pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Population Synthesis with Deep Generative Modeling
Authors: Stanislav S. Borysov, Jeppe Rich, Francisco C. Pereira
Date: 2018-08-21 00:00:00

Population synthesis is concerned with the generation of synthetic yet
realistic representations of populations. It is a fundamental problem in the
modeling of transport where the synthetic populations of micro-agents represent
a key input to most agent-based models. In this paper, a new methodological
framework for how to 'grow' pools of micro-agents is presented. The model
framework adopts a deep generative modeling approach from machine learning
based on a Variational Autoencoder (VAE). Compared to the previous population
synthesis approaches, including Iterative Proportional Fitting (IPF), Gibbs
sampling and traditional generative models such as Bayesian Networks or Hidden
Markov Models, the proposed method allows fitting the full joint distribution
for high dimensions. The proposed methodology is compared with a conventional
Gibbs sampler and a Bayesian Network by using a large-scale Danish trip diary.
It is shown that, while these two methods outperform the VAE in the
low-dimensional case, they both suffer from scalability issues when the number
of modeled attributes increases. It is also shown that the Gibbs sampler
essentially replicates the agents from the original sample when the required
conditional distributions are estimated as frequency tables. In contrast, the
VAE allows addressing the problem of sampling zeros by generating agents that
are virtually different from those in the original data but have similar
statistical properties. The presented approach can support agent-based modeling
at all levels by enabling richer synthetic populations with smaller zones and
more detailed individual characteristics.
Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing
Authors: Noha Radwan, Wolfram Burgard, Abhinav Valada
Date: 2018-08-21 00:00:00
Tasks: motion prediction, multimodal interaction

For mobile robots navigating on sidewalks, it is essential to be able to safely cross street intersections. Most existing approaches rely on the recognition of the traffic light signal to make an informed crossing decision. Although these approaches have been crucial enablers for urban navigation, the capabilities of robots employing such approaches are still limited to navigating only on streets containing signalized intersections. In this paper, we address this challenge and propose a multimodal convolutional neural network framework to predict the safety of a street intersection for crossing. Our architecture consists of two subnetworks; an interaction-aware trajectory estimation stream IA-TCNN, that predicts the future states of all observed traffic participants in the scene, and a traffic light recognition stream AtteNet. Our IA-TCNN utilizes dilated causal convolutions to model the behavior of the observable dynamic agents in the scene without explicitly assigning priorities to the interactions among them. While AtteNet utilizes Squeeze-Excitation blocks to learn a content-aware mechanism for selecting the relevant features from the data, thereby improving the noise robustness. Learned representations from the traffic light recognition stream are fused with the estimated trajectories from the motion prediction stream to learn the crossing decision. Furthermore, we extend our previously introduced Freiburg Street Crossing dataset with sequences captured at different types of intersections, demonstrating complex interactions among the traffic participants. Extensive experimental evaluations on public benchmark datasets and our proposed dataset demonstrate that our network achieves state-of-the-art performance for each of the subtasks, as well as for the crossing safety prediction.
Multi-Source Pointer Network for Product Title Summarization
Authors: Fei Sun, Peng Jiang, Hanxiao Sun, Changhua Pei, Wenwu Ou, Xiaobo Wang
Date: 2018-08-21 00:00:00
Tasks: Sentence, Sentence Summarization

In this paper, we study the product title summarization problem in E-commerce
applications for display on mobile devices. Comparing with conventional
sentence summarization, product title summarization has some extra and
essential constraints. For example, factual errors or loss of the key
information are intolerable for E-commerce applications. Therefore, we abstract
two more constraints for product title summarization: (i) do not introduce
irrelevant information; (ii) retain the key information (e.g., brand name and
commodity name). To address these issues, we propose a novel multi-source
pointer network by adding a new knowledge encoder for pointer network. The
first constraint is handled by pointer mechanism. For the second constraint, we
restore the key information by copying words from the knowledge encoder with
the help of the soft gating mechanism. For evaluation, we build a large
collection of real-world product titles along with human-written short titles.
Experimental results demonstrate that our model significantly outperforms the
other baselines. Finally, online deployment of our proposed model has yielded a
significant business impact, as measured by the click-through rate.
Self-supervised learning of a facial attribute embedding from video
Authors: Olivia Wiles, A. Sophia Koepke, Andrew Zisserman
Date: 2018-08-21 00:00:00
Tasks: Attribute, Self-Supervised Learning, Unsupervised Facial Landmark Detection

