pwc-archive/papers-with-abstracts huggingface dataset:
Query-by-example Spoken Term Detection using Attention-based Multi-hop Networks
Authors: Chia-Wei Ao, Hung-Yi Lee
Date: 2017-09-01 00:00:00

Retrieving spoken content with spoken queries, or query-by- example spoken
term detection (STD), is attractive because it makes possible the matching of
signals directly on the acoustic level without transcribing them into text.
Here, we propose an end-to-end query-by-example STD model based on an
attention-based multi-hop network, whose input is a spoken query and an audio
segment containing several utterances; the output states whether the audio
segment includes the query. The model can be trained in either a supervised
scenario using labeled data, or in an unsupervised fashion. In the supervised
scenario, we find that the attention mechanism and multiple hops improve
performance, and that the attention weights indicate the time span of the
detected terms. In the unsupervised setting, the model mimics the behavior of
the existing query-by-example STD system, yielding performance comparable to
the existing system but with a lower search time complexity.
Event Forecasting with Pattern Markov Chains
Authors: Elias Alevizos, Alexander Artikis, Georgios Paliouras
Date: 2018-04-27 00:00:00

We present a system for online probabilistic event forecasting. We assume
that a user is interested in detecting and forecasting event patterns, given in
the form of regular expressions. Our system can consume streams of events and
forecast when the pattern is expected to be fully matched. As more events are
consumed, the system revises its forecasts to reflect possible changes in the
state of the pattern. The framework of Pattern Markov Chains is used in order
to learn a probabilistic model for the pattern, with which forecasts with
guaranteed precision may be produced, in the form of intervals within which a
full match is expected. Experimental results from real-world datasets are shown
and the quality of the produced forecasts is explored, using both precision
scores and two other metrics: spread, which refers to the "focusing resolution"
of a forecast (interval length), and distance, which captures how early a
forecast is reported.
Adversarial Ladder Networks
Authors: Juan Maroñas Molano, Alberto Albiol Colomer, Roberto Paredes Palacios
Date: 2016-11-07 00:00:00
Tasks: General Classification, Unsupervised Pre-training

The use of unsupervised data in addition to supervised data in training
discriminative neural networks has improved the performance of this clas-
sification scheme. However, the best results were achieved with a training
process that is divided in two parts: first an unsupervised pre-training step
is done for initializing the weights of the network and after these weights are
refined with the use of supervised data. On the other hand adversarial noise
has improved the results of clas- sical supervised learning. Recently, a new
neural network topology called Ladder Network, where the key idea is based in
some properties of hierar- chichal latent variable models, has been proposed as
a technique to train a neural network using supervised and unsupervised data at
the same time with what is called semi-supervised learning. This technique has
reached state of the art classification. In this work we add adversarial noise
to the ladder network and get state of the art classification, with several
important conclusions on how adversarial noise can help in addition with new
possible lines of investi- gation. We also propose an alternative to add
adversarial noise to unsu- pervised data.
dhSegment: A generic deep-learning approach for document segmentation
Authors: Sofia Ares Oliveira, Benoit Seguin, Frederic Kaplan
Date: 2018-04-27 00:00:00
Tasks: Deep Learning, Diversity, Document Layout Analysis

In recent years there have been multiple successful attempts tackling document processing problems separately by designing task specific hand-tuned strategies. We argue that the diversity of historical document processing tasks prohibits to solve them one at a time and shows a need for designing generic approaches in order to handle the variability of historical series. In this paper, we address multiple tasks simultaneously such as page extraction, baseline extraction, layout analysis or multiple typologies of illustrations and photograph extraction. We propose an open-source implementation of a CNN-based pixel-wise predictor coupled with task dependent post-processing blocks. We show that a single CNN-architecture can be used across tasks with competitive results. Moreover most of the task-specific post-precessing steps can be decomposed in a small number of simple and standard reusable operations, adding to the flexibility of our approach.
An Element Sensitive Saliency Model with Position Prior Learning for Web Pages
Authors: Yujun Gu, Jie Chang, Ya zhang, Yan-Feng Wang
Date: 2018-04-27 00:00:00
Tasks: Position, Prediction, Saliency Prediction

Understanding human visual attention is important for multimedia
applications. Many studies have attempted to learn from eye-tracking data and
build computational saliency prediction models. However, limited efforts have
been devoted to saliency prediction for Web pages, which are characterized by
more diverse content elements and spatial layouts. In this paper, we propose a
novel end-to-end deep generative saliency model for Web pages. To capture
position biases introduced by page layouts, a Position Prior Learning
sub-network is proposed, which models position biases as multivariate Gaussian
distribution using variational auto-encoder. To model different elements of a
Web page, a Multi Discriminative Region Detection (MDRD) branch and a Text
Region Detection(TRD) branch are introduced, which target to extract
discriminative localizations and "prominent" text regions likely to correspond
to human attention, respectively. We validate the proposed model with FiWI, a
public Web-page dataset, and shows that the proposed model outperforms the
state-of-art models for Web-page saliency prediction.
Network Representation Learning: A Survey
Authors: Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
Date: 2017-12-04 00:00:00
Tasks: Representation Learning, Survey

