pwc-archive/papers-with-abstracts huggingface dataset:
Can Deep Neural Networks Match the Related Objects?: A Survey on ImageNet-trained Classification Models
Authors: Han S. Lee, Heechul Jung, Alex A. Agarwal, Junmo Kim
Date: 2017-09-12 00:00:00
Tasks: General Classification, image-classification, Image Classification, Object

Deep neural networks (DNNs) have shown the state-of-the-art level of
performances in wide range of complicated tasks. In recent years, the studies
have been actively conducted to analyze the black box characteristics of DNNs
and to grasp the learning behaviours, tendency, and limitations of DNNs. In
this paper, we investigate the limitation of DNNs in image classification task
and verify it with the method inspired by cognitive psychology. Through
analyzing the failure cases of ImageNet classification task, we hypothesize
that the DNNs do not sufficiently learn to associate related classes of
objects. To verify how DNNs understand the relatedness between object classes,
we conducted experiments on the image database provided in cognitive
psychology. We applied the ImageNet-trained DNNs to the database consisting of
pairs of related and unrelated object images to compare the feature
similarities and determine whether the pairs match each other. In the
experiments, we observed that the DNNs show limited performance in determining
relatedness between object classes. In addition, the DNNs present somewhat
improved performance in discovering relatedness based on similarity, but they
perform weaker in discovering relatedness based on association. Through these
experiments, a novel analysis of learning behaviour of DNNs is provided and the
limitation which needs to be overcome is suggested.
Domain Control for Neural Machine Translation
Authors: Catherine Kobus, Josep Crego, Jean Senellart
Date: 2016-12-19 00:00:00
Proceeding: RANLP 2017 9
Tasks: Domain Adaptation, Machine Translation, NMT, Sentence, Translation

Machine translation systems are very sensitive to the domains they were
trained on. Several domain adaptation techniques have been deeply studied. We
propose a new technique for neural machine translation (NMT) that we call
domain control which is performed at runtime using a unique neural network
covering multiple domains. The presented approach shows quality improvements
when compared to dedicated domains translating on any of the covered domains
and even on out-of-domain data. In addition, model parameters do not need to be
re-estimated for each domain, making this effective to real use cases.
Evaluation is carried out on English-to-French translation for two different
testing scenarios. We first consider the case where an end-user performs
translations on a known domain. Secondly, we consider the scenario where the
domain is not known and predicted at the sentence level before translating.
Results show consistent accuracy improvements for both conditions.
Opportunistic Self Organizing Migrating Algorithm for Real-Time Dynamic Traveling Salesman Problem
Authors: Shubham Dokania, Sunyam Bagga, Rohit Sharma
Date: 2017-09-12 00:00:00
Tasks: Traveling Salesman Problem

Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm
based on the self-organizing behavior of individuals in a simulated social
environment. SOMA performs iterative computations on a population of potential
solutions in the given search space to obtain an optimal solution. In this
paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been
proposed that introduces a novel strategy to generate perturbations
effectively. This strategy allows the individual to span across more possible
solutions and thus, is able to produce better solutions. A comprehensive
analysis of OSOMA on multi-dimensional unconstrained benchmark test functions
is performed. OSOMA is then applied to solve real-time Dynamic Traveling
Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and
simulated using real-time data from Google Maps with a varying cost-metric
between any two cities. Although DTSP is a very common and intuitive model in
the real world, its presence in literature is still very limited. OSOMA
performs exceptionally well on the problems mentioned above. To substantiate
this claim, the performance of OSOMA is compared with SOMA, Differential
Evolution and Particle Swarm Optimization.
Hierarchy Influenced Differential Evolution: A Motor Operation Inspired Approach
Authors: Shubham Dokania, Ayush Chopra, Feroz Ahmad, Anil Singh Parihar
Date: 2017-02-17 00:00:00
Tasks: Decision Making

Operational maturity of biological control systems have fuelled the
inspiration for a large number of mathematical and logical models for control,
automation and optimisation. The human brain represents the most sophisticated
control architecture known to us and is a central motivation for several
research attempts across various domains. In the present work, we introduce an
algorithm for mathematical optimisation that derives its intuition from the
hierarchical and distributed operations of the human motor system. The system
comprises global leaders, local leaders and an effector population that adapt
dynamically to attain global optimisation via a feedback mechanism coupled with
the structural hierarchy. The hierarchical system operation is distributed into
local control for movement and global controllers that facilitate gross motion
and decision making. We present our algorithm as a variant of the classical
Differential Evolution algorithm, introducing a hierarchical crossover
operation. The discussed approach is tested exhaustively on standard test
functions as well as the CEC 2017 benchmark. Our algorithm significantly
outperforms various standard algorithms as well as their popular variants as
discussed in the results.
Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
Authors: Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Luping Shi
Date: 2017-06-08 00:00:00
Tasks: object-detection, Object Detection, Vocal Bursts Intensity Prediction

