pwc-archive/papers-with-abstracts huggingface dataset:
Towards Training Probabilistic Topic Models on Neuromorphic Multi-chip Systems
Authors: Zihao Xiao, Jianfei Chen, Jun Zhu
Date: 2018-04-10 00:00:00
Tasks: Stochastic Optimization, Topic Models

Probabilistic topic models are popular unsupervised learning methods,
including probabilistic latent semantic indexing (pLSI) and latent Dirichlet
allocation (LDA). By now, their training is implemented on general purpose
computers (GPCs), which are flexible in programming but energy-consuming.
Towards low-energy implementations, this paper investigates their training on
an emerging hardware technology called the neuromorphic multi-chip systems
(NMSs). NMSs are very effective for a family of algorithms called spiking
neural networks (SNNs). We present three SNNs to train topic models. The first
SNN is a batch algorithm combining the conventional collapsed Gibbs sampling
(CGS) algorithm and an inference SNN to train LDA. The other two SNNs are
online algorithms targeting at both energy- and storage-limited environments.
The two online algorithms are equivalent with training LDA by using
maximum-a-posterior estimation and maximizing the semi-collapsed likelihood,
respectively. They use novel, tailored ordinary differential equations for
stochastic optimization. We simulate the new algorithms and show that they are
comparable with the GPC algorithms, while being suitable for NMS
implementation. We also propose an extension to train pLSI and a method to
prune the network to obey the limited fan-in of some NMSs.
Large Field and High Resolution: Detecting Needle in Haystack
Authors: Hadar Gorodissky, Daniel Harari, Shimon Ullman
Date: 2018-04-10 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The growing use of convolutional neural networks (CNN) for a broad range of
visual tasks, including tasks involving fine details, raises the problem of
applying such networks to a large field of view, since the amount of
computations increases significantly with the number of pixels. To deal
effectively with this difficulty, we develop and compare methods of using CNNs
for the task of small target localization in natural images, given a limited
"budget" of samples to form an image. Inspired in part by human vision, we
develop and compare variable sampling schemes, with peak resolution at the
center and decreasing resolution with eccentricity, applied iteratively by
re-centering the image at the previous predicted target location. The results
indicate that variable resolution models significantly outperform constant
resolution models. Surprisingly, variable resolution models and in particular
multi-channel models, outperform the optimal, "budget-free" full-resolution
model, using only 5\% of the samples.
A Fully Progressive Approach to Single-Image Super-Resolution
Authors: Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers
Date: 2018-04-09 00:00:00
Tasks: Generative Adversarial Network, Image Super-Resolution, SSIM, Super-Resolution

Recent deep learning approaches to single image super-resolution have
achieved impressive results in terms of traditional error measures and
perceptual quality. However, in each case it remains challenging to achieve
high quality results for large upsampling factors. To this end, we propose a
method (ProSR) that is progressive both in architecture and training: the
network upsamples an image in intermediate steps, while the learning process is
organized from easy to hard, as is done in curriculum learning. To obtain more
photorealistic results, we design a generative adversarial network (GAN), named
ProGanSR, that follows the same progressive multi-scale design principle. This
not only allows to scale well to high upsampling factors (e.g., 8x) but
constitutes a principled multi-scale approach that increases the reconstruction
quality for all upsampling factors simultaneously. In particular ProSR ranks
2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge
[34]. Compared to the top-ranking team, our model is marginally lower, but runs
5 times faster.
SCDA: School Compatibility Decomposition Algorithm for Solving the Multi-School Bus Routing and Scheduling Problem
Authors: Zhongxiang Wang, Ali Shafahi, Ali Haghani
Date: 2017-11-01 00:00:00
Tasks: Scheduling

Safely serving the school transportation demand with the minimum number of
buses is one of the highest financial goals of school transportation directors.
To achieve that objective, a good and efficient way to solve the routing and
scheduling problem is required. Due to the growth of the computing power, the
spotlight has been shed on solving the combined problem of the school bus
routing and scheduling problem. We show that an integrated multi-school bus
routing and scheduling can be formulated with the help of trip compatibility. A
novel decomposition algorithm is proposed to solve the integrated model. The
merit of this integrated model and the decomposition method is that with the
consideration of the trip compatibility, the interrelationship between the
routing and scheduling sub-problems will not be lost in the process of
decomposition. Results show the proposed decomposed problem could provide the
solutions using the same number of buses as the integrated model in much
shorter time (as little as 0.6%) and that the proposed method can save up to
26% number of buses from existing research.
Two Stream 3D Semantic Scene Completion
Authors: Martin Garbade, Yueh-Tung Chen, Johann Sawatzky, Juergen Gall
Date: 2018-04-10 00:00:00
Tasks: 3D geometry, 3D Semantic Scene Completion, Vocal Bursts Valence Prediction

