing both effectiveness of
the model and performance of the method.
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
Authors: Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, Yejin Choi, Luke Zettlemoyer
Date: 2017-04-26 00:00:00
Proceeding: ACL 2017 7
Tasks: AMR Parsing, Graph-to-Sequence

Sequence-to-sequence models have shown strong performance across a broad
range of applications. However, their application to parsing and generating
text usingAbstract Meaning Representation (AMR)has been limited, due to the
relatively limited amount of labeled data and the non-sequential nature of the
AMR graphs. We present a novel training procedure that can lift this limitation
using millions of unlabeled sentences and careful preprocessing of the AMR
graphs. For AMR parsing, our model achieves competitive results of 62.1SMATCH,
the current best score reported without significant use of external semantic
resources. For AMR generation, our model establishes a new state-of-the-art
performance of BLEU 33.8. We present extensive ablative and qualitative
analysis including strong evidence that sequence-based AMR models are robust
against ordering variations of graph-to-sequence conversions.
Induction of Decision Trees based on Generalized Graph Queries
Authors: Pedro Almagro-Blanco, Fernando Sancho-Caparrini
Date: 2017-08-18 00:00:00

Usually, decision tree induction algorithms are limited to work with non
relational data. Given a record, they do not take into account other objects
attributes even though they can provide valuable information for the learning
task. In this paper we present GGQ-ID3, a multi-relational decision tree
learning algorithm that uses Generalized Graph Queries (GGQ) as predicates in
the decision nodes. GGQs allow to express complex patterns (including cycles)
and they can be refined step-by-step. Also, they can evaluate structures (not
only single records) and perform Regular Pattern Matching. GGQ are built
dynamically (pattern mining) during the GGQ-ID3 tree construction process. We
will show how to use GGQ-ID3 to perform multi-relational machine learning
keeping complexity under control. Finally, some real examples of automatically
obtained classification trees and semantic patterns are shown.
  -----
  Normalmente, los algoritmos de inducci\'on de \'arboles de decisi\'on
trabajan con datos no relacionales. Dado un registro, no tienen en cuenta los
atributos de otros objetos a pesar de que \'estos pueden proporcionar
informaci\'on \'util para la tarea de aprendizaje. En este art\'iculo
presentamos GGQ-ID3, un algoritmo de aprendizaje de \'arboles de decisiones
multi-relacional que utiliza Generalized Graph Queries (GGQ) como predicados en
los nodos de decisi\'on. Los GGQs permiten expresar patrones complejos
(incluyendo ciclos) y pueden ser refinados paso a paso. Adem\'as, pueden
evaluar estructuras (no solo registros) y llevar a cabo Regular Pattern
Matching. En GGQ-ID3, los GGQ son construidos din\'amicamente (pattern mining)
durante el proceso de construcci\'on del \'arbol. Adem\'as, se muestran algunos
ejemplos reales de \'arboles de clasificaci\'on multi-relacionales y patrones
sem\'anticos obtenidos autom\'aticamente.
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation
Authors: Yuhuai Wu, Elman Mansimov, Shun Liao, Roger Grosse, Jimmy Ba
Date: 2017-08-17 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Atari Games, continuous-control, Continuous Control, Deep Reinforcement Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this work, we propose to apply trust region optimization to deep
reinforcement learning using a recently proposed Kronecker-factored
approximation to the curvature. We extend the framework of natural policy
gradient and propose to optimize both the actor and the critic using
Kronecker-factored approximate curvature (K-FAC) with trust region; hence we
call our method Actor Critic using Kronecker-Factored Trust Region (ACKTR). To
the best of our knowledge, this is the first scalable trust region natural
gradient method for actor-critic methods. It is also a method that learns
non-trivial tasks in continuous control as well as discrete control policies
directly from raw pixel inputs. We tested our approach across discrete domains
in Atari games as well as continuous domains in the MuJoCo environment. With
the proposed methods, we are able to achieve higher rewards and a 2- to 3-fold
improvement in sample efficiency on average, compared to previous
state-of-the-art on-policy actor-critic methods. Code is available at
https://github.com/openai/baselines
Guiding Network Analysis using Graph Slepians: An Illustration for the C. Elegans Connectome
Authors: Dimitri Van De Ville, Robin Demesmaeker, Maria Giulia Preti
Date: 2017-08-15 00:00:00

Spectral approaches of network analysis heavily rely upon the
eigendecomposition of the graph Laplacian. For instance, in graph signal
processing, the Laplacian eigendecomposition is used to define the graph
Fourier transform and then transpose signal processing operations to graphs by
implementing them in the spectral domain. Here, we build on recent work that
generalized Slepian functions to the graph setting. In particular, graph
Slepians are band-limited graph signals with maximal energy concentration in a
given subgraph. We show how this approach can be used to guide network
analysis; i.e., we propose a visualization that reveals network organization of
a subgraph, but while striking a balance with global network structure. These
developments are illustrated for the structural connectome of the C. Elegans.
Spotting Separator Points at Line Terminals in Compressed Document Images for Text-line Segmentation
Authors: Amarnath R, P. Nagabhushan
Date: 2017-08-18 00:00:00