We propose a self-supervised framework for learning facial attributes by
simply watching videos of a human face speaking, laughing, and moving over
time. To perform this task, we introduce a network, Facial Attributes-Net
(FAb-Net), that is trained to embed multiple frames from the same video
face-track into a common low-dimensional space. With this approach, we make
three contributions: first, we show that the network can leverage information
from multiple source frames by predicting confidence/attention masks for each
frame; second, we demonstrate that using a curriculum learning regime improves
the learned embedding; finally, we demonstrate that the network learns a
meaningful face embedding that encodes information about head pose, facial
landmarks and facial expression, i.e. facial attributes, without having been
supervised with any labelled data. We are comparable or superior to
state-of-the-art self-supervised methods on these tasks and approach the
performance of supervised methods.
Automatic Generation of Text Descriptive Comments for Code Blocks
Authors: Yuding Liang, Kenny Q. Zhu
Date: 2018-08-21 00:00:00
Tasks: Descriptive

We propose a framework to automatically generate descriptive comments for
source code blocks. While this problem has been studied by many researchers
previously, their methods are mostly based on fixed template and achieves poor
results. Our framework does not rely on any template, but makes use of a new
recursive neural network called Code-RNN to extract features from the source
code and embed them into one vector. When this vector representation is input
to a new recurrent neural network (Code-GRU), the overall framework generates
text descriptions of the code with accuracy (Rouge-2 value) significantly
higher than other learning-based approaches such as sequence-to-sequence model.
The Code-RNN model can also be used in other scenario where the representation
of code is required.
Adversarial training for multi-context joint entity and relation extraction
Authors: Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder
Date: 2018-08-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Joint Entity and Relation Extraction, Relation, Relation Extraction

Adversarial training (AT) is a regularization method that can be used to
improve the robustness of neural network methods by adding small perturbations
in the training data. We show how to use AT for the tasks of entity recognition
and relation extraction. In particular, we demonstrate that applying AT to a
general purpose baseline model for jointly extracting entities and relations,
allows improving the state-of-the-art effectiveness on several datasets in
different contexts (i.e., news, biomedical, and real estate data) and for
different languages (English and Dutch).
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks
Authors: Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, Yi Yang
Date: 2018-08-21 00:00:00

This paper proposed a Soft Filter Pruning (SFP) method to accelerate the
inference procedure of deep Convolutional Neural Networks (CNNs). Specifically,
the proposed SFP enables the pruned filters to be updated when training the
model after pruning. SFP has two advantages over previous works: (1) Larger
model capacity. Updating previously pruned filters provides our approach with
larger optimization space than fixing the filters to zero. Therefore, the
network trained by our method has a larger model capacity to learn from the
training data. (2) Less dependence on the pre-trained model. Large capacity
enables SFP to train from scratch and prune the model simultaneously. In
contrast, previous filter pruning methods should be conducted on the basis of
the pre-trained model to guarantee their performance. Empirically, SFP from
scratch outperforms the previous filter pruning methods. Moreover, our approach
has been demonstrated effective for many advanced CNN architectures. Notably,
on ILSCRC-2012, SFP reduces more than 42% FLOPs on ResNet-101 with even 0.2%
top-5 accuracy improvement, which has advanced the state-of-the-art. Code is
publicly available on GitHub: https://github.com/he-y/soft-filter-pruning
Machine Learning for Spatiotemporal Sequence Forecasting: A Survey
Authors: Xingjian Shi, Dit-yan Yeung
Date: 2018-08-21 00:00:00
Tasks: BIG-bench Machine Learning, Survey, Trajectory Forecasting

