pwc-archive/papers-with-abstracts huggingface dataset:
Using of heterogeneous corpora for training of an ASR system
Authors: Jan Trmal, Gaurav Kumar, Vimal Manohar, Sanjeev Khudanpur, Matt Post, Paul McNamee
Date: 2017-06-01 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech-to-Text, Speech-to-Text Translation, Translation

The paper summarizes the development of the LVCSR system built as a part of
the Pashto speech-translation system at the SCALE (Summer Camp for Applied
Language Exploration) 2015 workshop on "Speech-to-text-translation for
low-resource languages". The Pashto language was chosen as a good "proxy"
low-resource language, exhibiting multiple phenomena which make the
speech-recognition and and speech-to-text-translation systems development hard.
  Even when the amount of data is seemingly sufficient, given the fact that the
data originates from multiple sources, the preliminary experiments reveal that
there is little to no benefit in merging (concatenating) the corpora and more
elaborate ways of making use of all of the data must be worked out.
  This paper concentrates only on the LVCSR part and presents a range of
different techniques that were found to be useful in order to benefit from
multiple different corpora
Selective Classification for Deep Neural Networks
Authors: Yonatan Geifman, Ran El-Yaniv
Date: 2017-05-23 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Classification, General Classification

Selective classification techniques (also known as reject option) have not
yet been considered in the context of deep neural networks (DNNs). These
techniques can potentially significantly improve DNNs prediction performance by
trading-off coverage. In this paper we propose a method to construct a
selective classifier given a trained neural network. Our method allows a user
to set a desired risk level. At test time, the classifier rejects instances as
needed, to grant the desired risk (with high probability). Empirical results
over CIFAR and ImageNet convincingly demonstrate the viability of our method,
which opens up possibilities to operate DNNs in mission-critical applications.
For example, using our method an unprecedented 2% error in top-5 ImageNet
classification can be guaranteed with probability 99.9%, and almost 60% test
coverage.
Transfer Learning for Speech Recognition on a Budget
Authors: Julius Kunze, Louis Kirsch, Ilia Kurenkov, Andreas Krug, Jens Johannsmeier, Sebastian Stober
Date: 2017-06-01 00:00:00
Proceeding: WS 2017 8
Tasks: GPU, speech-recognition, Speech Recognition, Transfer Learning

End-to-end training of automated speech recognition (ASR) systems requires
massive data and compute resources. We explore transfer learning based on model
adaptation as an approach for training ASR models under constrained GPU memory,
throughput and training data. We conduct several systematic experiments
adapting a Wav2Letter convolutional neural network originally trained for
English ASR to the German language. We show that this technique allows faster
training on consumer-grade resources while requiring less training data in
order to achieve the same accuracy, thereby lowering the cost of training ASR
models in other languages. Model introspection revealed that small adaptations
to the network's weights were sufficient for good performance, especially for
inner layers.
Automatic Image Annotation via Label Transfer in the Semantic Space
Authors: Tiberio Uricchio, Lamberto Ballan, Lorenzo Seidenari, Alberto del Bimbo
Date: 2016-05-16 00:00:00
Tasks: Denoising

Automatic image annotation is among the fundamental problems in computer
vision and pattern recognition, and it is becoming increasingly important in
order to develop algorithms that are able to search and browse large-scale
image collections. In this paper, we propose a label propagation framework
based on Kernel Canonical Correlation Analysis (KCCA), which builds a latent
semantic space where correlation of visual and textual features are well
preserved into a semantic embedding. The proposed approach is robust and can
work either when the training set is well annotated by experts, as well as when
it is noisy such as in the case of user-generated tags in social media. We
report extensive results on four popular datasets. Our results show that our
KCCA-based framework can be applied to several state-of-the-art label transfer
methods to obtain significant improvements. Our approach works even with the
noisy tags of social users, provided that appropriate denoising is performed.
Experiments on a large scale setting show that our method can provide some
benefits even when the semantic space is estimated on a subset of training
images.
Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware
Authors: Denis Kleyko, E. Paxon Frady, Mansour Kheffache, Evgeny Osipov
Date: 2017-06-01 00:00:00
Tasks: Computational Efficiency, Time Series, Time Series Analysis