Inferring the 3D geometry and the semantic meaning of surfaces, which are occluded, is a very challenging task. Recently, a first end-to-end learning approach has been proposed that completes a scene from a single depth image. The approach voxelizes the scene and predicts for each voxel if it is occupied and, if it is occupied, the semantic class label. In this work, we propose a two stream approach that leverages depth information and semantic information, which is inferred from the RGB image, for this task. The approach constructs an incomplete 3D semantic tensor, which uses a compact three-channel encoding for the inferred semantic information, and uses a 3D CNN to infer the complete 3D semantic tensor. In our experimental evaluation, we show that the proposed two stream approach substantially outperforms the state-of-the-art for semantic scene completion.
Scalable Variational Inference for Dynamical Systems
Authors: Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann
Date: 2017-05-19 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Numerical Integration, Variational Inference

Gradient matching is a promising tool for learning parameters and state
dynamics of ordinary differential equations. It is a grid free inference
approach, which, for fully observable systems is at times competitive with
numerical integration. However, for many real-world applications, only sparse
observations are available or even unobserved variables are included in the
model description. In these cases most gradient matching methods are difficult
to apply or simply do not provide satisfactory results. That is why, despite
the high computational cost, numerical integration is still the gold standard
in many applications. Using an existing gradient matching approach, we propose
a scalable variational inference framework which can infer states and
parameters simultaneously, offers computational speedups, improved accuracy and
works well even under model misspecifications in a partially observable system.
Mining Social Media for Newsgathering: A Review
Authors: Arkaitz Zubiaga
Date: 2018-04-10 00:00:00

Social media is becoming an increasingly important data source for learning about breaking news and for following the latest developments of ongoing news. This is in part possible thanks to the existence of mobile devices, which allows anyone with access to the Internet to post updates from anywhere, leading in turn to a growing presence of citizen journalism. Consequently, social media has become a go-to resource for journalists during the process of newsgathering. Use of social media for newsgathering is however challenging, and suitable tools are needed in order to facilitate access to useful information for reporting. In this paper, we provide an overview of research in data mining and natural language processing for mining social media for newsgathering. We discuss five different areas that researchers have worked on to mitigate the challenges inherent to social media newsgathering: news discovery, curation of news, validation and verification of content, newsgathering dashboards, and other tasks. We outline the progress made so far in the field, summarise the current challenges as well as discuss future directions in the use of computational journalism to assist with social media newsgathering. This review is relevant to computer scientists researching news in social media as well as for interdisciplinary researchers interested in the intersection of computer science and journalism.
Viewpoint-aware Video Summarization
Authors: Atsushi Kanehira, Luc van Gool, Yoshitaka Ushiku, Tatsuya Harada
Date: 2018-04-09 00:00:00
Proceeding: CVPR 2018 6
Tasks: Semantic Similarity, Semantic Textual Similarity, Video Summarization

This paper introduces a novel variant of video summarization, namely building
a summary that depends on the particular aspect of a video the viewer focuses
on. We refer to this as $\textit{viewpoint}$. To infer what the desired
$\textit{viewpoint}$ may be, we assume that several other videos are available,
especially groups of videos, e.g., as folders on a person's phone or laptop.
The semantic similarity between videos in a group vs. the dissimilarity between
groups is used to produce $\textit{viewpoint}$-specific summaries. For
considering similarity as well as avoiding redundancy, output summary should be
(A) diverse, (B) representative of videos in the same group, and (C)
discriminative against videos in the different groups. To satisfy these
requirements (A)-(C) simultaneously, we proposed a novel video summarization
method from multiple groups of videos. Inspired by Fisher's discriminant
criteria, it selects summary by optimizing the combination of three terms (a)
inner-summary, (b) inner-group, and (c) between-group variances defined on the
feature representation of summary, which can simply represent (A)-(C).
Moreover, we developed a novel dataset to investigate how well the generated
summary reflects the underlying $\textit{viewpoint}$. Quantitative and
qualitative experiments conducted on the dataset demonstrate the effectiveness
of proposed method.
Hyperparameters and Tuning Strategies for Random Forest
Authors: Philipp Probst, Marvin Wright, Anne-Laure Boulesteix
Date: 2018-04-10 00:00:00

