pwc-archive/papers-with-abstracts huggingface dataset:
Robust Monocular SLAM for Egocentric Videos
Authors: Suvam Patra, Kartikeya Gupta, Faran Ahmad, Chetan Arora, Subhashis Banerjee
Date: 2017-07-18 00:00:00
Tasks: Simultaneous Localization and Mapping

Regardless of the tremendous progress, a truly general purpose pipeline for
Simultaneous Localization and Mapping (SLAM) remains a challenge. We
investigate the reported failure of state of the art (SOTA) SLAM techniques on
egocentric videos. We find that the dominant 3D rotations, low parallax between
successive frames, and primarily forward motion in egocentric videos are the
most common causes of failures. The incremental nature of SOTA SLAM, in the
presence of unreliable pose and 3D estimates in egocentric videos, with no
opportunities for global loop closures, generates drifts and leads to the
eventual failures of such techniques. Taking inspiration from batch mode
Structure from Motion (SFM) techniques, we propose to solve SLAM as an SFM
problem over the sliding temporal windows. This makes the problem well
constrained. Further, we propose to initialize the camera poses using 2D
rotation averaging, followed by translation averaging before structure
estimation using bundle adjustment. This helps in stabilizing the camera poses
when 3D estimates are not reliable. We show that the proposed SLAM technique,
incorporating the two key ideas works successfully for long, shaky egocentric
videos where other SOTA techniques have been reported to fail. Qualitative and
quantitative comparisons on publicly available egocentric video datasets
validate our results.
One-Shot Learning in Discriminative Neural Networks
Authors: Jordan Burgess, James Robert Lloyd, Zoubin Ghahramani
Date: 2017-07-18 00:00:00
Tasks: One-Shot Learning

We consider the task of one-shot learning of visual categories. In this paper
we explore a Bayesian procedure for updating a pretrained convnet to classify a
novel image category for which data is limited. We decompose this convnet into
a fixed feature extractor and softmax classifier. We assume that the target
weights for the new task come from the same distribution as the pretrained
softmax weights, which we model as a multivariate Gaussian. By using this as a
prior for the new weights, we demonstrate competitive performance with
state-of-the-art methods whilst also being consistent with 'normal' methods for
training deep networks on large data.
Spectral Filter Tracking
Authors: Zhen Cui, You yi Cai, Wen ming Zheng, Jian Yang
Date: 2017-07-18 00:00:00
Tasks: Graph Matching, Object Tracking, regression, Translation, Visual Object Tracking

Visual object tracking is a challenging computer vision task with numerous
real-world applications. Here we propose a simple but efficient Spectral Filter
Tracking (SFT)method. To characterize rotational and translation invariance of
tracking targets, the candidate image region is models as a pixelwise grid
graph. Instead of the conventional graph matching, we convert the tracking into
a plain least square regression problem to estimate the best center coordinate
of the target. But different from the holistic regression of correlation filter
based methods, SFT can operate on localized surrounding regions of each pixel
(i.e.,vertex) by using spectral graph filters, which thus is more robust to
resist local variations and cluttered background.To bypass the eigenvalue
decomposition problem of the graph Laplacian matrix L, we parameterize spectral
graph filters as the polynomial of L by spectral graph theory, in which L k
exactly encodes a k-hop local neighborhood of each vertex. Finally, the filter
parameters (i.e., polynomial coefficients) as well as feature projecting
functions are jointly integrated into the regression model.
Exploring Outliers in Crowdsourced Ranking for QoE
Authors: Qianqian Xu, Ming Yan, Chendi Huang, Jiechao Xiong, Qingming Huang, Yuan YAO
Date: 2017-07-18 00:00:00
Tasks: Outlier Detection