Spatiotemporal systems are common in the real-world. Forecasting the
multi-step future of these spatiotemporal systems based on the past
observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant
and challenging problem. Although lots of real-world problems can be viewed as
STSF and many research works have proposed machine learning based methods for
them, no existing work has summarized and compared these methods from a unified
perspective. This survey aims to provide a systematic review of machine
learning for STSF. In this survey, we define the STSF problem and classify it
into three subcategories: Trajectory Forecasting of Moving Point Cloud
(TF-MPC), STSF on Regular Grid (STSF-RG) and STSF on Irregular Grid (STSF-IG).
We then introduce the two major challenges of STSF: 1) how to learn a model for
multi-step forecasting and 2) how to adequately model the spatial and temporal
structures. After that, we review the existing works for solving these
challenges, including the general learning strategies for multi-step
forecasting, the classical machine learning based methods for STSF, and the
deep learning based methods for STSF. We also compare these methods and point
out some potential research directions.
Demonstrating PAR4SEM - A Semantic Writing Aid with Adaptive Paraphrasing
Authors: Seid Muhie Yimam, Chris Biemann
Date: 2018-08-21 00:00:00
Proceeding: EMNLP 2018 11
Tasks: BIG-bench Machine Learning, Text Simplification

In this paper, we present Par4Sem, a semantic writing aid tool based on
adaptive paraphrasing. Unlike many annotation tools that are primarily used to
collect training examples, Par4Sem is integrated into a real word application,
in this case a writing aid tool, in order to collect training examples from
usage data. Par4Sem is a tool, which supports an adaptive, iterative, and
interactive process where the underlying machine learning models are updated
for each iteration using new training examples from usage data. After
motivating the use of ever-learning tools in NLP applications, we evaluate
Par4Sem by adopting it to a text simplification task through mere usage.
Deep Video-Based Performance Cloning
Authors: Kfir Aberman, Mingyi Shi, Jing Liao, Dani Lischinski, Baoquan Chen, Daniel Cohen-Or
Date: 2018-08-21 00:00:00

We present a new video-based performance cloning technique. After training a
deep generative network using a reference video capturing the appearance and
dynamics of a target actor, we are able to generate videos where this actor
reenacts other performances. All of the training data and the driving
performances are provided as ordinary video segments, without motion capture or
depth information. Our generative model is realized as a deep neural network
with two branches, both of which train the same space-time conditional
generator, using shared weights. One branch, responsible for learning to
generate the appearance of the target actor in various poses, uses
\emph{paired} training data, self-generated from the reference video. The
second branch uses unpaired data to improve generation of temporally coherent
video renditions of unseen pose sequences. We demonstrate a variety of
promising results, where our method is able to generate temporally coherent
videos, for challenging scenarios where the reference and driving videos
consist of very different dance performances. Supplementary video:
https://youtu.be/JpwsEeqNhhA.
Search for Common Minima in Joint Optimization of Multiple Cost Functions
Authors: Daiki Adachi, Naoto Tsujimoto, Ryosuke Akashi, Synge Todo, Shinji Tsuneyuki
Date: 2018-08-21 00:00:00

We present a novel optimization method, named the Combined Optimization
Method (COM), for the joint optimization of two or more cost functions. Unlike
the conventional joint optimization schemes, which try to find minima in a
weighted sum of cost functions, the COM explores search space for common minima
shared by all the cost functions. Given a set of multiple cost functions that
have qualitatively different distributions of local minima with each other, the
proposed method finds the common minima with a high success rate without the
help of any metaheuristics. As a demonstration, we apply the COM to the crystal
structure prediction in materials science. By introducing the concept of data
assimilation, i.e., adopting the theoretical potential energy of the crystal
and the crystallinity, which characterizes the agreement with the theoretical
and experimental X-ray diffraction patterns, as cost functions, we show that
the correct crystal structures of Si diamond, low quartz, and low cristobalite
can be predicted with significantly higher success rates than the previous
methods.
Deep Learned Full-3D Object Completion from Single View
Authors: Dario Rethage, Federico Tombari, Felix Achilles, Nassir Navab
Date: 2018-08-21 00:00:00
Tasks: 3D geometry, 3D Reconstruction, Object, Scene Understanding