Compared with artificial neural networks (ANNs), spiking neural networks
(SNNs) are promising to explore the brain-like behaviors since the spikes could
encode more spatio-temporal information. Although pre-training from ANN or
direct training based on backpropagation (BP) makes the supervised training of
SNNs possible, these methods only exploit the networks' spatial domain
information which leads to the performance bottleneck and requires many
complicated training skills. Another fundamental issue is that the spike
activity is naturally non-differentiable which causes great difficulties in
training SNNs. To this end, we build an iterative LIF model that is more
friendly for gradient descent training. By simultaneously considering the
layer-by-layer spatial domain (SD) and the timing-dependent temporal domain
(TD) in the training phase, as well as an approximated derivative for the spike
activity, we propose a spatio-temporal backpropagation (STBP) training
framework without using any complicated technology. We achieve the best
performance of multi-layered perceptron (MLP) compared with existing
state-of-the-art algorithms over the static MNIST and the dynamic N-MNIST
dataset as well as a custom object detection dataset. This work provides a new
perspective to explore the high-performance SNNs for future brain-like
computing paradigm with rich spatio-temporal dynamics.
Urban morphology meets deep learning: Exploring urban forms in one million cities, town and villages across the planet
Authors: Vahid Moosavi
Date: 2017-09-09 00:00:00

Study of urban form is an important area of research in urban planning/design
that contributes to our understanding of how cities function and evolve.
However, classical approaches are based on very limited observations and
inconsistent methods. As an alternative, availability of massive urban data
collections such as Open Street Map from the one hand and the recent
advancements in machine learning methods such as deep learning techniques on
the other have opened up new possibilities to automatically investigate urban
forms at the global scale. In this work for the first time, by collecting a
large data set of street networks in more than one million cities, towns and
villages all over the world, we trained a deep convolutional auto-encoder, that
automatically learns the hierarchical structures of urban forms and represents
them via dense and comparable vectors. We showed how the learned urban vectors
could be used for different investigations. Using the learned urban vectors,
one is able to easily find and compare similar urban forms all over the world,
considering their overall spatial structure and other factors such as
orientation, graphical structure, and density and partial deformations. Further
cluster analysis reveals the distribution of the main patterns of urban forms
all over the planet.
Learning with Bounded Instance- and Label-dependent Label Noise
Authors: Jiacheng Cheng, Tongliang Liu, Kotagiri Ramamohanarao, DaCheng Tao
Date: 2017-09-12 00:00:00
Proceeding: ICML 2020 1

Instance- and Label-dependent label Noise (ILN) widely exists in real-world datasets but has been rarely studied. In this paper, we focus on Bounded Instance- and Label-dependent label Noise (BILN), a particular case of ILN where the label noise rates -- the probabilities that the true labels of examples flip into the corrupted ones -- have upper bound less than $1$. Specifically, we introduce the concept of distilled examples, i.e. examples whose labels are identical with the labels assigned for them by the Bayes optimal classifier, and prove that under certain conditions classifiers learnt on distilled examples will converge to the Bayes optimal classifier. Inspired by the idea of learning with distilled examples, we then propose a learning algorithm with theoretical guarantees for its robustness to BILN. At last, empirical evaluations on both synthetic and real-world datasets show effectiveness of our algorithm in learning with BILN.
Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction
Authors: Robert Maier, Raphael Schaller, Daniel Cremers
Date: 2017-09-12 00:00:00
Tasks: 3D Reconstruction, CPU, GPU

State-of-the-art methods for large-scale 3D reconstruction from RGB-D sensors
usually reduce drift in camera tracking by globally optimizing the estimated
camera poses in real-time without simultaneously updating the reconstructed
surface on pose changes. We propose an efficient on-the-fly surface correction
method for globally consistent dense 3D reconstruction of large-scale scenes.
Our approach uses a dense Visual RGB-D SLAM system that estimates the camera
motion in real-time on a CPU and refines it in a global pose graph
optimization. Consecutive RGB-D frames are locally fused into keyframes, which
are incorporated into a sparse voxel hashed Signed Distance Field (SDF) on the
GPU. On pose graph updates, the SDF volume is corrected on-the-fly using a
novel keyframe re-integration strategy with reduced GPU-host streaming. We
demonstrate in an extensive quantitative evaluation that our method is up to
93% more runtime efficient compared to the state-of-the-art and requires
significantly less memory, with only negligible loss of surface quality.
Overall, our system requires only a single GPU and allows for real-time surface
correction of large environments.
Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF
Authors: Yan Shao, Christian Hardmeier, Jörg Tiedemann, Joakim Nivre
Date: 2017-04-05 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: POS, POS Tagging, Segmentation