Outlier detection is a crucial part of robust evaluation for crowdsourceable
assessment of Quality of Experience (QoE) and has attracted much attention in
recent years. In this paper, we propose some simple and fast algorithms for
outlier detection and robust QoE evaluation based on the nonconvex optimization
principle. Several iterative procedures are designed with or without knowing
the number of outliers in samples. Theoretical analysis is given to show that
such procedures can reach statistically good estimates under mild conditions.
Finally, experimental results with simulated and real-world crowdsourcing
datasets show that the proposed algorithms could produce similar performance to
Huber-LASSO approach in robust ranking, yet with nearly 8 or 90 times speed-up,
without or with a prior knowledge on the sparsity size of outliers,
respectively. Therefore the proposed methodology provides us a set of helpful
tools for robust QoE evaluation with crowdsourcing data.
Boolean kernels for collaborative filtering in top-N item recommendation
Authors: Mirko Polato, Fabio Aiolli
Date: 2016-12-21 00:00:00
Tasks: Collaborative Filtering

In many personalized recommendation problems available data consists only of
positive interactions (implicit feedback) between users and items. This problem
is also known as One-Class Collaborative Filtering (OC-CF). Linear models
usually achieve state-of-the-art performances on OC-CF problems and many
efforts have been devoted to build more expressive and complex representations
able to improve the recommendations. Recent analysis show that collaborative
filtering (CF) datasets have peculiar characteristics such as high sparsity and
a long tailed distribution of the ratings. In this paper we propose a boolean
kernel, called Disjunctive kernel, which is less expressive than the linear one
but it is able to alleviate the sparsity issue in CF contexts. The embedding of
this kernel is composed by all the combinations of a certain arity d of the
input variables, and these combined features are semantically interpreted as
disjunctions of the input variables. Experiments on several CF datasets show
the effectiveness and the efficiency of the proposed kernel.
A Deep Multi-Level Network for Saliency Prediction
Authors: Marcella Cornia, Lorenzo Baraldi, Giuseppe Serra, Rita Cucchiara
Date: 2016-09-05 00:00:00
Tasks: Prediction, Saliency Prediction

This paper presents a novel deep architecture for saliency prediction.
Current state of the art models for saliency prediction employ Fully
Convolutional networks that perform a non-linear combination of features
extracted from the last convolutional layer to predict saliency maps. We
propose an architecture which, instead, combines features extracted at
different levels of a Convolutional Neural Network (CNN). Our model is composed
of three main blocks: a feature extraction CNN, a feature encoding network,
that weights low and high level feature maps, and a prior learning network. We
compare our solution with state of the art saliency models on two public
benchmarks datasets. Results show that our model outperforms under all
evaluation metrics on the SALICON dataset, which is currently the largest
public dataset for saliency prediction, and achieves competitive results on the
MIT300 benchmark.
Beyond Forward Shortcuts: Fully Convolutional Master-Slave Networks (MSNets) with Backward Skip Connections for Semantic Segmentation
Authors: Abrar H. Abdulnabi, Stefan Winkler, Gang Wang
Date: 2017-07-18 00:00:00
Tasks: Scene Parsing, Semantic Segmentation

Recent deep CNNs contain forward shortcut connections; i.e. skip connections
from low to high layers. Reusing features from lower layers that have higher
resolution (location information) benefit higher layers to recover lost details
and mitigate information degradation. However, during inference the lower
layers do not know about high layer features, although they contain contextual
high semantics that benefit low layers to adaptively extract informative
features for later layers. In this paper, we study the influence of backward
skip connections which are in the opposite direction to forward shortcuts, i.e.
paths from high layers to low layers. To achieve this -- which indeed runs
counter to the nature of feed-forward networks -- we propose a new fully
convolutional model that consists of a pair of networks. A `Slave' network is
dedicated to provide the backward connections from its top layers to the
`Master' network's bottom layers. The Master network is used to produce the
final label predictions. In our experiments we validate the proposed FCN model
on ADE20K (ImageNet scene parsing), PASCAL-Context, and PASCAL VOC 2011
datasets.
Bayesian Nonlinear Support Vector Machines for Big Data
Authors: Florian Wenzel, Theo Galy-Fajou, Matthaeus Deutsch, Marius Kloft
Date: 2017-07-18 00:00:00
Tasks: Variational Inference