Line separators are used to segregate text-lines from one another in document
image analysis. Finding the separator points at every line terminal in a
document image would enable text-line segmentation. In particular, identifying
the separators in handwritten text could be a thrilling exercise. Obviously it
would be challenging to perform this in the compressed version of a document
image and that is the proposed objective in this research. Such an effort would
prevent the computational burden of decompressing a document for text-line
segmentation. Since document images are generally compressed using run length
encoding (RLE) technique as per the CCITT standards, the first column in the
RLE will be a white column. The value (depth) in the white column is very low
when a particular line is a text line and the depth could be larger at the
point of text line separation. A longer consecutive sequence of such larger
depth should indicate the gap between the text lines, which provides the
separator region. In case of over separation and under separation issues,
corrective actions such as deletion and insertion are suggested respectively.
An extensive experimentation is conducted on the compressed images of the
benchmark datasets of ICDAR13 and Alireza et al [17] to demonstrate the
efficacy.
Mesh-based 3D Textured Urban Mapping
Authors: Andrea Romanoni, Daniele Fiorenti, Matteo Matteucci
Date: 2017-08-18 00:00:00
Tasks: Autonomous Driving, Surface Reconstruction

In the era of autonomous driving, urban mapping represents a core step to let
vehicles interact with the urban context. Successful mapping algorithms have
been proposed in the last decade building the map leveraging on data from a
single sensor. The focus of the system presented in this paper is twofold: the
joint estimation of a 3D map from lidar data and images, based on a 3D mesh,
and its texturing. Indeed, even if most surveying vehicles for mapping are
endowed by cameras and lidar, existing mapping algorithms usually rely on
either images or lidar data; moreover both image-based and lidar-based systems
often represent the map as a point cloud, while a continuous textured mesh
representation would be useful for visualization and navigation purposes. In
the proposed framework, we join the accuracy of the 3D lidar data, and the
dense information and appearance carried by the images, in estimating a
visibility consistent map upon the lidar measurements, and refining it
photometrically through the acquired images. We evaluate the proposed framework
against the KITTI dataset and we show the performance improvement with respect
to two state of the art urban mapping algorithms, and two widely used surface
reconstruction algorithms in Computer Graphics.
Unsupervised Feature Learning for Writer Identification and Writer Retrieval
Authors: Vincent Christlein, Martin Gropp, Stefan Fiel, Andreas Maier
Date: 2017-05-25 00:00:00
Tasks: Classification, Clustering, General Classification, Retrieval, Writer Retrieval

Deep Convolutional Neural Networks (CNN) have shown great success in
supervised classification tasks such as character classification or dating.
Deep learning methods typically need a lot of annotated training data, which is
not available in many scenarios. In these cases, traditional methods are often
better than or equivalent to deep learning methods. In this paper, we propose a
simple, yet effective, way to learn CNN activation features in an unsupervised
manner. Therefore, we train a deep residual network using surrogate classes.
The surrogate classes are created by clustering the training dataset, where
each cluster index represents one surrogate class. The activations from the
penultimate CNN layer serve as features for subsequent classification tasks. We
evaluate the feature representations on two publicly available datasets. The
focus lies on the ICDAR17 competition dataset on historical document writer
identification (Historical-WI). We show that the activation features trained
without supervision are superior to descriptors of state-of-the-art writer
identification methods. Additionally, we achieve comparable results in the case
of handwriting classification using the ICFHR16 competition dataset on
historical Latin script types (CLaMM16).
Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution
Authors: E. Manjavacas, J. de Gussem, W. Daelemans, M. Kestemont
Date: 2017-08-18 00:00:00
Tasks: Attribute, Authorship Attribution, Text Generation

Recent applications of neural language models have led to an increased
interest in the automatic generation of natural language. However impressive,
the evaluation of neurally generated text has so far remained rather informal
and anecdotal. Here, we present an attempt at the systematic assessment of one
aspect of the quality of neurally generated text. We focus on a specific aspect
of neural language generation: its ability to reproduce authorial writing
styles. Using established models for authorship attribution, we empirically
assess the stylistic qualities of neurally generated text. In comparison to
conventional language models, neural models generate fuzzier text that is
relatively harder to attribute correctly. Nevertheless, our results also
suggest that neurally generated text offers more valuable perspectives for the
augmentation of training data.
Pillar Networks++: Distributed non-parametric deep and wide networks
Authors: Biswa Sengupta, Yu Qian
Date: 2017-08-18 00:00:00
Tasks: Action Recognition, Gaussian Processes, Temporal Action Localization

