pwc-archive/papers-with-abstracts huggingface dataset:
VoiceLoop: Voice Fitting and Synthesis via a Phonological Loop
Authors: Yaniv Taigman, Lior Wolf, Adam Polyak, Eliya Nachmani
Date: 2017-07-20 00:00:00
Proceeding: ICLR 2018 1
Tasks: Sentence, text-to-speech, Text to Speech

We present a new neural text to speech (TTS) method that is able to transform
text to speech in voices that are sampled in the wild. Unlike other systems,
our solution is able to deal with unconstrained voice samples and without
requiring aligned phonemes or linguistic features. The network architecture is
simpler than those in the existing literature and is based on a novel shifting
buffer working memory. The same buffer is used for estimating the attention,
computing the output audio, and for updating the buffer itself. The input
sentence is encoded using a context-free lookup table that contains one entry
per character or phoneme. The speakers are similarly represented by a short
vector that can also be fitted to new identities, even with only a few samples.
Variability in the generated speech is achieved by priming the buffer prior to
generating the audio. Experimental results on several datasets demonstrate
convincing capabilities, making TTS accessible to a wider range of
applications. In order to promote reproducibility, we release our source code
and models.
Lifelong Learning with Dynamically Expandable Networks
Authors: Jaehong Yoon, Eunho Yang, Jeongtae Lee, Sung Ju Hwang
Date: 2017-08-04 00:00:00
Proceeding: ICLR 2018 1
Tasks: Lifelong learning

We propose a novel deep network architecture for lifelong learning which we
refer to as Dynamically Expandable Network (DEN), that can dynamically decide
its network capacity as it trains on a sequence of tasks, to learn a compact
overlapping knowledge sharing structure among tasks. DEN is efficiently trained
in an online manner by performing selective retraining, dynamically expands
network capacity upon arrival of each task with only the necessary number of
units, and effectively prevents semantic drift by splitting/duplicating units
and timestamping them. We validate DEN on multiple public datasets under
lifelong learning scenarios, on which it not only significantly outperforms
existing lifelong learning methods for deep networks, but also achieves the
same level of performance as the batch counterparts with substantially fewer
number of parameters. Further, the obtained network fine-tuned on all tasks
obtained significantly better performance over the batch models, which shows
that it can be used to estimate the optimal network structure even when all
tasks are available in the first place.
On Understanding and Machine Understanding
Authors: Tong Chern
Date: 2011-03-24 00:00:00

In the present paper, we try to propose a self-similar network theory for the
basic understanding. By extending the natural languages to a kind of so called
idealy sufficient language, we can proceed a few steps to the investigation of
the language searching and the language understanding of AI.
  Image understanding, and the familiarity of the brain to the surrounding
environment are also discussed. Group effects are discussed by addressing the
essense of the power of influences, and constructing the influence network of a
society. We also give a discussion of inspirations.
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Authors: Bing Yu, Haoteng Yin, Zhanxing Zhu
Date: 2017-09-14 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting, Time Series Prediction, Traffic Prediction

Timely accurate traffic forecast is crucial for urban traffic control and
guidance. Due to the high nonlinearity and complexity of traffic flow,
traditional methods cannot satisfy the requirements of mid-and-long term
prediction tasks and often neglect spatial and temporal dependencies. In this
paper, we propose a novel deep learning framework, Spatio-Temporal Graph
Convolutional Networks (STGCN), to tackle the time series prediction problem in
traffic domain. Instead of applying regular convolutional and recurrent units,
we formulate the problem on graphs and build the model with complete
convolutional structures, which enable much faster training speed with fewer
parameters. Experiments show that our model STGCN effectively captures
comprehensive spatio-temporal correlations through modeling multi-scale traffic
networks and consistently outperforms state-of-the-art baselines on various
real-world traffic datasets.
Reliable Decision Support using Counterfactual Models
Authors: Peter Schulam, Suchi Saria
Date: 2017-03-30 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: counterfactual, Decision Making