3D geometry is a very informative cue when interacting with and navigating an
environment. This writing proposes a new approach to 3D reconstruction and
scene understanding, which implicitly learns 3D geometry from depth maps
pairing a deep convolutional neural network architecture with an auto-encoder.
A data set of synthetic depth views and voxelized 3D representations is built
based on ModelNet, a large-scale collection of CAD models, to train networks.
The proposed method offers a significant advantage over current, explicit
reconstruction methods in that it learns key geometric features offline and
makes use of those to predict the most probable reconstruction of an unseen
object. The relatively small network, consisting of roughly 4 million weights,
achieves a 92.9% reconstruction accuracy at a 30x30x30 resolution through the
use of a pre-trained decompression layer. This is roughly 1/4 the weights of
the current leading network. The fast execution time of the model makes it
suitable for real-time applications.
Fully-Convolutional Point Networks for Large-Scale Point Clouds
Authors: Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari
Date: 2018-08-21 00:00:00
Proceeding: ECCV 2018 9
Tasks: Semantic Segmentation

This work proposes a general-purpose, fully-convolutional network
architecture for efficiently processing large-scale 3D data. One striking
characteristic of our approach is its ability to process unorganized 3D
representations such as point clouds as input, then transforming them
internally to ordered structures to be processed via 3D convolutions. In
contrast to conventional approaches that maintain either unorganized or
organized representations, from input to output, our approach has the advantage
of operating on memory efficient input data representations while at the same
time exploiting the natural structure of convolutional operations to avoid the
redundant computing and storing of spatial information in the network. The
network eliminates the need to pre- or post process the raw sensor data. This,
together with the fully-convolutional nature of the network, makes it an
end-to-end method able to process point clouds of huge spaces or even entire
rooms with up to 200k points at once. Another advantage is that our network can
produce either an ordered output or map predictions directly onto the input
cloud, thus making it suitable as a general-purpose point cloud descriptor
applicable to many 3D tasks. We demonstrate our network's ability to
effectively learn both low-level features as well as complex compositional
relationships by evaluating it on benchmark datasets for semantic voxel
segmentation, semantic part segmentation and 3D scene captioning.
Analysis of Speeches in Indian Parliamentary Debates
Authors: Sakala Venkata Krishna Rohit, Navjyoti Singh
Date: 2018-08-21 00:00:00
Tasks: General Classification, Stance Classification

With the increasing usage of the internet, more and more data is being
digitized including parliamentary debates but they are in an unstructured
format. There is a need to convert them into a structured format for linguistic
analysis. Much work has been done on parliamentary data such as Hansard,
American congressional floor-debate data on various aspects but less on
pragmatics. In this paper, we provide a dataset for the synopsis of Indian
parliamentary debates and perform stance classification of speeches i.e
identifying if the speaker is supporting the bill/issue or against it. We also
analyze the intention of the speeches beyond mere sentences i.e pragmatics in
the parliament. Based on thorough manual analysis of the debates, we developed
an annotation scheme of 4 mutually exclusive categories to analyze the purpose
of the speeches: to find out ISSUES, to BLAME, to APPRECIATE and for CALL FOR
ACTION. We have annotated the dataset provided, with these 4 categories and
conducted preliminary experiments for automatic detection of the categories.
Our automated classification approach gave us promising results.
Measuring Semantic Abstraction of Multilingual NMT with Paraphrase Recognition and Generation Tasks
Authors: Jörg Tiedemann, Yves Scherrer
Date: 2018-08-21 00:00:00
Proceeding: WS 2019 6
Tasks: Decoder, NMT, Paraphrase Generation, Translation