We propose a fast inference method for Bayesian nonlinear support vector
machines that leverages stochastic variational inference and inducing points.
Our experiments show that the proposed method is faster than competing Bayesian
approaches and scales easily to millions of data points. It provides additional
features over frequentist competitors such as accurate predictive uncertainty
estimates and automatic hyperparameter search.
Cosmological model discrimination with Deep Learning
Authors: Jorit Schmelzle, Aurelien Lucchi, Tomasz Kacprzak, Adam Amara, Raphael Sgier, Alexandre Réfrégier, Thomas Hofmann
Date: 2017-07-17 00:00:00
Tasks: Deep Learning, model

We demonstrate the potential of Deep Learning methods for measurements of
cosmological parameters from density fields, focusing on the extraction of
non-Gaussian information. We consider weak lensing mass maps as our dataset. We
aim for our method to be able to distinguish between five models, which were
chosen to lie along the $\sigma_8$ - $\Omega_m$ degeneracy, and have nearly the
same two-point statistics. We design and implement a Deep Convolutional Neural
Network (DCNN) which learns the relation between five cosmological models and
the mass maps they generate. We develop a new training strategy which ensures
the good performance of the network for high levels of noise. We compare the
performance of this approach to commonly used non-Gaussian statistics, namely
the skewness and kurtosis of the convergence maps. We find that our
implementation of DCNN outperforms the skewness and kurtosis statistics,
especially for high noise levels. The network maintains the mean discrimination
efficiency greater than $85\%$ even for noise levels corresponding to ground
based lensing observations, while the other statistics perform worse in this
setting, achieving efficiency less than $70\%$. This demonstrates the ability
of CNN-based methods to efficiently break the $\sigma_8$ - $\Omega_m$
degeneracy with weak lensing mass maps alone. We discuss the potential of this
method to be applied to the analysis of real weak lensing data and other
datasets.
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks
Authors: Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu
Date: 2016-06-02 00:00:00
Tasks: Model Compression

Parallelization framework has become a necessity to speed up the training of
deep neural networks (DNN) recently. Such framework typically employs the Model
Average approach, denoted as MA-DNN, in which parallel workers conduct
respective training based on their own local data while the parameters of local
models are periodically communicated and averaged to obtain a global model
which serves as the new start of local models. However, since DNN is a highly
non-convex model, averaging parameters cannot ensure that such global model can
perform better than those local models. To tackle this problem, we introduce a
new parallel training framework called Ensemble-Compression, denoted as EC-DNN.
In this framework, we propose to aggregate the local models by ensemble, i.e.,
averaging the outputs of local models instead of the parameters. As most of
prevalent loss functions are convex to the output of DNN, the performance of
ensemble-based global model is guaranteed to be at least as good as the average
performance of local models. However, a big challenge lies in the explosion of
model size since each round of ensemble can give rise to multiple times size
increment. Thus, we carry out model compression after each ensemble,
specialized by a distillation based method in this paper, to reduce the size of
the global model to be the same as the local ones. Our experimental results
demonstrate the prominent advantage of EC-DNN over MA-DNN in terms of both
accuracy and speedup.
MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
Authors: Li-Chia Yang, Szu-Yu Chou, Yi-Hsuan Yang
Date: 2017-03-31 00:00:00
Tasks: Generative Adversarial Network, Music Generation