With the widespread use of information technologies, information networks are
becoming increasingly popular to capture complex relationships across various
disciplines, such as social networks, citation networks, telecommunication
networks, and biological networks. Analyzing these networks sheds light on
different aspects of social life such as the structure of societies,
information diffusion, and communication patterns. In reality, however, the
large scale of information networks often makes network analytic tasks
computationally expensive or intractable. Network representation learning has
been recently proposed as a new learning paradigm to embed network vertices
into a low-dimensional vector space, by preserving network topology structure,
vertex content, and other side information. This facilitates the original
network to be easily handled in the new vector space for further analysis. In
this survey, we perform a comprehensive review of the current literature on
network representation learning in the data mining and machine learning field.
We propose new taxonomies to categorize and summarize the state-of-the-art
network representation learning techniques according to the underlying learning
mechanisms, the network information intended to preserve, as well as the
algorithmic designs and methodologies. We summarize evaluation protocols used
for validating network representation learning including published benchmark
datasets, evaluation methods, and open source algorithms. We also perform
empirical studies to compare the performance of representative algorithms on
common datasets, and analyze their computational complexity. Finally, we
suggest promising research directions to facilitate future study.
Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation
Authors: Sohil Shah, Pallabi Ghosh, Larry S. Davis, Tom Goldstein
Date: 2018-04-27 00:00:00
Tasks: General Classification, Image Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

Many imaging tasks require global information about all pixels in an image.
Conventional bottom-up classification networks globalize information by
decreasing resolution; features are pooled and downsampled into a single
output. But for semantic segmentation and object detection tasks, a network
must provide higher-resolution pixel-level outputs. To globalize information
while preserving resolution, many researchers propose the inclusion of
sophisticated auxiliary blocks, but these come at the cost of a considerable
increase in network size and computational cost. This paper proposes stacked
u-nets (SUNets), which iteratively combine features from different resolution
scales while maintaining resolution. SUNets leverage the information
globalization power of u-nets in a deeper network architectures that is capable
of handling the complexity of natural images. SUNets perform extremely well on
semantic segmentation tasks using a small number of parameters.
Latent Fingerprint Recognition: Role of Texture Template
Authors: Kai Cao, Anil K. Jain
Date: 2018-04-27 00:00:00
Tasks: Graph Matching, Template Matching

We propose a texture template approach, consisting of a set of virtual
minutiae, to improve the overall latent fingerprint recognition accuracy. To
compensate for the lack of sufficient number of minutiae in poor quality latent
prints, we generate a set of virtual minutiae. However, due to a large number
of these regularly placed virtual minutiae, texture based template matching has
a large computational requirement compared to matching true minutiae templates.
To improve both the accuracy and efficiency of the texture template matching,
we investigate: i) both original and enhanced fingerprint patches for training
convolutional neural networks (ConvNets) to improve the distinctiveness of
descriptors associated with each virtual minutiae, ii) smaller patches around
virtual minutiae and a fast ConvNet architecture to speed up descriptor
extraction, iii) reduce the descriptor length, iv) a modified hierarchical
graph matching strategy to improve the matching speed, and v) extraction of
multiple texture templates to boost the performance. Experiments on NIST SD27
latent database show that the above strategies can improve the matching speed
from 11 ms (24 threads) per comparison (between a latent and a reference print)
to only 7.7 ms (single thread) per comparison while improving the rank-1
accuracy by 8.9% against 10K gallery.
Deep learning approach to Fourier ptychographic microscopy
Authors: Thanh Nguyen, Yujia Xue, Yunzhe Li, Lei Tian, George Nehmetallah
Date: 2018-04-27 00:00:00
Tasks: Deep Learning, Generative Adversarial Network, Time Series Analysis, Transfer Learning