In this paper, we investigate whether multilingual neural translation models learn stronger semantic abstractions of sentences than bilingual ones. We test this hypotheses by measuring the perplexity of such models when applied to paraphrases of the source language. The intuition is that an encoder produces better representations if a decoder is capable of recognizing synonymous sentences in the same language even though the model is never trained for that task. In our setup, we add 16 different auxiliary languages to a bidirectional bilingual baseline model (English-French) and test it with in-domain and out-of-domain paraphrases in English. The results show that the perplexity is significantly reduced in each of the cases, indicating that meaning can be grounded in translation. This is further supported by a study on paraphrase generation that we also include at the end of the paper.
The Influence of Down-Sampling Strategies on SVD Word Embedding Stability
Authors: Johannes Hellrich, Bernd Kampe, Udo Hahn
Date: 2018-08-21 00:00:00
Proceeding: WS 2019 6
Tasks: Word Embeddings

The stability of word embedding algorithms, i.e., the consistency of the word
representations they reveal when trained repeatedly on the same data set, has
recently raised concerns. We here compare word embedding algorithms on three
corpora of different sizes, and evaluate both their stability and accuracy. We
find strong evidence that down-sampling strategies (used as part of their
training procedures) are particularly influential for the stability of
SVDPPMI-type embeddings. This finding seems to explain diverging reports on
their stability and lead us to a simple modification which provides superior
stability as well as accuracy on par with skip-gram embeddings.
Are You Tampering With My Data?
Authors: Michele Alberti, Vinaychandran Pondenkandath, Marcel Würsch, Manuel Bouillon, Mathias Seuret, Rolf Ingold, Marcus Liwicki
Date: 2018-08-21 00:00:00

We propose a novel approach towards adversarial attacks on neural networks
(NN), focusing on tampering the data used for training instead of generating
attacks on trained models. Our network-agnostic method creates a backdoor
during training which can be exploited at test time to force a neural network
to exhibit abnormal behaviour. We demonstrate on two widely used datasets
(CIFAR-10 and SVHN) that a universal modification of just one pixel per image
for all the images of a class in the training set is enough to corrupt the
training procedure of several state-of-the-art deep neural networks causing the
networks to misclassify any images to which the modification is applied. Our
aim is to bring to the attention of the machine learning community, the
possibility that even learning-based methods that are personally trained on
public datasets can be subject to attacks by a skillful adversary.
Text-to-image Synthesis via Symmetrical Distillation Networks
Authors: Mingkuan Yuan, Yuxin Peng
Date: 2018-08-21 00:00:00
Tasks: Image Generation

Text-to-image synthesis aims to automatically generate images according to
text descriptions given by users, which is a highly challenging task. The main
issues of text-to-image synthesis lie in two gaps: the heterogeneous and
homogeneous gaps. The heterogeneous gap is between the high-level concepts of
text descriptions and the pixel-level contents of images, while the homogeneous
gap exists between synthetic image distributions and real image distributions.
For addressing these problems, we exploit the excellent capability of generic
discriminative models (e.g. VGG19), which can guide the training process of a
new generative model on multiple levels to bridge the two gaps. The high-level
representations can teach the generative model to extract necessary visual
information from text descriptions, which can bridge the heterogeneous gap. The
mid-level and low-level representations can lead it to learn structures and
details of images respectively, which relieves the homogeneous gap. Therefore,
we propose Symmetrical Distillation Networks (SDN) composed of a source
discriminative model as "teacher" and a target generative model as "student".
The target generative model has a symmetrical structure with the source
discriminative model, in order to transfer hierarchical knowledge accessibly.
Moreover, we decompose the training process into two stages with different
distillation paradigms for promoting the performance of the target generative
model. Experiments on two widely-used datasets are conducted to verify the
effectiveness of our proposed SDN.