Most existing neural network models for music generation use recurrent neural
networks. However, the recent WaveNet model proposed by DeepMind shows that
convolutional neural networks (CNNs) can also generate realistic musical
waveforms in the audio domain. Following this light, we investigate using CNNs
for generating melody (a series of MIDI notes) one bar after another in the
symbolic domain. In addition to the generator, we use a discriminator to learn
the distributions of melodies, making it a generative adversarial network
(GAN). Moreover, we propose a novel conditional mechanism to exploit available
prior knowledge, so that the model can generate melodies either from scratch,
by following a chord sequence, or by conditioning on the melody of previous
bars (e.g. a priming melody), among other possibilities. The resulting model,
named MidiNet, can be expanded to generate music with multiple MIDI channels
(i.e. tracks). We conduct a user study to compare the melody of eight-bar long
generated by MidiNet and by Google's MelodyRNN models, each time using the same
priming melody. Result shows that MidiNet performs comparably with MelodyRNN
models in being realistic and pleasant to listen to, yet MidiNet's melodies are
reported to be much more interesting.
Interleaved Group Convolutions for Deep Neural Networks
Authors: Ting Zhang, Guo-Jun Qi, Bin Xiao, Jingdong Wang
Date: 2017-07-10 00:00:00

In this paper, we present a simple and modularized neural network
architecture, named interleaved group convolutional neural networks (IGCNets).
The main point lies in a novel building block, a pair of two successive
interleaved group convolutions: primary group convolution and secondary group
convolution. The two group convolutions are complementary: (i) the convolution
on each partition in primary group convolution is a spatial convolution, while
on each partition in secondary group convolution, the convolution is a
point-wise convolution; (ii) the channels in the same secondary partition come
from different primary partitions. We discuss one representative advantage:
Wider than a regular convolution with the number of parameters and the
computation complexity preserved. We also show that regular convolutions, group
convolution with summation fusion, and the Xception block are special cases of
interleaved group convolutions. Empirical results over standard benchmarks,
CIFAR-$10$, CIFAR-$100$, SVHN and ImageNet demonstrate that our networks are
more efficient in using parameters and computation complexity with similar or
higher accuracy.
W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis
Authors: Aitor García-Pablos, Montse Cuadros, German Rigau
Date: 2017-05-22 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), General Classification, Sentiment Analysis, Sentiment Classification

With the increase of online customer opinions in specialised websites and
social networks, the necessity of automatic systems to help to organise and
classify customer reviews by domain-specific aspect/categories and sentiment
polarity is more important than ever. Supervised approaches to Aspect Based
Sentiment Analysis obtain good results for the domain/language their are
trained on, but having manually labelled data for training supervised systems
for all domains and languages are usually very costly and time consuming. In
this work we describe W2VLDA, an almost unsupervised system based on topic
modelling, that combined with some other unsupervised methods and a minimal
configuration, performs aspect/category classifiation,
aspect-terms/opinion-words separation and sentiment polarity classification for
any given domain and language. We evaluate the performance of the aspect and
sentiment classification in the multilingual SemEval 2016 task 5 (ABSA)
dataset. We show competitive results for several languages (English, Spanish,
French and Dutch) and domains (hotels, restaurants, electronic-devices).
A Machine Learning Approach for Evaluating Creative Artifacts
Authors: Disha Shrivastava, Saneem Ahmed CG, Anirban Laha, Karthik Sankaranarayanan
Date: 2017-07-18 00:00:00
Tasks: BIG-bench Machine Learning

Much work has been done in understanding human creativity and defining
measures to evaluate creativity. This is necessary mainly for the reason of
having an objective and automatic way of quantifying creative artifacts. In
this work, we propose a regression-based learning framework which takes into
account quantitatively the essential criteria for creativity like novelty,
influence, value and unexpectedness. As it is often the case with most creative
domains, there is no clear ground truth available for creativity. Our proposed
learning framework is applicable to all creative domains; yet we evaluate it on
a dataset of movies created from IMDb and Rotten Tomatoes due to availability
of audience and critic scores, which can be used as proxy ground truth labels
for creativity. We report promising results and observations from our
experiments in the following ways : 1) Correlation of creative criteria with
critic scores, 2) Improvement in movie rating prediction with inclusion of
various creative criteria, and 3) Identification of creative movies.
Differentially Private Identity and Closeness Testing of Discrete Distributions
Authors: Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld
Date: 2017-07-18 00:00:00