Convolutional neural networks (CNNs) have gained tremendous success in
solving complex inverse problems. The aim of this work is to develop a novel
CNN framework to reconstruct video sequence of dynamic live cells captured
using a computational microscopy technique, Fourier ptychographic microscopy
(FPM). The unique feature of the FPM is its capability to reconstruct images
with both wide field-of-view (FOV) and high resolution, i.e. a large
space-bandwidth-product (SBP), by taking a series of low resolution intensity
images. For live cell imaging, a single FPM frame contains thousands of cell
samples with different morphological features. Our idea is to fully exploit the
statistical information provided by this large spatial ensemble so as to make
predictions in a sequential measurement, without using any additional temporal
dataset. Specifically, we show that it is possible to reconstruct high-SBP
dynamic cell videos by a CNN trained only on the first FPM dataset captured at
the beginning of a time-series experiment. Our CNN approach reconstructs a
12800X10800 pixels phase image using only ~25 seconds, a 50X speedup compared
to the model-based FPM algorithm. In addition, the CNN further reduces the
required number of images in each time frame by ~6X. Overall, this
significantly improves the imaging throughput by reducing both the acquisition
and computational times. The proposed CNN is based on the conditional
generative adversarial network (cGAN) framework. Additionally, we also exploit
transfer learning so that our pre-trained CNN can be further optimized to image
other cell types. Our technique demonstrates a promising deep learning approach
to continuously monitor large live-cell populations over an extended time and
gather useful spatial and temporal information with sub-cellular resolution.
Scalable Bilinear $π$ Learning Using State and Action Features
Authors: Yi-Chen Chen, Lihong Li, Mengdi Wang
Date: 2018-04-27 00:00:00
Tasks: Reinforcement Learning

Approximate linear programming (ALP) represents one of the major algorithmic
families to solve large-scale Markov decision processes (MDP). In this work, we
study a primal-dual formulation of the ALP, and develop a scalable, model-free
algorithm called bilinear $\pi$ learning for reinforcement learning when a
sampling oracle is provided. This algorithm enjoys a number of advantages.
First, it adopts (bi)linear models to represent the high-dimensional value
function and state-action distributions, using given state and action features.
Its run-time complexity depends on the number of features, not the size of the
underlying MDPs. Second, it operates in a fully online fashion without having
to store any sample, thus having minimal memory footprint. Third, we prove that
it is sample-efficient, solving for the optimal policy to high precision with a
sample complexity linear in the dimension of the parameter space.
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
Authors: Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Niki Parmar, Mike Schuster, Zhifeng Chen, Yonghui Wu, Macduff Hughes
Date: 2018-04-26 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, Translation

The past year has witnessed rapid advances in sequence-to-sequence (seq2seq)
modeling for Machine Translation (MT). The classic RNN-based approaches to MT
were first out-performed by the convolutional seq2seq model, which was then
out-performed by the more recent Transformer model. Each of these new
approaches consists of a fundamental architecture accompanied by a set of
modeling and training techniques that are in principle applicable to other
seq2seq architectures. In this paper, we tease apart the new architectures and
their accompanying techniques in two ways. First, we identify several key
modeling and training techniques, and apply them to the RNN architecture,
yielding a new RNMT+ model that outperforms all of the three fundamental
architectures on the benchmark WMT'14 English to French and English to German
tasks. Second, we analyze the properties of each fundamental seq2seq
architecture and devise new hybrid architectures intended to combine their
strengths. Our hybrid models obtain further improvements, outperforming the
RNMT+ model on both benchmark datasets.
Adversarial Training of Variational Auto-encoders for High Fidelity Image Generation
Authors: Salman H. Khan, Munawar Hayat, Nick Barnes
Date: 2018-04-27 00:00:00
Tasks: Image Generation, Vocal Bursts Intensity Prediction

Variational auto-encoders (VAEs) provide an attractive solution to image
generation problem. However, they tend to produce blurred and over-smoothed
images due to their dependence on pixel-wise reconstruction loss. This paper
introduces a new approach to alleviate this problem in the VAE based generative
models. Our model simultaneously learns to match the data, reconstruction loss
and the latent distributions of real and fake images to improve the quality of
generated samples. To compute the loss distributions, we introduce an
auto-encoder based discriminator model which allows an adversarial learning
procedure. The discriminator in our model also provides perceptual guidance to
the VAE by matching the learned similarity metric of the real and fake samples
in the latent space. To stabilize the overall training process, our model uses
an error feedback approach to maintain the equilibrium between competing
networks in the model. Our experiments show that the generated samples from our
proposed model exhibit a diverse set of attributes and facial expressions and
scale up to high-resolution images very well.
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
Authors: Lingjiao Chen, Hongyi Wang, Zachary Charles, Dimitris Papailiopoulos
Date: 2018-03-27 00:00:00
Proceeding: ICML 2018 7