We propose an approximation of Echo State Networks (ESN) that can be efficiently implemented on digital hardware based on the mathematics of hyperdimensional computing. The reservoir of the proposed integer Echo State Network (intESN) is a vector containing only n-bits integers (where n<8 is normally sufficient for a satisfactory performance). The recurrent matrix multiplication is replaced with an efficient cyclic shift operation. The proposed intESN approach is verified with typical tasks in reservoir computing: memorizing of a sequence of inputs; classifying time-series; learning dynamic processes. Such architecture results in dramatic improvements in memory footprint and computational efficiency, with minimal performance loss. The experiments on a field-programmable gate array confirm that the proposed intESN approach is much more energy efficient than the conventional ESN.
Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement
Authors: Jakub Sochor, Roman Juránek, Adam Herout
Date: 2017-02-21 00:00:00
Tasks: Camera Calibration, Vehicle Speed Estimation

In this paper, we focus on fully automatic traffic surveillance camera
calibration, which we use for speed measurement of passing vehicles. We improve
over a recent state-of-the-art camera calibration method for traffic
surveillance based on two detected vanishing points. More importantly, we
propose a novel automatic scene scale inference method. The method is based on
matching bounding boxes of rendered 3D models of vehicles with detected
bounding boxes in the image. The proposed method can be used from arbitrary
viewpoints, since it has no constraints on camera placement. We evaluate our
method on the recent comprehensive dataset for speed measurement BrnoCompSpeed.
Experiments show that our automatic camera calibration method by detection of
two vanishing points reduces error by 50% (mean distance ratio error reduced
from 0.18 to 0.09) compared to the previous state-of-the-art method. We also
show that our scene scale inference method is more precise, outperforming both
state-of-the-art automatic calibration method for speed measurement (error
reduction by 86% -- 7.98km/h to 1.10km/h) and manual calibration (error
reduction by 19% -- 1.35km/h to 1.10km/h). We also present qualitative results
of the proposed automatic camera calibration method on video sequences obtained
from real surveillance cameras in various places, and under different lighting
conditions (night, dawn, day).
Learning Structures of Bayesian Networks for Variable Groups
Authors: Pekka Parviainen, Samuel Kaski
Date: 2015-08-31 00:00:00

Bayesian networks, and especially their structures, are powerful tools for
representing conditional independencies and dependencies between random
variables. In applications where related variables form a priori known groups,
chosen to represent different "views" to or aspects of the same entities, one
may be more interested in modeling dependencies between groups of variables
rather than between individual variables. Motivated by this, we study prospects
of representing relationships between variable groups using Bayesian network
structures. We show that for dependency structures between groups to be
expressible exactly, the data have to satisfy the so-called groupwise
faithfulness assumption. We also show that one cannot learn causal relations
between groups using only groupwise conditional independencies, but also
variable-wise relations are needed. Additionally, we present algorithms for
finding the groupwise dependency structures.
Network driven sampling; a critical threshold for design effects
Authors: Karl Rohe
Date: 2015-05-20 00:00:00
Tasks: Clustering, Survey Sampling