Decision-makers are faced with the challenge of estimating what is likely to
happen when they take an action. For instance, if I choose not to treat this
patient, are they likely to die? Practitioners commonly use supervised learning
algorithms to fit predictive models that help decision-makers reason about
likely future outcomes, but we show that this approach is unreliable, and
sometimes even dangerous. The key issue is that supervised learning algorithms
are highly sensitive to the policy used to choose actions in the training data,
which causes the model to capture relationships that do not generalize. We
propose using a different learning objective that predicts counterfactuals
instead of predicting outcomes under an existing action policy as in supervised
learning. To support decision-making in temporal settings, we introduce the
Counterfactual Gaussian Process (CGP) to predict the counterfactual future
progression of continuous-time trajectories under sequences of future actions.
We demonstrate the benefits of the CGP on two important decision-support tasks:
risk prediction and "what if?" reasoning for individualized treatment planning.
Online optimal exact identification of a quantum change point
Authors: Gael Sentís, Esteban Martínez-Vargas, Ramon Muñoz-Tapia
Date: 2018-02-01 00:00:00

We consider online detection strategies for identifying a change point in a
stream of quantum particles allegedly prepared in identical states. We show
that the identification of the change point can be done without error via
sequential local measurements while attaining the optimal performance bound set
by quantum mechanics. In this way, we establish the task of exactly identifying
a quantum change point as an instance where local protocols are as powerful as
global ones. The optimal online detection strategy requires only one bit of
memory between subsequent measurements, and it is amenable to experimental
realization with current technology.
HoloFace: Augmenting Human-to-Human Interactions on HoloLens
Authors: Marek Kowalski, Zbigniew Nasarzewski, Grzegorz Galinski, Piotr Garbat
Date: 2018-02-01 00:00:00
Tasks: Attribute, Emotion Recognition, Face Alignment, Face Model, Head Pose Estimation, Pose Estimation, Retrieval

We present HoloFace, an open-source framework for face alignment, head pose
estimation and facial attribute retrieval for Microsoft HoloLens. HoloFace
implements two state-of-the-art face alignment methods which can be used
interchangeably: one running locally and one running on a remote backend. Head
pose estimation is accomplished by fitting a deformable 3D model to the
landmarks localized using face alignment. The head pose provides both the
rotation of the head and a position in the world space. The parameters of the
fitted 3D face model provide estimates of facial attributes such as mouth
opening or smile. Together the above information can be used to augment the
faces of people seen by the HoloLens user, and thus their interaction.
Potential usage scenarios include facial recognition, emotion recognition, eye
gaze tracking and many others. We demonstrate the capabilities of our framework
by augmenting the faces of people seen through the HoloLens with various
objects and animations.
Emerging Language Spaces Learned From Massively Multilingual Corpora
Authors: Jörg Tiedemann
Date: 2018-02-01 00:00:00
Tasks: Machine Translation, Translation

Translations capture important information about languages that can be used
as implicit supervision in learning linguistic properties and semantic
representations. In an information-centric view, translated texts may be
considered as semantic mirrors of the original text and the significant
variations that we can observe across various languages can be used to
disambiguate a given expression using the linguistic signal that is grounded in
translation. Parallel corpora consisting of massive amounts of human
translations with a large linguistic variation can be applied to increase
abstractions and we propose the use of highly multilingual machine translation
models to find language-independent meaning representations. Our initial
experiments show that neural machine translation models can indeed learn in
such a setup and we can show that the learning algorithm picks up information
about the relation between languages in order to optimize transfer leaning with
shared parameters. The model creates a continuous language space that
represents relationships in terms of geometric distances, which we can
visualize to illustrate how languages cluster according to language families
and groups. Does this open the door for new ideas of data-driven language
typology with promising models and techniques in empirical cross-linguistic
research?
Hierarchical Aggregation Approach for Distributed clustering of spatial datasets
Authors: Malika Bendechache, Nhien-An Le-Khac, M-Tahar Kechadi
Date: 2018-02-01 00:00:00
Tasks: Clustering

