pwc-archive/papers-with-abstracts huggingface dataset:
On Asymptotic Covariances of A Few Unrotated Factor Solutions
Authors: Xingwei Hu
Date: 2018-11-12 00:00:00

In this paper, we provide explicit formulas, in terms of the covariances of
sample covariances or sample correlations, for the asymptotic covariances of
unrotated factor loading estimates and unique variance estimates. These
estimates are extracted from least square, principal, iterative principal
component, alpha or image factor analysis. If the sample is taken from a
multivariate normal population, these formulas, together with the delta
methods, will produce the standard errors for the rotated loading estimates. A
simulation study shows that the formulas provide reasonable results.
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
Authors: Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, Haifeng Li
Date: 2018-11-12 00:00:00
Tasks: Management, Prediction, Traffic Prediction

Accurate and real-time traffic forecasting plays an important role in the
Intelligent Traffic System and is of great significance for urban traffic
planning, traffic management, and traffic control. However, traffic forecasting
has always been considered an open scientific issue, owing to the constraints
of urban road network topological structure and the law of dynamic change with
time, namely, spatial dependence and temporal dependence. To capture the
spatial and temporal dependence simultaneously, we propose a novel neural
network-based traffic forecasting method, the temporal graph convolutional
network (T-GCN) model, which is in combination with the graph convolutional
network (GCN) and gated recurrent unit (GRU). Specifically, the GCN is used to
learn complex topological structures to capture spatial dependence and the
gated recurrent unit is used to learn dynamic changes of traffic data to
capture temporal dependence. Then, the T-GCN model is employed to traffic
forecasting based on the urban road network. Experiments demonstrate that our
T-GCN model can obtain the spatio-temporal correlation from traffic data and
the predictions outperform state-of-art baselines on real-world traffic
datasets. Our tensorflow implementation of the T-GCN is available at
https://github.com/lehaifeng/T-GCN.
Fast Matrix Factorization with Non-Uniform Weights on Missing Data
Authors: Xiangnan He, Jinhui Tang, Xiaoyu Du, Richang Hong, Tongwei Ren, Tat-Seng Chua
Date: 2018-11-11 00:00:00

Matrix factorization (MF) has been widely used to discover the low-rank
structure and to predict the missing entries of data matrix. In many real-world
learning systems, the data matrix can be very high-dimensional but sparse. This
poses an imbalanced learning problem, since the scale of missing entries is
usually much larger than that of observed entries, but they cannot be ignored
due to the valuable negative signal. For efficiency concern, existing work
typically applies a uniform weight on missing entries to allow a fast learning
algorithm. However, this simplification will decrease modeling fidelity,
resulting in suboptimal performance for downstream applications.
  In this work, we weight the missing data non-uniformly, and more generically,
we allow any weighting strategy on the missing data. To address the efficiency
challenge, we propose a fast learning method, for which the time complexity is
determined by the number of observed entries in the data matrix, rather than
the matrix size. The key idea is two-fold: 1) we apply truncated SVD on the
weight matrix to get a more compact representation of the weights, and 2) we
learn MF parameters with element-wise alternating least squares (eALS) and
memorize the key intermediate variables to avoid repeating computations that
are unnecessary. We conduct extensive experiments on two recommendation
benchmarks, demonstrating the correctness, efficiency, and effectiveness of our
fast eALS method.
Correction of AI systems by linear discriminants: Probabilistic foundations
Authors: A. N. Gorban, A. Golubkov, B. Grechuk, E. M. Mirkes, I. Y. Tyukin
Date: 2018-11-11 00:00:00