Web crawling, snowball sampling, and respondent-driven sampling (RDS) are
three types of network sampling techniques used to contact individuals in
hard-to-reach populations. This paper studies these procedures as a Markov
process on the social network that is indexed by a tree. Each node in this tree
corresponds to an observation and each edge in the tree corresponds to a
referral. Indexing with a tree (instead of a chain) allows for the sampled
units to refer multiple future units into the sample. In survey sampling, the
design effect characterizes the additional variance induced by a novel sampling
strategy. If the design effect is some value $DE$, then constructing an
estimator from the novel design makes the variance of the estimator $DE$ times
greater than it would be under a simple random sample with the same sample size
$n$. Under certain assumptions on the referral tree, the design effect of
network sampling has a critical threshold that is a function of the referral
rate $m$ and the clustering structure in the social network, represented by the
second eigenvalue of the Markov transition matrix, $\lambda_2$. If $m <
1/\lambda_2^2$, then the design effect is finite (i.e. the standard estimator
is $\sqrt{n}$-consistent). However, if $m > 1/\lambda_2^2$, then the design
effect grows with $n$ (i.e. the standard estimator is no longer
$\sqrt{n}$-consistent). Past this critical threshold, the standard error of the
estimator converges at the slower rate of $n^{\log_m \lambda_2}$. The Markov
model allows for nodes to be resampled; computational results show that the
findings hold in without-replacement sampling. To estimate confidence intervals
that adapt to the correct level of uncertainty, a novel resampling procedure is
proposed. Computational experiments compare this procedure to previous
techniques.
Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild
Authors: Zhen-Hua Feng, Josef Kittler, Muhammad Awais, Patrik Huber, Xiao-Jun Wu
Date: 2017-05-05 00:00:00
Tasks: Face Alignment, Face Detection, Pose Estimation

We present a framework for robust face detection and landmark localisation of
faces in the wild, which has been evaluated as part of `the 2nd Facial Landmark
Localisation Competition'. The framework has four stages: face detection,
bounding box aggregation, pose estimation and landmark localisation. To achieve
a high detection rate, we use two publicly available CNN-based face detectors
and two proprietary detectors. We aggregate the detected face bounding boxes of
each input image to reduce false positives and improve face detection accuracy.
A cascaded shape regressor, trained using faces with a variety of pose
variations, is then employed for pose estimation and image pre-processing.
Last, we train the final cascaded shape regressor for fine-grained landmark
localisation, using a large number of training samples with limited pose
variations. The experimental results obtained on the 300W and Menpo benchmarks
demonstrate the superiority of our framework over state-of-the-art methods.
Polish Read Speech Corpus for Speech Tools and Services
Authors: Danijel Koržinek, Krzysztof Marasek, Łukasz Brocki, Krzysztof Wołk
Date: 2017-06-01 00:00:00
Tasks: Action Detection, Activity Detection, Grapheme-to-Phoneme Conversion, Keyword Spotting, speaker-diarization, Speaker Diarization, Speech-to-Text

This paper describes the speech processing activities conducted at the Polish
consortium of the CLARIN project. The purpose of this segment of the project
was to develop specific tools that would allow for automatic and semi-automatic
processing of large quantities of acoustic speech data. The tools include the
following: grapheme-to-phoneme conversion, speech-to-text alignment, voice
activity detection, speaker diarization, keyword spotting and automatic speech
transcription. Furthermore, in order to develop these tools, a large
high-quality studio speech corpus was recorded and released under an open
license, to encourage development in the area of Polish speech research.
Another purpose of the corpus was to serve as a reference for studies in
phonetics and pronunciation. All the tools and resources were released on the
the Polish CLARIN website. This paper discusses the current status and future
plans for the project.
Supervised Quantile Normalisation
Authors: Marine Le Morvan, Jean-Philippe Vert
Date: 2017-06-01 00:00:00

Quantile normalisation is a popular normalisation method for data subject to
unwanted variations such as images, speech, or genomic data. It applies a
monotonic transformation to the feature values of each sample to ensure that
after normalisation, they follow the same target distribution for each sample.
Choosing a "good" target distribution remains however largely empirical and
heuristic, and is usually done independently of the subsequent analysis of
normalised data. We propose instead to couple the quantile normalisation step
with the subsequent analysis, and to optimise the target distribution jointly
with the other parameters in the analysis. We illustrate this principle on the
problem of estimating a linear model over normalised data, and show that it
leads to a particular low-rank matrix regression problem that can be solved
efficiently. We illustrate the potential of our method, which we term SUQUAN,
on simulated data, images and genomic data, where it outperforms standard
quantile normalisation.
Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning
Authors: Filip de Roos, Philipp Hennig
Date: 2017-06-01 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis, Transfer Learning