In recent work, it was shown that combining multi-kernel based support vector
machines (SVMs) can lead to near state-of-the-art performance on an action
recognition dataset (HMDB-51 dataset). This was 0.4\% lower than frameworks
that used hand-crafted features in addition to the deep convolutional feature
extractors. In the present work, we show that combining distributed Gaussian
Processes with multi-stream deep convolutional neural networks (CNN) alleviate
the need to augment a neural network with hand-crafted features. In contrast to
prior work, we treat each deep neural convolutional network as an expert
wherein the individual predictions (and their respective uncertainties) are
combined into a Product of Experts (PoE) framework.
Dimensionality Reduction using Similarity-induced Embeddings
Authors: Nikolaos Passalis, Anastasios Tefas
Date: 2017-06-18 00:00:00
Tasks: Dimensionality Reduction, Supervised dimensionality reduction

The vast majority of Dimensionality Reduction (DR) techniques rely on
second-order statistics to define their optimization objective. Even though
this provides adequate results in most cases, it comes with several
shortcomings. The methods require carefully designed regularizers and they are
usually prone to outliers. In this work, a new DR framework, that can directly
model the target distribution using the notion of similarity instead of
distance, is introduced. The proposed framework, called Similarity Embedding
Framework, can overcome the aforementioned limitations and provides a
conceptually simpler way to express optimization targets similar to existing DR
techniques. Deriving a new DR technique using the Similarity Embedding
Framework becomes simply a matter of choosing an appropriate target similarity
matrix. A variety of classical tasks, such as performing supervised
dimensionality reduction and providing out-of-of-sample extensions, as well as,
new novel techniques, such as providing fast linear embeddings for complex
techniques, are demonstrated in this paper using the proposed framework. Six
datasets from a diverse range of domains are used to evaluate the proposed
method and it is demonstrated that it can outperform many existing DR
techniques.
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning
Authors: Yao-Yuan Yang, Kuan-Hao Huang, Chih-Wei Chang, Hsuan-Tien Lin
Date: 2016-11-29 00:00:00
Tasks: Active Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning

Label space expansion for multi-label classification (MLC) is a methodology
that encodes the original label vectors to higher dimensional codes before
training and decodes the predicted codes back to the label vectors during
testing. The methodology has been demonstrated to improve the performance of
MLC algorithms when coupled with off-the-shelf error-correcting codes for
encoding and decoding. Nevertheless, such a coding scheme can be complicated to
implement, and cannot easily satisfy a common application need of
cost-sensitive MLC---adapting to different evaluation criteria of interest. In
this work, we show that a simpler coding scheme based on the concept of a
reference pair of label vectors achieves cost-sensitivity more naturally. In
particular, our proposed cost-sensitive reference pair encoding (CSRPE)
algorithm contains cluster-based encoding, weight-based training and
voting-based decoding steps, all utilizing the cost information. Furthermore,
we leverage the cost information embedded in the code space of CSRPE to propose
a novel active learning algorithm for cost-sensitive MLC. Extensive
experimental results verify that CSRPE performs better than state-of-the-art
algorithms across different MLC criteria. The results also demonstrate that the
CSRPE-backed active learning algorithm is superior to existing algorithms for
active MLC, and further justify the usefulness of CSRPE.
Exploring Directional Path-Consistency for Solving Constraint Networks
Authors: Shufeng Kong, Sanjiang Li, Michael Sioutis
Date: 2017-08-18 00:00:00
Tasks: Scene Labeling

Among the local consistency techniques used for solving constraint networks,
path-consistency (PC) has received a great deal of attention. However,
enforcing PC is computationally expensive and sometimes even unnecessary.
Directional path-consistency (DPC) is a weaker notion of PC that considers a
given variable ordering and can thus be enforced more efficiently than PC. This
paper shows that DPC (the DPC enforcing algorithm of Dechter and Pearl) decides
the constraint satisfaction problem (CSP) of a constraint language if it is
complete and has the variable elimination property (VEP). However, we also show
that no complete VEP constraint language can have a domain with more than 2
values. We then present a simple variant of the DPC algorithm, called DPC*, and
show that the CSP of a constraint language can be decided by DPC* if it is
closed under a majority operation. In fact, DPC* is sufficient for guaranteeing
backtrack-free search for such constraint networks. Examples of majority-closed
constraint classes include the classes of connected row-convex (CRC)
constraints and tree-preserving constraints, which have found applications in
various domains, such as scene labeling, temporal reasoning, geometric
reasoning, and logical filtering. Our experimental evaluations show that DPC*
significantly outperforms the state-of-the-art algorithms for solving
majority-closed constraints.
EmoAtt at EmoInt-2017: Inner attention sentence embedding for Emotion Intensity
Authors: Edison Marrese-Taylor, Yutaka Matsuo
Date: 2017-08-18 00:00:00
Proceeding: WS 2017 9
Tasks: Representation Learning, Sentence, Sentence Embedding, Sentence-Embedding

In this paper we describe a deep learning system that has been designed and
built for the WASSA 2017 Emotion Intensity Shared Task. We introduce a
representation learning approach based on inner attention on top of an RNN.
Results show that our model offers good capabilities and is able to
successfully identify emotion-bearing words to predict intensity without
leveraging on lexicons, obtaining the 13th place among 22 shared task
competitors.