Distributed model training is vulnerable to byzantine system failures and
adversarial compute nodes, i.e., nodes that use malicious updates to corrupt
the global model stored at a parameter server (PS). To guarantee some form of
robustness, recent work suggests using variants of the geometric median as an
aggregation rule, in place of gradient averaging. Unfortunately, median-based
rules can incur a prohibitive computational overhead in large-scale settings,
and their convergence guarantees often require strong assumptions. In this
work, we present DRACO, a scalable framework for robust distributed training
that uses ideas from coding theory. In DRACO, each compute node evaluates
redundant gradients that are used by the parameter server to eliminate the
effects of adversarial updates. DRACO comes with problem-independent robustness
guarantees, and the model that it trains is identical to the one trained in the
adversary-free setup. We provide extensive experiments on real datasets and
distributed setups across a variety of large-scale models, where we show that
DRACO is several times, to orders of magnitude faster than median-based
approaches.
Classification of auditory stimuli from EEG signals with a regulated recurrent neural network reservoir
Authors: Marc-Antoine Moinnereau, Thomas Brienne, Simon Brodeur, Jean Rouat, Kevin Whittingstall, Eric Plourde
Date: 2018-04-27 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG), General Classification

The use of electroencephalogram (EEG) as the main input signal in
brain-machine interfaces has been widely proposed due to the non-invasive
nature of the EEG. Here we are specifically interested in interfaces that
extract information from the auditory system and more specifically in the task
of classifying heard speech from EEGs. To do so, we propose to limit the
preprocessing of the EEGs and use machine learning approaches to automatically
extract their meaningful characteristics. More specifically, we use a regulated
recurrent neural network (RNN) reservoir, which has been shown to outperform
classic machine learning approaches when applied to several different
bio-signals, and we compare it with a deep neural network approach. Moreover,
we also investigate the classification performance as a function of the number
of EEG electrodes. A set of 8 subjects were presented randomly with 3 different
auditory stimuli (English vowels a, i and u). We obtained an excellent
classification rate of 83.2% with the RNN when considering all 64 electrodes. A
rate of 81.7% was achieved with only 10 electrodes.
CompNet: Neural networks growing via the compact network morphism
Authors: Jun Lu, Wei Ma, Boi Faltings
Date: 2018-04-27 00:00:00
Tasks: MORPH

It is often the case that the performance of a neural network can be improved
by adding layers. In real-world practices, we always train dozens of neural
network architectures in parallel which is a wasteful process. We explored
$CompNet$, in which case we morph a well-trained neural network to a deeper one
where network function can be preserved and the added layer is compact. The
work of the paper makes two contributions: a). The modified network can
converge fast and keep the same functionality so that we do not need to train
from scratch again; b). The layer size of the added layer in the neural network
is controlled by removing the redundant parameters with sparse optimization.
This differs from previous network morphism approaches which tend to add more
neurons or channels beyond the actual requirements and result in redundance of
the model. The method is illustrated using several neural network structures on
different data sets including MNIST and CIFAR10.
Recent Progresses in Deep Learning based Acoustic Models (Updated)
Authors: Dong Yu, Jinyu Li
Date: 2018-04-25 00:00:00
Tasks: Deep Learning, General Classification, Speech Enhancement, speech-recognition, Speech Recognition

In this paper, we summarize recent progresses made in deep learning based
acoustic models and the motivation and insights behind the surveyed techniques.
We first discuss acoustic models that can effectively exploit variable-length
contextual information, such as recurrent neural networks (RNNs), convolutional
neural networks (CNNs), and their various combination with other models. We
then describe acoustic models that are optimized end-to-end with emphasis on
feature representations learned jointly with rest of the system, the
connectionist temporal classification (CTC) criterion, and the attention-based
sequence-to-sequence model. We further illustrate robustness issues in speech
recognition systems, and discuss acoustic model adaptation, speech enhancement
and separation, and robust training strategies. We also cover modeling
techniques that lead to more efficient decoding and discuss possible future
directions in acoustic model research.
Combining Symbolic Expressions and Black-box Function Evaluations in Neural Programs
Authors: Forough Arabshahi, Sameer Singh, Animashree Anandkumar
Date: 2018-01-12 00:00:00
Proceeding: ICLR 2018 1
Tasks: valid

Neural programming involves training neural networks to learn programs,
mathematics, or logic from data. Previous works have failed to achieve good
generalization performance, especially on problems and programs with high
complexity or on large domains. This is because they mostly rely either on
black-box function evaluations that do not capture the structure of the
program, or on detailed execution traces that are expensive to obtain, and
hence the training data has poor coverage of the domain under consideration. We
present a novel framework that utilizes black-box function evaluations, in
conjunction with symbolic expressions that define relationships between the
given functions. We employ tree LSTMs to incorporate the structure of the
symbolic expression trees. We use tree encoding for numbers present in function
evaluation data, based on their decimal representation. We present an
evaluation benchmark for this task to demonstrate our proposed model combines
symbolic reasoning and function evaluation in a fruitful manner, obtaining high
accuracies in our experiments. Our framework generalizes significantly better
to expressions of higher depth and is able to fill partial equations with valid
completions.