Solving symmetric positive definite linear problems is a fundamental
computational task in machine learning. The exact solution, famously, is
cubicly expensive in the size of the matrix. To alleviate this problem, several
linear-time approximations, such as spectral and inducing-point methods, have
been suggested and are now in wide use. These are low-rank approximations that
choose the low-rank space a priori and do not refine it over time. While this
allows linear cost in the data-set size, it also causes a finite, uncorrected
approximation error. Authors from numerical linear algebra have explored ways
to iteratively refine such low-rank approximations, at a cost of a small number
of matrix-vector multiplications. This idea is particularly interesting in the
many situations in machine learning where one has to solve a sequence of
related symmetric positive definite linear problems. From the machine learning
perspective, such deflation methods can be interpreted as transfer learning of
a low-rank approximation across a time-series of numerical tasks. We study the
use of such methods for our field. Our empirical results show that, on
regression and classification problems of intermediate size, this approach can
interpolate between low computational cost and numerical precision.
An Effective Approach for Point Clouds Registration Based on the Hard and Soft Assignments
Authors: Congcong Jin, Jihua Zhu, Yaochen Li, SHAOYI DU, Zhongyu Li, Huimin Lu
Date: 2017-06-01 00:00:00

For the registration of partially overlapping point clouds, this paper
proposes an effective approach based on both the hard and soft assignments.
Given two initially posed clouds, it firstly establishes the forward
correspondence for each point in the data shape and calculates the value of
binary variable, which can indicate whether this point correspondence is
located in the overlapping areas or not. Then, it establishes the bilateral
correspondence and computes bidirectional distances for each point in the
overlapping areas. Based on the ratio of bidirectional distances, the
exponential function is selected and utilized to calculate the probability
value, which can indicate the reliability of the point correspondence.
Subsequently, both the values of hard and soft assignments are embedded into
the proposed objective function for registration of partially overlapping point
clouds and a novel variant of ICP algorithm is proposed to obtain the optimal
rigid transformation. The proposed approach can achieve good registration of
point clouds, even when their overlap percentage is low. Experimental results
tested on public data sets illustrate its superiority over previous approaches
on accuracy and robustness.
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications
Authors: Giorgio Roffo
Date: 2017-06-01 00:00:00
Tasks: feature selection, Information Retrieval, Learning-To-Rank, Recommendation Systems, Retrieval

The last decade has seen a revolution in the theory and application of
machine learning and pattern recognition. Through these advancements, variable
ranking has emerged as an active and growing research area and it is now
beginning to be applied to many new problems. The rationale behind this fact is
that many pattern recognition problems are by nature ranking problems. The main
objective of a ranking algorithm is to sort objects according to some criteria,
so that, the most relevant items will appear early in the produced result list.
Ranking methods can be analyzed from two different methodological perspectives:
ranking to learn and learning to rank. The former aims at studying methods and
techniques to sort objects for improving the accuracy of a machine learning
model. Enhancing a model performance can be challenging at times. For example,
in pattern classification tasks, different data representations can complicate
and hide the different explanatory factors of variation behind the data. In
particular, hand-crafted features contain many cues that are either redundant
or irrelevant, which turn out to reduce the overall accuracy of the classifier.
In such a case feature selection is used, that, by producing ranked lists of
features, helps to filter out the unwanted information. Moreover, in real-time
systems (e.g., visual trackers) ranking approaches are used as optimization
procedures which improve the robustness of the system that deals with the high
variability of the image streams that change over time. The other way around,
learning to rank is necessary in the construction of ranking models for
information retrieval, biometric authentication, re-identification, and
recommender systems. In this context, the ranking model's purpose is to sort
objects according to their degrees of relevance, importance, or preference as
defined in the specific application.
Deep Learning for Hate Speech Detection in Tweets
Authors: Pinkesh Badjatiya, Shashank Gupta, Manish Gupta, Vasudeva Varma
Date: 2017-06-01 00:00:00
Tasks: 16k, Deep Learning, Event Extraction, Hate Speech Detection, Sentiment Analysis, Word Embeddings