The random forest algorithm (RF) has several hyperparameters that have to be
set by the user, e.g., the number of observations drawn randomly for each tree
and whether they are drawn with or without replacement, the number of variables
drawn randomly for each split, the splitting rule, the minimum number of
samples that a node must contain and the number of trees. In this paper, we
first provide a literature review on the parameters' influence on the
prediction performance and on variable importance measures.
  It is well known that in most cases RF works reasonably well with the default
values of the hyperparameters specified in software packages. Nevertheless,
tuning the hyperparameters can improve the performance of RF. In the second
part of this paper, after a brief overview of tuning strategies we demonstrate
the application of one of the most established tuning strategies, model-based
optimization (MBO). To make it easier to use, we provide the tuneRanger R
package that tunes RF with MBO automatically. In a benchmark study on several
datasets, we compare the prediction performance and runtime of tuneRanger with
other tuning implementations in R and RF with default hyperparameters.
Learning from Informants: Relations between Learning Success Criteria
Authors: Martin Aschenbach, Timo Kötzing, Karen Seidel
Date: 2018-01-31 00:00:00

Learning from positive and negative information, so-called \emph{informants}, being one of the models for human and machine learning introduced by E.~M.~Gold, is investigated. Particularly, naturally arising questions about this learning setting, originating in results on learning from solely positive information, are answered. By a carefully arranged argument learners can be assumed to only change their hypothesis in case it is inconsistent with the data (such a learning behavior is called \emph{conservative}). The deduced main theorem states the relations between the most important delayable learning success criteria, being the ones not ruined by a delayed in time hypothesis output. Additionally, our investigations concerning the non-delayable requirement of consistent learning underpin the claim for \emph{delayability} being the right structural property to gain a deeper understanding concerning the nature of learning success criteria. Moreover, we obtain an anomalous \emph{hierarchy} when allowing for an increasing finite number of \emph{anomalies} of the hypothesized language by the learner compared with the language to be learned. In contrast to the vacillatory hierarchy for learning from solely positive information, we observe a \emph{duality} depending on whether infinitely many \emph{vacillations} between different (almost) correct hypotheses are still considered a successful learning behavior.
Natural Language Statistical Features of LSTM-generated Texts
Authors: Marco Lippi, Marcelo A Montemurro, Mirko Degli Esposti, Giampaolo Cristadoro
Date: 2018-04-10 00:00:00
Tasks: Image Captioning, Text Generation

Long Short-Term Memory (LSTM) networks have recently shown remarkable
performance in several tasks dealing with natural language generation, such as
image captioning or poetry composition. Yet, only few works have analyzed text
generated by LSTMs in order to quantitatively evaluate to which extent such
artificial texts resemble those generated by humans. We compared the
statistical structure of LSTM-generated language to that of written natural
language, and to those produced by Markov models of various orders. In
particular, we characterized the statistical structure of language by assessing
word-frequency statistics, long-range correlations, and entropy measures. Our
main finding is that while both LSTM and Markov-generated texts can exhibit
features similar to real ones in their word-frequency statistics and entropy
measures, LSTM-texts are shown to reproduce long-range correlations at scales
comparable to those found in natural language. Moreover, for LSTM networks a
temperature-like parameter controlling the generation process shows an optimal
value---for which the produced texts are closest to real language---consistent
across all the different statistical features investigated.
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations
Authors: Guillermo Garcia-Hernando, Shanxin Yuan, Seungryul Baek, Tae-Kyun Kim
Date: 2017-04-08 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Hand Pose Estimation, Action Recognition, Egocentric Activity Recognition, Hand Gesture Recognition, Hand Pose Estimation, Object, Pose Estimation, Temporal Action Localization

In this work we study the use of 3D hand poses to recognize first-person
dynamic hand actions interacting with 3D objects. Towards this goal, we
collected RGB-D video sequences comprised of more than 100K frames of 45 daily
hand action categories, involving 26 different objects in several hand
configurations. To obtain hand pose annotations, we used our own mo-cap system
that automatically infers the 3D location of each of the 21 joints of a hand
model via 6 magnetic sensors and inverse kinematics. Additionally, we recorded
the 6D object poses and provide 3D object models for a subset of hand-object
interaction sequences. To the best of our knowledge, this is the first
benchmark that enables the study of first-person hand actions with the use of
3D hand poses. We present an extensive experimental evaluation of RGB-D and
pose-based action recognition by 18 baselines/state-of-the-art approaches. The
impact of using appearance features, poses, and their combinations are
measured, and the different training/testing protocols are evaluated. Finally,
we assess how ready the 3D hand pose estimation field is when hands are
severely occluded by objects in egocentric views and its influence on action
recognition. From the results, we see clear benefits of using hand pose as a
cue for action recognition compared to other data modalities. Our dataset and
experiments can be of interest to communities of 3D hand pose estimation, 6D
object pose, and robotics as well as action recognition.
Exploring Disentangled Feature Representation Beyond Face Identification
Authors: Yu Liu, Fangyin Wei, Jing Shao, Lu Sheng, Junjie Yan, Xiaogang Wang
Date: 2018-04-10 00:00:00
Proceeding: CVPR 2018 6
Tasks: Attribute, Face Generation, Face Identification

