pwc-archive/papers-with-abstracts huggingface dataset:
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks
Authors: Quanzeng You, Jiebo Luo, Hailin Jin, Jianchao Yang
Date: 2015-09-20 00:00:00
Tasks: Sentiment Analysis

Sentiment analysis of online user generated content is important for many
social media analytics tasks. Researchers have largely relied on textual
sentiment analysis to develop systems to predict political elections, measure
economic indicators, and so on. Recently, social media users are increasingly
using images and videos to express their opinions and share their experiences.
Sentiment analysis of such large scale visual content can help better extract
user sentiments toward events or topics, such as those in image tweets, so that
prediction of sentiment from visual content is complementary to textual
sentiment analysis. Motivated by the needs in leveraging large scale yet noisy
training data to solve the extremely challenging problem of image sentiment
analysis, we employ Convolutional Neural Networks (CNN). We first design a
suitable CNN architecture for image sentiment analysis. We obtain half a
million training samples by using a baseline sentiment algorithm to label
Flickr images. To make use of such noisy machine labeled data, we employ a
progressive strategy to fine-tune the deep network. Furthermore, we improve the
performance on Twitter images by inducing domain transfer with a small number
of manually labeled Twitter images. We have conducted extensive experiments on
manually labeled Twitter images. The results show that the proposed CNN can
achieve better performance in image sentiment analysis than competing
algorithms.
Image Retrieval Based on LBP Pyramidal Multiresolution using Reversible Watermarking
Authors: H. Ouahi, K. Afdel, M. Machkour
Date: 2015-09-20 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

In the medical field, images are increasingly used to facilitate diagnosis of
diseases. These images are stored in multimedia databases accompanied by doctor
s prescriptions and other information related to patients.Search for medical
images has become for clinical applications an essential tool to bring
effective aid in diagnosis. Content Based Image Retrieval (CBIR) is one of the
possible solutions to effectively manage these databases. Our contribution is
to define a relevant descriptor to retrieve images based on multiresolution
analysis of texture using Local Binary Pattern LBP. This descriptor once
calculated and information s relating to the patient; will be placed in the
image using the technique of reversible watermarking. Thereby, the image,
descriptor of its contents, the BFILE locator and patientrelated information
become a single entity, so even the administrator cannot have access to the
patient private data.
Deep Convolutional Features for Image Based Retrieval and Scene Categorization
Authors: Arsalan Mousavian, Jana Kosecka
Date: 2015-09-20 00:00:00
Tasks: Image Retrieval, Retrieval

Several recent approaches showed how the representations learned by
Convolutional Neural Networks can be repurposed for novel tasks. Most commonly
it has been shown that the activation features of the last fully connected
layers (fc7 or fc6) of the network, followed by a linear classifier outperform
the state-of-the-art on several recognition challenge datasets. Instead of
recognition, this paper focuses on the image retrieval problem and proposes a
examines alternative pooling strategies derived for CNN features. The presented
scheme uses the features maps from an earlier layer 5 of the CNN architecture,
which has been shown to preserve coarse spatial information and is semantically
meaningful. We examine several pooling strategies and demonstrate superior
performance on the image retrieval task (INRIA Holidays) at the fraction of the
computational cost, while using a relatively small memory requirements. In
addition to retrieval, we see similar efficiency gains on the SUN397 scene
categorization dataset, demonstrating wide applicability of this simple
strategy. We also introduce and evaluate a novel GeoPlaces5K dataset from
different geographical locations in the world for image retrieval that stresses
more dramatic changes in appearance and viewpoint.
Image Set Querying Based Localization
Authors: Lei Deng, Siyuan Huang, Yueqi Duan, Baohua Chen, Jie zhou
Date: 2015-09-20 00:00:00
Tasks: Image-Based Localization

Conventional single image based localization methods usually fail to localize
a querying image when there exist large variations between the querying image
and the pre-built scene. To address this, we propose an image-set querying
based localization approach. When the localization by a single image fails to
work, the system will ask the user to capture more auxiliary images. First, a
local 3D model is established for the querying image set. Then, the pose of the
querying image set is estimated by solving a nonlinear optimization problem,
which aims to match the local 3D model against the pre-built scene. Experiments
have shown the effectiveness and feasibility of the proposed approach.
Effective Approaches to Attention-based Neural Machine Translation
Authors: Minh-Thang Luong, Hieu Pham, Christopher D. Manning
Date: 2015-08-17 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Image-guided Story Ending Generation, Machine Translation, NMT, Sentence, Translation