In this paper, we present a new approach of distributed clustering for
spatial datasets, based on an innovative and efficient aggregation technique.
This distributed approach consists of two phases: 1) local clustering phase,
where each node performs a clustering on its local data, 2) aggregation phase,
where the local clusters are aggregated to produce global clusters. This
approach is characterised by the fact that the local clusters are represented
in a simple and efficient way. And The aggregation phase is designed in such a
way that the final clusters are compact and accurate while the overall process
is efficient in both response time and memory allocation. We evaluated the
approach with different datasets and compared it to well-known clustering
techniques. The experimental results show that our approach is very promising
and outperforms all those algorithms
VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control
Authors: Jingwei Zhang, Lei Tai, Peng Yun, Yufeng Xiong, Ming Liu, Joschka Boedecker, Wolfram Burgard
Date: 2018-02-01 00:00:00
Tasks: Deep Reinforcement Learning, Domain Adaptation, Reinforcement Learning, Style Transfer

In this paper, we deal with the reality gap from a novel perspective,
targeting transferring Deep Reinforcement Learning (DRL) policies learned in
simulated environments to the real-world domain for visual control tasks.
Instead of adopting the common solutions to the problem by increasing the
visual fidelity of synthetic images output from simulators during the training
phase, we seek to tackle the problem by translating the real-world image
streams back to the synthetic domain during the deployment phase, to make the
robot feel at home. We propose this as a lightweight, flexible, and efficient
solution for visual control, as 1) no extra transfer steps are required during
the expensive training of DRL agents in simulation; 2) the trained DRL agents
will not be constrained to being deployable in only one specific real-world
environment; 3) the policy training and the transfer operations are decoupled,
and can be conducted in parallel. Besides this, we propose a simple yet
effective shift loss that is agnostic to the downstream task, to constrain the
consistency between subsequent frames which is important for consistent policy
outputs. We validate the shift loss for artistic style transfer for videos and
domain adaptation, and validate our visual control approach in indoor and
outdoor robotics experiments.
A Nonparametric Delayed Feedback Model for Conversion Rate Prediction
Authors: Yuya Yoshikawa, Yusaku Imai
Date: 2018-02-01 00:00:00
Tasks: Prediction

Predicting conversion rates (CVRs) in display advertising (e.g., predicting
the proportion of users who purchase an item (i.e., a conversion) after its
corresponding ad is clicked) is important when measuring the effects of ads
shown to users and to understanding the interests of the users. There is
generally a time delay (i.e., so-called {\it delayed feedback}) between the ad
click and conversion. Owing to the delayed feedback, samples that are converted
after an observation period may be treated as negative. To overcome this
drawback, CVR prediction assuming that the time delay follows an exponential
distribution has been proposed. In practice, however, there is no guarantee
that the delay is generated from the exponential distribution, and the best
distribution with which to represent the delay depends on the data. In this
paper, we propose a nonparametric delayed feedback model for CVR prediction
that represents the distribution of the time delay without assuming a
parametric distribution, such as an exponential or Weibull distribution.
Because the distribution of the time delay is modeled depending on the content
of an ad and the features of a user, various shapes of the distribution can be
represented potentially. In experiments, we show that the proposed model can
capture the distribution for the time delay on a synthetic dataset, even when
the distribution is complicated. Moreover, on a real dataset, we show that the
proposed model outperforms the existing method that assumes an exponential
distribution for the time delay in terms of conversion rate prediction.
Phonetic and Graphemic Systems for Multi-Genre Broadcast Transcription
Authors: Yu Wang, Xie Chen, Mark Gales, Anton Ragni, Jeremy Wong
Date: 2018-02-01 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

State-of-the-art English automatic speech recognition systems typically use
phonetic rather than graphemic lexicons. Graphemic systems are known to perform
less well for English as the mapping from the written form to the spoken form
is complicated. However, in recent years the representational power of
deep-learning based acoustic models has improved, raising interest in graphemic
acoustic models for English, due to the simplicity of generating the lexicon.
In this paper, phonetic and graphemic models are compared for an English
Multi-Genre Broadcast transcription task. A range of acoustic models based on
lattice-free MMI training are constructed using phonetic and graphemic
lexicons. For this task, it is found that having a long-span temporal history
reduces the difference in performance between the two forms of models. In
addition, system combination is examined, using parameter smoothing and
hypothesis combination. As the combination approaches become more complicated
the difference between the phonetic and graphemic systems further decreases.
Finally, for all configurations examined the combination of phonetic and
graphemic systems yields consistent gains.
Densely Dilated Spatial Pooling Convolutional Network using benign loss functions for imbalanced volumetric prostate segmentation
Authors: Qiuhua Liu, Min Fu, Xinqi Gong, Hao Jiang
Date: 2018-01-31 00:00:00
Tasks: Decoder