We investigate the problems of identity and closeness testing over a discrete
population from random samples. Our goal is to develop efficient testers while
guaranteeing Differential Privacy to the individuals of the population. We
describe an approach that yields sample-efficient differentially private
testers for these problems. Our theoretical results show that there exist
private identity and closeness testers that are nearly as sample-efficient as
their non-private counterparts. We perform an experimental evaluation of our
algorithms on synthetic data. Our experiments illustrate that our private
testers achieve small type I and type II errors with sample size sublinear in
the domain size of the underlying distributions.
Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks
Authors: Vardaan Pahuja, Anirban Laha, Shachar Mirkin, Vikas Raykar, Lili Kotlerman, Guy Lev
Date: 2017-03-14 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The stream of words produced by Automatic Speech Recognition (ASR) systems is
typically devoid of punctuations and formatting. Most natural language
processing applications expect segmented and well-formatted texts as input,
which is not available in ASR output. This paper proposes a novel technique of
jointly modeling multiple correlated tasks such as punctuation and
capitalization using bidirectional recurrent neural networks, which leads to
improved performance for each of these tasks. This method could be extended for
joint modeling of any other correlated sequence labeling tasks.
Guided Co-training for Large-Scale Multi-View Spectral Clustering
Authors: Tyng-Luh Liu
Date: 2017-07-18 00:00:00
Tasks: Clustering

In many real-world applications, we have access to multiple views of the
data, each of which characterizes the data from a distinct aspect. Several
previous algorithms have demonstrated that one can achieve better clustering
accuracy by integrating information from all views appropriately than using
only an individual view. Owing to the effectiveness of spectral clustering,
many multi-view clustering methods are based on it. Unfortunately, they have
limited applicability to large-scale data due to the high computational
complexity of spectral clustering. In this work, we propose a novel multi-view
spectral clustering method for large-scale data. Our approach is structured
under the guided co-training scheme to fuse distinct views, and uses the
sampling technique to accelerate spectral clustering. More specifically, we
first select $p$ ($\ll n$) landmark points and then approximate the
eigen-decomposition accordingly. The augmented view, which is essential to
guided co-training process, can then be quickly determined by our method. The
proposed algorithm scales linearly with the number of given data. Extensive
experiments have been performed and the results support the advantage of our
method for handling the large-scale multi-view situation.
PunFields at SemEval-2017 Task 7: Employing Roget's Thesaurus in Automatic Pun Recognition and Interpretation
Authors: Elena Mikhalkova, Yuri Karyakin
Date: 2017-07-18 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Classification

The article describes a model of automatic interpretation of English puns,
based on Roget's Thesaurus, and its implementation, PunFields. In a pun, the
algorithm discovers two groups of words that belong to two main semantic
fields. The fields become a semantic vector based on which an SVM classifier
learns to recognize puns. A rule-based model is then applied for recognition of
intentionally ambiguous (target) words and their definitions. In SemEval Task 7
PunFields shows a considerably good result in pun classification, but requires
improvement in searching for the target word and its definition.
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
Authors: Shital Shah, Debadeepta Dey, Chris Lovett, Ashish Kapoor
Date: 2017-05-15 00:00:00
Tasks: Autonomous Vehicles

Developing and testing algorithms for autonomous vehicles in real world is an
expensive and time consuming process. Also, in order to utilize recent advances
in machine intelligence and deep learning we need to collect a large amount of
annotated training data in a variety of conditions and environments. We present
a new simulator built on Unreal Engine that offers physically and visually
realistic simulations for both of these goals. Our simulator includes a physics
engine that can operate at a high frequency for real-time hardware-in-the-loop
(HITL) simulations with support for popular protocols (e.g. MavLink). The
simulator is designed from the ground up to be extensible to accommodate new
types of vehicles, hardware platforms and software protocols. In addition, the
modular design enables various components to be easily usable independently in
other projects. We demonstrate the simulator by first implementing a quadrotor
as an autonomous vehicle and then experimentally comparing the software
components with real-world flights.