We present a character-based model for joint segmentation and POS tagging for
Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is
adapted and applied with novel vector representations of Chinese characters
that capture rich contextual information and lower-than-character level
features. The proposed model is extensively evaluated and compared with a
state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The
experimental results indicate that our model is accurate and robust across
datasets in different sizes, genres and annotation schemes. We obtain
state-of-the-art performance on CTB5, achieving 94.38 F1-score for joint
segmentation and POS tagging.
Language Models of Spoken Dutch
Authors: Lyan Verwimp, Joris Pelemans, Marieke Lycke, Hugo Van hamme, Patrick Wambacq
Date: 2017-09-12 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In Flanders, all TV shows are subtitled. However, the process of subtitling
is a very time-consuming one and can be sped up by providing the output of a
speech recognizer run on the audio of the TV show, prior to the subtitling.
Naturally, this speech recognition will perform much better if the employed
language model is adapted to the register and the topic of the program. We
present several language models trained on subtitles of television shows
provided by the Flemish public-service broadcaster VRT. This data was gathered
in the context of the project STON which has as purpose to facilitate the
process of subtitling TV shows. One model is trained on all available data (46M
word tokens), but we also trained models on a specific type of TV show or
domain/topic. Language models of spoken language are quite rare due to the lack
of training data. The size of this corpus is relatively large for a corpus of
spoken language (compare with e.g. CGN which has 9M words), but still rather
small for a language model. Thus, in practice it is advised to interpolate
these models with a large background language model trained on written
language. The models can be freely downloaded on
http://www.esat.kuleuven.be/psi/spraak/downloads/.
Cross-lingual Word Segmentation and Morpheme Segmentation as Sequence Labelling
Authors: Yan Shao
Date: 2017-09-12 00:00:00
Tasks: Segmentation

This paper presents our segmentation system developed for the MLP 2017 shared
tasks on cross-lingual word segmentation and morpheme segmentation. We model
both word and morpheme segmentation as character-level sequence labelling
tasks. The prevalent bidirectional recurrent neural network with conditional
random fields as the output interface is adapted as the baseline system, which
is further improved via ensemble decoding. Our universal system is applied to
and extensively evaluated on all the official data sets without any
language-specific adjustment. The official evaluation results indicate that the
proposed model achieves outstanding accuracies both for word and morpheme
segmentation on all the languages in various types when compared to the other
participating systems.
280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification
Authors: Amit Gupta, Rémi Lebret, Hamza Harkous, Karl Aberer
Date: 2017-04-25 00:00:00
Tasks: General Classification

We propose a simple, yet effective, approach towards inducing multilingual
taxonomies from Wikipedia. Given an English taxonomy, our approach leverages
the interlanguage links of Wikipedia followed by character-level classifiers to
induce high-precision, high-coverage taxonomies in other languages. Through
experiments, we demonstrate that our approach significantly outperforms the
state-of-the-art, heuristics-heavy approaches for six languages. As a
consequence of our work, we release presumably the largest and the most
accurate multilingual taxonomic resource spanning over 280 languages.
Transform Invariant Auto-encoder
Authors: Tadashi Matsuo, Hiroya Fukuhara, Nobutaka Shimada
Date: 2017-09-12 00:00:00
Tasks: Dimensionality Reduction, Position

The auto-encoder method is a type of dimensionality reduction method. A
mapping from a vector to a descriptor that represents essential information can
be automatically generated from a set of vectors without any supervising
information. However, an image and its spatially shifted version are encoded
into different descriptors by an existing ordinary auto-encoder because each
descriptor includes a spatial subpattern and its position. To generate a
descriptor representing a spatial subpattern in an image, we need to normalize
its spatial position in the images prior to training an ordinary auto-encoder;
however, such a normalization is generally difficult for images without obvious
standard positions. We propose a transform invariant auto-encoder and an
inference model of transform parameters. By the proposed method, we can
separate an input into a transform invariant descriptor and transform
parameters. The proposed method can be applied to various auto-encoders without
requiring any special modules or labeled training samples. By applying it to
shift transforms, we can achieve a shift invariant auto-encoder that can
extract a typical spatial subpattern independent of its relative position in a
window. In addition, we can achieve a model that can infer shift parameters
required to restore the input from the typical subpattern. As an example of the
proposed method, we demonstrate that a descriptor generated by a shift
invariant auto-encoder can represent a typical spatial subpattern. In addition,
we demonstrate the imitation of a human hand by a robot hand as an example of a
regression based on spatial subpatterns.
GANs for Biological Image Synthesis
Authors: Anton Osokin, Anatole Chessel, Rafael E. Carazo Salas, Federico Vaggi
Date: 2017-08-15 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Generation