Artificial Intelligence (AI) systems sometimes make errors and will make
errors in the future, from time to time. These errors are usually unexpected,
and can lead to dramatic consequences. Intensive development of AI and its
practical applications makes the problem of errors more important. Total
re-engineering of the systems can create new errors and is not always possible
due to the resources involved. The important challenge is to develop fast
methods to correct errors without damaging existing skills. We formulated the
technical requirements to the 'ideal' correctors. Such correctors include
binary classifiers, which separate the situations with high risk of errors from
the situations where the AI systems work properly. Surprisingly, for
essentially high-dimensional data such methods are possible: simple linear
Fisher discriminant can separate the situations with errors from correctly
solved tasks even for exponentially large samples. The paper presents the
probabilistic basis for fast non-destructive correction of AI systems. A series
of new stochastic separation theorems is proven. These theorems provide new
instruments for fast non-iterative correction of errors of legacy AI systems.
The new approaches become efficient in high-dimensions, for correction of
high-dimensional systems in high-dimensional world (i.e. for processing of
essentially high-dimensional data by large systems).
ADNet: A Deep Network for Detecting Adverts
Authors: Murhaf Hossari, Soumyabrata Dev, Matthew Nicholson, Killian McCabe, Atul Nautiyal, Clare Conran, Jian Tang, Wei Xu, François Pitié
Date: 2018-11-09 00:00:00
Tasks: Detecting Adverts, Marketing

Online video advertising gives content providers the ability to deliver
compelling content, reach a growing audience, and generate additional revenue
from online media. Recently, advertising strategies are designed to look for
original advert(s) in a video frame, and replacing them with new adverts. These
strategies, popularly known as product placement or embedded marketing, greatly
help the marketing agencies to reach out to a wider audience. However, in the
existing literature, such detection of candidate frames in a video sequence for
the purpose of advert integration, is done manually. In this paper, we propose
a deep-learning architecture called ADNet, that automatically detects the
presence of advertisements in video frames. Our approach is the first of its
kind that automatically detects the presence of adverts in a video frame, and
achieves state-of-the-art results on a public dataset.
Reasoning From Data in the Mathematical Theory of Evidence
Authors: Mieczysław Kłopotek
Date: 2018-11-09 00:00:00

Mathematical Theory of Evidence (MTE) is known as a foundation for reasoning
when knowledge is expressed at various levels of detail. Though much research
effort has been committed to this theory since its foundation, many questions
remain open. One of the most important open questions seems to be the
relationship between frequencies and the Mathematical Theory of Evidence. The
theory is blamed to leave frequencies outside (or aside of) its framework. The
seriousness of this accusation is obvious: no experiment may be run to compare
the performance of MTE-based models of real world processes against real world
data.
  In this paper we develop a frequentist model of the MTE bringing to fall the
above argument against MTE. We describe, how to interpret data in terms of MTE
belief functions, how to reason from data about conditional belief functions,
how to generate a random sample out of a MTE model, how to derive MTE model
from data and how to compare results of reasoning in MTE model and reasoning
from data.
  It is claimed in this paper that MTE is suitable to model some types of
destructive processes
Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages
Authors: Enno Hermann, Herman Kamper, Sharon Goldwater
Date: 2018-11-09 00:00:00
Tasks: Clustering

Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such as text corpora or pronunciation dictionaries). A good representation should capture phonetic content and abstract away from other types of variability, such as speaker differences and channel noise. Previous work in this area has primarily focused unsupervised learning from target language data only, and has been evaluated only intrinsically. Here we directly compare multiple methods, including some that use only target language speech data and some that use transcribed speech from other (non-target) languages, and we evaluate using two intrinsic measures as well as on a downstream unsupervised word segmentation and clustering task. We find that combining two existing target-language-only methods yields better features than either method alone. Nevertheless, even better results are obtained by extracting target language bottleneck features using a model trained on other languages. Cross-lingual training using just one other language is enough to provide this benefit, but multilingual training helps even more. In addition to these results, which hold across both intrinsic measures and the extrinsic task, we discuss the qualitative differences between the different types of learned features.
ExcitNet vocoder: A neural excitation model for parametric speech synthesis systems
Authors: Eunwoo Song, Kyungguen Byun, Hong-Goo Kang
Date: 2018-11-09 00:00:00
Tasks: Speech Synthesis