An attentional mechanism has lately been used to improve neural machine
translation (NMT) by selectively focusing on parts of the source sentence
during translation. However, there has been little work exploring useful
architectures for attention-based NMT. This paper examines two simple and
effective classes of attentional mechanism: a global approach which always
attends to all source words and a local one that only looks at a subset of
source words at a time. We demonstrate the effectiveness of both approaches
over the WMT translation tasks between English and German in both directions.
With local attention, we achieve a significant gain of 5.0 BLEU points over
non-attentional systems which already incorporate known techniques such as
dropout. Our ensemble model using different attention architectures has
established a new state-of-the-art result in the WMT'15 English to German
translation task with 25.9 BLEU points, an improvement of 1.0 BLEU points over
the existing best system backed by NMT and an n-gram reranker.
Query-Answer Causality in Databases: Abductive Diagnosis and View-Updates
Authors: Babak Salimi, Leopoldo Bertossi
Date: 2015-06-13 00:00:00

Causality has been recently introduced in databases, to model, characterize
and possibly compute causes for query results (answers). Connections between
query causality and consistency-based diagnosis and database repairs (wrt.
integrity constrain violations) have been established in the literature. In
this work we establish connections between query causality and abductive
diagnosis and the view-update problem. The unveiled relationships allow us to
obtain new complexity results for query causality -the main focus of our work-
and also for the two other areas.
Lexical Normalisation of Twitter Data
Authors: Bilal Ahmed
Date: 2014-09-16 00:00:00

Twitter with over 500 million users globally, generates over 100,000 tweets
per minute . The 140 character limit per tweet, perhaps unintentionally,
encourages users to use shorthand notations and to strip spellings to their
bare minimum "syllables" or elisions e.g. "srsly". The analysis of twitter
messages which typically contain misspellings, elisions, and grammatical
errors, poses a challenge to established Natural Language Processing (NLP)
tools which are generally designed with the assumption that the data conforms
to the basic grammatical structure commonly used in English language. In order
to make sense of Twitter messages it is necessary to first transform them into
a canonical form, consistent with the dictionary or grammar. This process,
performed at the level of individual tokens ("words"), is called lexical
normalisation. This paper investigates various techniques for lexical
normalisation of Twitter data and presents the findings as the techniques are
applied to process raw data from Twitter.
Using Neural Networks for Click Prediction of Sponsored Search
Authors: Afroze Ibrahim Baqapuri, Ilya Trofimov
Date: 2014-12-20 00:00:00
Tasks: Click-Through Rate Prediction

Sponsored search is a multi-billion dollar industry and makes up a major
source of revenue for search engines (SE). click-through-rate (CTR) estimation
plays a crucial role for ads selection, and greatly affects the SE revenue,
advertiser traffic and user experience. We propose a novel architecture for
solving CTR prediction problem by combining artificial neural networks (ANN)
with decision trees. First we compare ANN with respect to other popular machine
learning models being used for this task. Then we go on to combine ANN with
MatrixNet (proprietary implementation of boosted trees) and evaluate the
performance of the system as a whole. The results show that our approach
provides significant improvement over existing models.
Face Photo Sketch Synthesis via Larger Patch and Multiresolution Spline
Authors: Xu Yang
Date: 2015-09-19 00:00:00

Face photo sketch synthesis has got some researchers' attention in recent
years because of its potential applications in digital entertainment and law
enforcement. Some patches based methods have been proposed to solve this
problem. These methods usually focus more on how to get a sketch patch for a
given photo patch than how to blend these generated patches. However, without
appropriately blending method, some jagged parts and mottled points will appear
in the entire face sketch. In order to get a smoother sketch, we propose a new
method to reduce such jagged parts and mottled points. In our system, we resort
to an existed method, which is Markov Random Fields (MRF), to train a crude
face sketch firstly. Then this crude sketch face sketch will be divided into
some larger patches again and retrained by Non-Negative Matrix Factorization
(NMF). At last, we use Multiresolution Spline and a blend trick named
full-coverage trick to blend these retrained patches. The experiment results
show that compared with some previous method, we can get a smoother face
sketch.
A Fuzzy MLP Approach for Non-linear Pattern Classification
Authors: Tirtharaj Dash, H. S. Behera
Date: 2015-09-19 00:00:00
Tasks: Classification, Decision Making, General Classification

In case of decision making problems, classification of pattern is a complex
and crucial task. Pattern classification using multilayer perceptron (MLP)
trained with back propagation learning becomes much complex with increase in
number of layers, number of nodes and number of epochs and ultimate increases
computational time [31]. In this paper, an attempt has been made to use fuzzy
MLP and its learning algorithm for pattern classification. The time and space
complexities of the algorithm have been analyzed. A training performance
comparison has been carried out between MLP and the proposed fuzzy-MLP model by
considering six cases. Results are noted against different learning rates
ranging from 0 to 1. A new performance evaluation factor 'convergence gain' has
been introduced. It is observed that the number of epochs drastically reduced
and performance increased compared to MLP. The average and minimum gain has
been found to be 93% and 75% respectively. The best gain is found to be 95% and
is obtained by setting the learning rate to 0.55.
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Authors: Xingjian Shi, Zhourong Chen, Hao Wang, Dit-yan Yeung, Wai-kin Wong, Wang-chun Woo
Date: 2015-06-13 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: BIG-bench Machine Learning, Video Prediction, Weather Forecasting