In this paper, we propose a novel application of Generative Adversarial
Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy.
Compared to natural images, cells tend to have a simpler and more geometric
global structure that facilitates image generation. However, the correlation
between the spatial pattern of different fluorescent proteins reflects
important biological functions, and synthesized images have to capture these
relationships to be relevant for biological applications. We adapt GANs to the
task at hand and propose new models with casual dependencies between image
channels that can generate multi-channel images, which would be impossible to
obtain experimentally. We evaluate our approach using two independent
techniques and compare it against sensible baselines. Finally, we demonstrate
that by interpolating across the latent space we can mimic the known changes in
protein localization that occur through time during the cell cycle, allowing us
to predict temporal evolution from static images.
Dependencies: Formalising Semantic Catenae for Information Retrieval
Authors: Christina Lioma
Date: 2017-09-12 00:00:00
Tasks: Information Retrieval, Retrieval

Building machines that can understand text like humans is an AI-complete
problem. A great deal of research has already gone into this, with astounding
results, allowing everyday people to discuss with their telephones, or have
their reading materials analysed and classified by computers. A prerequisite
for processing text semantics, common to the above examples, is having some
computational representation of text as an abstract object. Operations on this
representation practically correspond to making semantic inferences, and by
extension simulating understanding text. The complexity and granularity of
semantic processing that can be realised is constrained by the mathematical and
computational robustness, expressiveness, and rigour of the tools used.
  This dissertation contributes a series of such tools, diverse in their
mathematical formulation, but common in their application to model semantic
inferences when machines process text. These tools are principally expressed in
nine distinct models that capture aspects of semantic dependence in highly
interpretable and non-complex ways. This dissertation further reflects on
present and future problems with the current research paradigm in this area,
and makes recommendations on how to overcome them.
  The amalgamation of the body of work presented in this dissertation advances
the complexity and granularity of semantic inferences that can be made
automatically by machines.
Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets
Authors: Junyu Luo, Yong Xu, Chenwei Tang, Jiancheng Lv
Date: 2017-03-29 00:00:00
Tasks: Decoder

The inverse mapping of GANs'(Generative Adversarial Nets) generator has a
great potential value.Hence, some works have been developed to construct the
inverse function of generator by directly learning or adversarial
learning.While the results are encouraging, the problem is highly challenging
and the existing ways of training inverse models of GANs have many
disadvantages, such as hard to train or poor performance.Due to these reasons,
we propose a new approach based on using inverse generator ($IG$) model as
encoder and pre-trained generator ($G$) as decoder of an AutoEncoder network to
train the $IG$ model. In the proposed model, the difference between the input
and output, which are both the generated image of pre-trained GAN's generator,
of AutoEncoder is directly minimized. The optimizing method can overcome the
difficulty in training and inverse model of an non one-to-one function.We also
applied the inverse model of GANs' generators to image searching and
translation.The experimental results prove that the proposed approach works
better than the traditional approaches in image searching.
Construction of Latent Descriptor Space and Inference Model of Hand-Object Interactions
Authors: Tadashi Matsuo, Nobutaka Shimada
Date: 2017-09-12 00:00:00
Tasks: Object, Object Recognition

Appearance-based generic object recognition is a challenging problem because
all possible appearances of objects cannot be registered, especially as new
objects are produced every day. Function of objects, however, has a
comparatively small number of prototypes. Therefore, function-based
classification of new objects could be a valuable tool for generic object
recognition. Object functions are closely related to hand-object interactions
during handling of a functional object; i.e., how the hand approaches the
object, which parts of the object and contact the hand, and the shape of the
hand during interaction. Hand-object interactions are helpful for modeling
object functions. However, it is difficult to assign discrete labels to
interactions because an object shape and grasping hand-postures intrinsically
have continuous variations. To describe these interactions, we propose the
interaction descriptor space which is acquired from unlabeled appearances of
human hand-object interactions. By using interaction descriptors, we can
numerically describe the relation between an object's appearance and its
possible interaction with the hand. The model infers the quantitative state of
the interaction from the object image alone. It also identifies the parts of
objects designed for hand interactions such as grips and handles. We
demonstrate that the proposed method can unsupervisedly generate interaction
descriptors that make clusters corresponding to interaction types. And also we
demonstrate that the model can infer possible hand-object interactions.