This paper proposes a WaveNet-based neural excitation model (ExcitNet) for statistical parametric speech synthesis systems. Conventional WaveNet-based neural vocoding systems significantly improve the perceptual quality of synthesized speech by statistically generating a time sequence of speech waveforms through an auto-regressive framework. However, they often suffer from noisy outputs because of the difficulties in capturing the complicated time-varying nature of speech signals. To improve modeling efficiency, the proposed ExcitNet vocoder employs an adaptive inverse filter to decouple spectral components from the speech signal. The residual component, i.e. excitation signal, is then trained and generated within the WaveNet framework. In this way, the quality of the synthesized speech signal can be further improved since the spectral component is well represented by a deep learning framework and, moreover, the residual component is efficiently generated by the WaveNet framework. Experimental results show that the proposed ExcitNet vocoder, trained both speaker-dependently and speaker-independently, outperforms traditional linear prediction vocoders and similarly configured conventional WaveNet vocoders.
AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms
Authors: Kou Tanaka, Hirokazu Kameoka, Takuhiro Kaneko, Nobukatsu Hojo
Date: 2018-11-09 00:00:00
Tasks: GPU, Image Captioning, Machine Translation, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Translation, Voice Conversion

This paper describes a method based on a sequence-to-sequence learning
(Seq2Seq) with attention and context preservation mechanism for voice
conversion (VC) tasks. Seq2Seq has been outstanding at numerous tasks involving
sequence modeling such as speech synthesis and recognition, machine
translation, and image captioning. In contrast to current VC techniques, our
method 1) stabilizes and accelerates the training procedure by considering
guided attention and proposed context preservation losses, 2) allows not only
spectral envelopes but also fundamental frequency contours and durations of
speech to be converted, 3) requires no context information such as phoneme
labels, and 4) requires no time-aligned source and target speech data in
advance. In our experiment, the proposed VC framework can be trained in only
one day, using only one GPU of an NVIDIA Tesla K80, while the quality of the
synthesized speech is higher than that of speech converted by Gaussian mixture
model-based VC and is comparable to that of speech generated by recurrent
neural network-based text-to-speech synthesis, which can be regarded as an
upper limit on VC performance.
Scene Parsing via Dense Recurrent Neural Networks with Attentional Selection
Authors: Heng Fan, Peng Chu, Longin Jan Latecki, Haibin Ling
Date: 2018-11-09 00:00:00
Tasks: Scene Labeling, Scene Parsing

Recurrent neural networks (RNNs) have shown the ability to improve scene
parsing through capturing long-range dependencies among image units. In this
paper, we propose dense RNNs for scene labeling by exploring various long-range
semantic dependencies among image units. Different from existing RNN based
approaches, our dense RNNs are able to capture richer contextual dependencies
for each image unit by enabling immediate connections between each pair of
image units, which significantly enhances their discriminative power. Besides,
to select relevant dependencies and meanwhile to restrain irrelevant ones for
each unit from dense connections, we introduce an attention model into dense
RNNs. The attention model allows automatically assigning more importance to
helpful dependencies while less weight to unconcerned dependencies. Integrating
with convolutional neural networks (CNNs), we develop an end-to-end scene
labeling system. Extensive experiments on three large-scale benchmarks
demonstrate that the proposed approach can improve the baselines by large
margins and outperform other state-of-the-art algorithms.
No-Frills Human-Object Interaction Detection: Factorization, Layout Encodings, and Training Techniques
Authors: Tanmay Gupta, Alexander Schwing, Derek Hoiem
Date: 2018-11-14 00:00:00
Proceeding: ICCV 2019 10
Tasks: Human-Object Interaction Detection, Object