Hate speech detection on Twitter is critical for applications like
controversial event extraction, building AI chatterbots, content
recommendation, and sentiment analysis. We define this task as being able to
classify a tweet as racist, sexist or neither. The complexity of the natural
language constructs makes this task very challenging. We perform extensive
experiments with multiple deep learning architectures to learn semantic word
embeddings to handle this complexity. Our experiments on a benchmark dataset of
16K annotated tweets show that such deep learning methods outperform
state-of-the-art char/word n-gram methods by ~18 F1 points.
Efficient learning with robust gradient descent
Authors: Matthew J. Holland, Kazushi Ikeda
Date: 2017-06-01 00:00:00

Minimizing the empirical risk is a popular training strategy, but for
learning tasks where the data may be noisy or heavy-tailed, one may require
many observations in order to generalize well. To achieve better performance
under less stringent requirements, we introduce a procedure which constructs a
robust approximation of the risk gradient for use in an iterative learning
routine. Using high-probability bounds on the excess risk of this algorithm, we
show that our update does not deviate far from the ideal gradient-based update.
Empirical tests using both controlled simulations and real-world benchmark data
show that in diverse settings, the proposed procedure can learn more
efficiently, using less resources (iterations and observations) while
generalizing better.
Data Driven Coded Aperture Design for Depth Recovery
Authors: Prasan A Shedligeri, Sreyas Mohan, Kaushik Mitra
Date: 2017-05-29 00:00:00

Inserting a patterned occluder at the aperture of a camera lens has been
shown to improve the recovery of depth map and all-focus image compared to a
fully open aperture. However, design of the aperture pattern plays a very
critical role. Previous approaches for designing aperture codes make simple
assumptions on image distributions to obtain metrics for evaluating aperture
codes. However, real images may not follow those assumptions and hence the
designed code may not be optimal for them. To address this drawback we propose
a data driven approach for learning the optimal aperture pattern to recover
depth map from a single coded image. We propose a two stage architecture where,
in the first stage we simulate coded aperture images from a training dataset of
all-focus images and depth maps and in the second stage we recover the depth
map using a deep neural network. We demonstrate that our learned aperture code
performs better than previously designed codes even on code design metrics
proposed by previous approaches.
Bilinear CNNs for Fine-grained Visual Recognition
Authors: Tsung-Yu Lin, Aruni RoyChowdhury, Subhransu Maji
Date: 2015-04-29 00:00:00
Tasks: Fine-Grained Image Classification, Fine-Grained Visual Recognition, GPU, image-classification, Image Classification, Scene Recognition

We present a simple and effective architecture for fine-grained visual
recognition called Bilinear Convolutional Neural Networks (B-CNNs). These
networks represent an image as a pooled outer product of features derived from
two CNNs and capture localized feature interactions in a translationally
invariant manner. B-CNNs belong to the class of orderless texture
representations but unlike prior work they can be trained in an end-to-end
manner. Our most accurate model obtains 84.1%, 79.4%, 86.9% and 91.3% per-image
accuracy on the Caltech-UCSD birds [67], NABirds [64], FGVC aircraft [42], and
Stanford cars [33] dataset respectively and runs at 30 frames-per-second on a
NVIDIA Titan X GPU. We then present a systematic analysis of these networks and
show that (1) the bilinear features are highly redundant and can be reduced by
an order of magnitude in size without significant loss in accuracy, (2) are
also effective for other image classification tasks such as texture and scene
recognition, and (3) can be trained from scratch on the ImageNet dataset
offering consistent improvements over the baseline architecture. Finally, we
present visualizations of these models on various datasets using top
activations of neural units and gradient-based inversion techniques. The source
code for the complete system is available at http://vis-www.cs.umass.edu/bcnn.