The high incidence rate of prostate disease poses a requirement in early
detection for diagnosis. As one of the main imaging methods used for prostate
cancer detection, Magnetic Resonance Imaging (MRI) has wide range of appearance
and imbalance problems, making automated prostate segmentation fundamental but
challenging. Here we propose a novel Densely Dilated Spatial Pooling
Convolutional Network (DDSP ConNet) in encoder-decoder structure. It employs
dense structure to combine dilated convolution and global pooling, thus
supplies coarse segmentation results from encoder and decoder subnet and
preserves more contextual information. To obtain richer hierarchical feature
maps, residual long connection is furtherly adopted to fuse contexture
features. Meanwhile, we adopt DSC loss and Jaccard loss functions to train our
DDSP ConNet. We surprisingly found and proved that, in contrast to re-weighted
cross entropy, DSC loss and Jaccard loss have a lot of benign properties in
theory, including symmetry, continuity and differentiability about the
parameters of network. Extensive experiments on the MICCAI PROMISE12 challenge
dataset have been done to corroborate the effectiveness of our DDSP ConNet with
DSC loss and Jaccard loss. Totally, our method achieves a score of 85.78 in the
test dataset, outperforming most of other competitors.
Greedy Active Learning Algorithm for Logistic Regression Models
Authors: Hsiang-Ling Hsu, Yuan-Chin Ivan Chang, Ray-Bing Chen
Date: 2018-02-01 00:00:00
Tasks: Active Learning, Binary Classification, Experimental Design, General Classification, regression, Variable Selection

We study a logistic model-based active learning procedure for binary
classification problems, in which we adopt a batch subject selection strategy
with a modified sequential experimental design method. Moreover, accompanying
the proposed subject selection scheme, we simultaneously conduct a greedy
variable selection procedure such that we can update the classification model
with all labeled training subjects. The proposed algorithm repeatedly performs
both subject and variable selection steps until a prefixed stopping criterion
is reached. Our numerical results show that the proposed procedure has
competitive performance, with smaller training size and a more compact model,
comparing with that of the classifier trained with all variables and a full
data set. We also apply the proposed procedure to a well-known wave data set
(Breiman et al., 1984) to confirm the performance of our method.
Face Aging with Contextual Generative Adversarial Nets
Authors: Si Liu, Yao Sun, Defa Zhu, Renda Bao, Wei Wang, Xiangbo Shu, Shuicheng Yan
Date: 2018-02-01 00:00:00
Tasks: Face Verification

Face aging, which renders aging faces for an input face, has attracted
extensive attention in the multimedia research. Recently, several conditional
Generative Adversarial Nets (GANs) based methods have achieved great success.
They can generate images fitting the real face distributions conditioned on
each individual age group. However, these methods fail to capture the
transition patterns, e.g., the gradual shape and texture changes between
adjacent age groups. In this paper, we propose a novel Contextual Generative
Adversarial Nets (C-GANs) to specifically take it into consideration. The
C-GANs consists of a conditional transformation network and two discriminative
networks. The conditional transformation network imitates the aging procedure
with several specially designed residual blocks. The age discriminative network
guides the synthesized face to fit the real conditional distribution. The
transition pattern discriminative network is novel, aiming to distinguish the
real transition patterns with the fake ones. It serves as an extra
regularization term for the conditional transformation network, ensuring the
generated image pairs to fit the corresponding real transition pattern
distribution. Experimental results demonstrate the proposed framework produces
appealing results by comparing with the state-of-the-art and ground truth. We
also observe performance gain for cross-age face verification.
On Polynomial time Constructions of Minimum Height Decision Tree
Authors: Nader H. Bshouty, Waseem Makhoul
Date: 2018-02-01 00:00:00