We show that for human-object interaction detection a relatively simple factorized model with appearance and layout encodings constructed from pre-trained object detectors outperforms more sophisticated approaches. Our model includes factors for detection scores, human and object appearance, and coarse (box-pair configuration) and optionally fine-grained layout (human pose). We also develop training techniques that improve learning efficiency by: (1) eliminating a train-inference mismatch; (2) rejecting easy negatives during mini-batch training; and (3) using a ratio of negatives to positives that is two orders of magnitude larger than existing approaches. We conduct a thorough ablation study to understand the importance of different factors and training techniques using the challenging HICO-Det dataset.
Composing Modeling and Inference Operations with Probabilistic Program Combinators
Authors: Eli Sennesh, Adam Ścibior, Hao Wu, Jan-Willem van de Meent
Date: 2018-11-14 00:00:00

Probabilistic programs with dynamic computation graphs can define measures
over sample spaces with unbounded dimensionality, which constitute programmatic
analogues to Bayesian nonparametrics. Owing to the generality of this model
class, inference relies on `black-box' Monte Carlo methods that are often not
able to take advantage of conditional independence and exchangeability, which
have historically been the cornerstones of efficient inference. We here seek to
develop a `middle ground' between probabilistic models with fully dynamic and
fully static computation graphs. To this end, we introduce a combinator library
for the Probabilistic Torch framework. Combinators are functions that accept
models and return transformed models. We assume that models are dynamic, but
that model composition is static, in the sense that combinator application
takes place prior to evaluating the model on data. Combinators provide
primitives for both model and inference composition. Model combinators take the
form of classic functional programming constructs such as map and reduce. These
constructs define a computation graph at a coarsened level of representation,
in which nodes correspond to models, rather than individual variables.
Inference combinators implement operations such as importance resampling and
application of a transition kernel, which alter the evaluation strategy for a
model whilst preserving proper weighting. Owing to this property, models
defined using combinators can be trained using stochastic methods that optimize
either variational or wake-sleep style objectives. As a validation of this
principle, we use combinators to implement black box inference for hidden
Markov models.
Jointly Learning to Label Sentences and Tokens
Authors: Marek Rei, Anders Søgaard
Date: 2018-11-14 00:00:00
Tasks: Grammatical Error Detection, Sentence, Sentence Classification

Learning to construct text representations in end-to-end systems can be
difficult, as natural languages are highly compositional and task-specific
annotated datasets are often limited in size. Methods for directly supervising
language composition can allow us to guide the models based on existing
knowledge, regularizing them towards more robust and interpretable
representations. In this paper, we investigate how objectives at different
granularities can be used to learn better language representations and we
propose an architecture for jointly learning to label sentences and tokens. The
predictions at each level are combined together using an attention mechanism,
with token-level labels also acting as explicit supervision for composing
sentence-level representations. Our experiments show that by learning to
perform these tasks jointly on multiple levels, the model achieves substantial
improvements for both sentence classification and sequence labeling.
Domain Randomization for Scene-Specific Car Detection and Pose Estimation
Authors: Rawal Khirodkar, Donghyun Yoo, Kris M. Kitani
Date: 2018-11-14 00:00:00
Tasks: 3D Pose Estimation, Instance Segmentation, Pose Estimation, Synthetic Data Generation

We address the issue of domain gap when making use of synthetic data to train
a scene-specific object detector and pose estimator. While previous works have
shown that the constraints of learning a scene-specific model can be leveraged
to create geometrically and photometrically consistent synthetic data, care
must be taken to design synthetic content which is as close as possible to the
real-world data distribution. In this work, we propose to solve domain gap
through the use of appearance randomization to generate a wide range of
synthetic objects to span the space of realistic images for training. An
ablation study of our results is presented to delineate the individual
contribution of different components in the randomization process. We evaluate
our method on VIRAT, UA-DETRAC, EPFL-Car datasets, where we demonstrate that
using scene specific domain randomized synthetic data is better than
fine-tuning off-the-shelf models on limited real data.
Deep Nonlinear Non-Gaussian Filtering for Dynamical Systems
Authors: Arash Mehrjou, Bernhard Schölkopf
Date: 2018-11-14 00:00:00