This paper proposes learning disentangled but complementary face features
with minimal supervision by face identification. Specifically, we construct an
identity Distilling and Dispelling Autoencoder (D2AE) framework that
adversarially learns the identity-distilled features for identity verification
and the identity-dispelled features to fool the verification system. Thanks to
the design of two-stream cues, the learned disentangled features represent not
only the identity or attribute but the complete input image. Comprehensive
evaluations further demonstrate that the proposed features not only maintain
state-of-the-art identity verification performance on LFW, but also acquire
competitive discriminative power for face attribute recognition on CelebA and
LFWA. Moreover, the proposed system is ready to semantically control the face
generation/editing based on various identities and attributes in an
unsupervised manner.
The shortest way to visit all metro lines in a city
Authors: Florian Sikora
Date: 2017-09-13 00:00:00
Tasks: All

What if $\{$a tourist, a train addict, Dr. Sheldon Cooper, somebody who likes
to waste time$\}$ wants to visit all metro lines or carriages in a given
network in a minimum number of steps? We study this problem with an application
to the metro network of Paris and Tokyo, proposing optimal solutions thanks to
mathematical programming tools. Quite surprisingly, it appears that you can
visit all 16 Parisian metro lines in only 26 steps (we denote by a step the act
of taking the metro from one station to an adjacent one). Perhaps even more
surprisingly, adding the 5 RER lines to these 16 lines does not increase the
size of the best solution. It is also possible to visit the 13 lines of (the
dense network of) Tokyo with only 15 steps.
Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Applications
Authors: Antonin Chambolle, Matthias J. Ehrhardt, Peter Richtárik, Carola-Bibiane Schönlieb
Date: 2017-06-15 00:00:00

We propose a stochastic extension of the primal-dual hybrid gradient
algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems
that are separable in the dual variable. The analysis is carried out for
general convex-concave saddle point problems and problems that are either
partially smooth / strongly convex or fully smooth / strongly convex. We
perform the analysis for arbitrary samplings of dual variables, and obtain
known deterministic results as a special case. Several variants of our
stochastic method significantly outperform the deterministic variant on a
variety of imaging tasks.
Camera Style Adaptation for Person Re-identification
Authors: Zhun Zhong, Liang Zheng, Zhedong Zheng, Shaozi Li, Yi Yang
Date: 2017-11-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Data Augmentation, Diversity, Person Re-Identification, Retrieval

Being a cross-camera retrieval task, person re-identification suffers from
image style variations caused by different cameras. The art implicitly
addresses this problem by learning a camera-invariant descriptor subspace. In
this paper, we explicitly consider this challenge by introducing camera style
(CamStyle) adaptation. CamStyle can serve as a data augmentation approach that
smooths the camera style disparities. Specifically, with CycleGAN, labeled
training images can be style-transferred to each camera, and, along with the
original training samples, form the augmented training set. This method, while
increasing data diversity against over-fitting, also incurs a considerable
level of noise. In the effort to alleviate the impact of noise, the label
smooth regularization (LSR) is adopted. The vanilla version of our method
(without LSR) performs reasonably well on few-camera systems in which
over-fitting often occurs. With LSR, we demonstrate consistent improvement in
all systems regardless of the extent of over-fitting. We also report
competitive accuracy compared with the state of the art.
Universal features of mountain ridge networks on Earth
Authors: Rafał Rak, Jarosław Kwapień, Paweł Oświęcimka, Paweł Zięba, Stanisław Drożdż
Date: 2018-04-10 00:00:00
Tasks: Geophysics

Compared to the heavily studied surface drainage systems, the mountain ridge systems have been a subject of less attention even on the empirical level, despite the fact that their structure is richer. To reduce this deficiency, we analyze different mountain ranges by means of a network approach and grasp some essential features of the ridge branching structure. We also employ a fractal analysis as it is especially suitable for describing properties of rough objects and surfaces. As our approach differs from typical analyses that are carried out in geophysics, we believe that it can initialize a research direction that will allow to shed more light on the processes that are responsible for landscape formation and will contribute to the network theory by indicating a need for the construction of new models of the network growth as no existing model can properly describe the ridge formation. We also believe that certain features of our study can offer help in the cartographic generalization. Specifically, we study structure of the ridge networks based on the empirical elevation data collected by SRTM. We consider mountain ranges from different geological periods and geographical locations. For each mountain range, we construct a simple topographic network representation (the ridge junctions are nodes) and a ridge representation (the ridges are nodes and the junctions are edges) and calculate the parameters characterizing their topology. We observe that the topographic networks inherit the fractal structure of the mountain ranges but do not show any other complex features. In contrast, the ridge networks, while lacking the proper fractality, reveal the power-law degree distributions with the exponent $1.6\le \beta \le 1.7$. By taking into account the fact that the analyzed mountains differ in many properties, these values seem to be universal for the earthly mountainous terrain.