In this paper we study a polynomial time algorithms that for an input
$A\subseteq {B_m}$ outputs a decision tree for $A$ of minimum depth. This
problem has many applications that include, to name a few, computer vision,
group testing, exact learning from membership queries and game theory.
  Arkin et al. and Moshkov gave a polynomial time $(\ln |A|)$- approximation
algorithm (for the depth). The result of Dinur and Steurer for set cover
implies that this problem cannot be approximated with ratio $(1-o(1))\cdot \ln
|A|$, unless P=NP. Moskov the combinatorial measure of extended teaching
dimension of $A$, $ETD(A)$. He showed that $ETD(A)$ is a lower bound for the
depth of the decision tree for $A$ and then gave an {\it exponential time}
$ETD(A)/\log(ETD(A))$-approximation algorithm.
  In this paper we further study the $ETD(A)$ measure and a new combinatorial
measure, $DEN(A)$, that we call the density of the set $A$. We show that
$DEN(A)\le ETD(A)+1$. We then give two results. The first result is that the
lower bound $ETD(A)$ of Moshkov for the depth of the decision tree for $A$ is
greater than the bounds that are obtained by the classical technique used in
the literature. The second result is a polynomial time $(\ln 2)
DEN(A)$-approximation (and therefore $(\ln 2) ETD(A)$-approximation) algorithm
for the depth of the decision tree of $A$. We also show that a better
approximation ratio implies P=NP.
  We then apply the above results to learning the class of disjunctions of
predicates from membership queries. We show that the $ETD$ of this class is
bounded from above by the degree $d$ of its Hasse diagram. We then show that
Moshkov algorithm can be run in polynomial time and is $(d/\log
d)$-approximation algorithm. This gives optimal algorithms when the degree is
constant. For example, learning axis parallel rays over constant dimension
space.
Adapting predominant and novel sense discovery algorithms for identifying corpus-specific sense differences
Authors: Binny Mathew, Suman Kalyan Maity, Pratip Sarkar, Animesh Mukherjee, Pawan Goyal
Date: 2018-02-01 00:00:00
Proceeding: WS 2017 8

Word senses are not static and may have temporal, spatial or corpus-specific
scopes. Identifying such scopes might benefit the existing WSD systems largely.
In this paper, while studying corpus specific word senses, we adapt three
existing predominant and novel-sense discovery algorithms to identify these
corpus-specific senses. We make use of text data available in the form of
millions of digitized books and newspaper archives as two different sources of
corpora and propose automated methods to identify corpus-specific word senses
at various time points. We conduct an extensive and thorough human judgment
experiment to rigorously evaluate and compare the performance of these
approaches. Post adaptation, the output of the three algorithms are in the same
format and the accuracy results are also comparable, with roughly 45-60% of the
reported corpus-specific senses being judged as genuine.
Indexing of CNN Features for Large Scale Image Search
Authors: Ruoyu Liu, Yao Zhao, Shikui Wei, Yi Yang
Date: 2015-08-02 00:00:00
Tasks: Clustering, Image Retrieval, Quantization, Retrieval

The convolutional neural network (CNN) features can give a good description
of image content, which usually represent images with unique global vectors.
Although they are compact compared to local descriptors, they still cannot
efficiently deal with large-scale image retrieval due to the cost of the linear
incremental computation and storage. To address this issue, we build a simple
but effective indexing framework based on inverted table, which significantly
decreases both the search time and memory usage. In addition, several
strategies are fully investigated under an indexing framework to adapt it to
CNN features and compensate for quantization errors. First, we use multiple
assignment for the query and database images to increase the probability of
relevant images' co-existing in the same Voronoi cells obtained via the
clustering algorithm. Then, we introduce embedding codes to further improve
precision by removing false matches during a search. We demonstrate that by
using hashing schemes to calculate the embedding codes and by changing the
ranking rule, indexing framework speeds can be greatly improved. Extensive
experiments conducted on several unsupervised and supervised benchmarks support
these results and the superiority of the proposed indexing framework. We also
provide a fair comparison between the popular CNN features.