Filtering is a general name for inferring the states of a dynamical system
given observations. The most common filtering approach is Gaussian Filtering
(GF) where the distribution of the inferred states is a Gaussian whose mean is
an affine function of the observations. There are two restrictions in this
model: Gaussianity and Affinity. We propose a model to relax both these
assumptions based on recent advances in implicit generative models. Empirical
results show that the proposed method gives a significant advantage over GF and
nonlinear methods based on fixed nonlinear kernels.
Streaming Network Embedding through Local Actions
Authors: Xi Liu, Ping-Chun Hsieh, Nick Duffield, Rui Chen, Muhe Xie, Xidao Wen
Date: 2018-11-14 00:00:00
Tasks: Clustering, Multi-class Classification, Network Embedding

Recently, considerable research attention has been paid to network embedding,
a popular approach to construct feature vectors of vertices. Due to the curse
of dimensionality and sparsity in graphical datasets, this approach has become
indispensable for machine learning tasks over large networks. The majority of
existing literature has considered this technique under the assumption that the
network is static. However, networks in many applications, nodes and edges
accrue to a growing network as a streaming. A small number of very recent
results have addressed the problem of embedding for dynamic networks. However,
they either rely on knowledge of vertex attributes, suffer high-time complexity
or need to be re-trained without closed-form expression. Thus the approach of
adapting the existing methods to the streaming environment faces non-trivial
technical challenges.
  These challenges motivate developing new approaches to the problems of
streaming network embedding. In this paper, We propose a new framework that is
able to generate latent features for new vertices with high efficiency and low
complexity under specified iteration rounds. We formulate a constrained
optimization problem for the modification of the representation resulting from
a stream arrival. We show this problem has no closed-form solution and instead
develop an online approximation solution. Our solution follows three steps: (1)
identify vertices affected by new vertices, (2) generate latent features for
new vertices, and (3) update the latent features of the most affected vertices.
The generated representations are provably feasible and not far from the
optimal ones in terms of expectation. Multi-class classification and clustering
on five real-world networks demonstrate that our model can efficiently update
vertex representations and simultaneously achieve comparable or even better
performance.
Improvements on SCORE, Especially for Weak Signals
Authors: Jiashun Jin, Zheng Tracy Ke, Shengming Luo
Date: 2018-11-14 00:00:00
Tasks: Clustering, Community Detection

A network may have weak signals and severe degree heterogeneity, and may be very sparse in one occurrence but very dense in another. SCORE (Jin, 2015) is a recent approach to network community detection. It accommodates severe degree heterogeneity and is adaptive to different levels of sparsity, but its performance for networks with weak signals is unclear. In this paper, we show that in a broad class of network settings where we allow for weak signals, severe degree heterogeneity, and a wide range of network sparsity, SCORE achieves prefect clustering and has the so-called "exponential rate" in Hamming clustering errors. The proof uses the most recent advancement on entry-wise bounds for the leading eigenvectors of the network adjacency matrix. The theoretical analysis assures us that SCORE continues to work well in the weak signal settings, but it does not rule out the possibility that SCORE may be further improved to have better performance in real applications, especially for networks with weak signals. As a second contribution of the paper, we propose SCORE+ as an improved version of SCORE. We investigate SCORE+ with 8 network data sets and found that it outperforms several representative approaches. In particular, for the 6 data sets with relatively strong signals, SCORE+ has similar performance as that of SCORE, but for the 2 data sets (Simmons, Caltech) with possibly weak signals, SCORE+ has much lower error rates. SCORE+ proposes several changes to SCORE. We carefully explain the rationale underlying each of these changes, using a mixture of theoretical and numerical study.