The goal of precipitation nowcasting is to predict the future rainfall
intensity in a local region over a relatively short period of time. Very few
previous studies have examined this crucial and challenging weather forecasting
problem from the machine learning perspective. In this paper, we formulate
precipitation nowcasting as a spatiotemporal sequence forecasting problem in
which both the input and the prediction target are spatiotemporal sequences. By
extending the fully connected LSTM (FC-LSTM) to have convolutional structures
in both the input-to-state and state-to-state transitions, we propose the
convolutional LSTM (ConvLSTM) and use it to build an end-to-end trainable model
for the precipitation nowcasting problem. Experiments show that our ConvLSTM
network captures spatiotemporal correlations better and consistently
outperforms FC-LSTM and the state-of-the-art operational ROVER algorithm for
precipitation nowcasting.
Exploiting Reduction Rules and Data Structures: Local Search for Minimum Vertex Cover in Massive Graphs
Authors: Yi Fan, Chengqian Li, Zongjie Ma, LjiLjana Brankovic, Vladimir Estivill-Castro, Abdul Sattar
Date: 2015-09-19 00:00:00

The Minimum Vertex Cover (MinVC) problem is a well-known NP-hard problem.
Recently there has been great interest in solving this problem on real-world
massive graphs. For such graphs, local search is a promising approach to
finding optimal or near-optimal solutions. In this paper we propose a local
search algorithm that exploits reduction rules and data structures to solve the
MinVC problem in such graphs. Experimental results on a wide range of real-word
massive graphs show that our algorithm finds better covers than
state-of-the-art local search algorithms for MinVC. Also we present interesting
results about the complexities of some well-known heuristics.
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
Authors: Ashesh Jain, Hema S. Koppula, Bharad Raghavan, Shane Soh, Ashutosh Saxena
Date: 2015-04-10 00:00:00
Proceeding: ICCV 2015 12

Advanced Driver Assistance Systems (ADAS) have made driving safer over the
last decade. They prepare vehicles for unsafe road conditions and alert drivers
if they perform a dangerous maneuver. However, many accidents are unavoidable
because by the time drivers are alerted, it is already too late. Anticipating
maneuvers beforehand can alert drivers before they perform the maneuver and
also give ADAS more time to avoid or prepare for the danger.
  In this work we anticipate driving maneuvers a few seconds before they occur.
For this purpose we equip a car with cameras and a computing device to capture
the driving context from both inside and outside of the car. We propose an
Autoregressive Input-Output HMM to model the contextual information alongwith
the maneuvers. We evaluate our approach on a diverse data set with 1180 miles
of natural freeway and city driving and show that we can anticipate maneuvers
3.5 seconds before they occur with over 80\% F1-score in real-time.
Similar Handwritten Chinese Character Discrimination by Weakly Supervised Learning
Authors: Zhibo Yang, Huanle Xu, Keda Fu, Yong Xia
Date: 2015-09-19 00:00:00
Tasks: Weakly-supervised Learning

Traditional approaches for handwritten Chinese character recognition suffer
in classifying similar characters. In this paper, we propose to discriminate
similar handwritten Chinese characters by using weakly supervised learning. Our
approach learns a discriminative SVM for each similar pair which simultaneously
localizes the discriminative region of similar character and makes the
classification. For the first time, similar handwritten Chinese character
recognition (SHCCR) is formulated as an optimization problem extended from SVM.
We also propose a novel feature descriptor, Gradient Context, and apply
bag-of-words model to represent regions with different scales. In our method,
we do not need to select a sized-fixed sub-window to differentiate similar
characters. The unconstrained property makes our method well adapted to high
variance in the size and position of discriminative regions in similar
handwritten Chinese characters. We evaluate our proposed approach over the
CASIA Chinese character data set and the results show that our method
outperforms the state of the art.
DenseBox: Unifying Landmark Localization with End to End Object Detection
Authors: Lichao Huang, Yi Yang, Yafeng Deng, Yinan Yu
Date: 2015-09-16 00:00:00
Tasks: Face Detection, Multi-Task Learning, Object, object-detection, Object Detection

How can a single fully convolutional neural network (FCN) perform on object
detection? We introduce DenseBox, a unified end-to-end FCN framework that
directly predicts bounding boxes and object class confidences through all
locations and scales of an image. Our contribution is two-fold. First, we show
that a single FCN, if designed and optimized carefully, can detect multiple
different objects extremely accurately and efficiently. Second, we show that
when incorporating with landmark localization during multi-task learning,
DenseBox further improves object detection accuray. We present experimental
results on public benchmark datasets including MALF face detection and KITTI
car detection, that indicate our DenseBox is the state-of-the-art system for
detecting challenging objects such as faces and cars.
Robobarista: Object Part based Transfer of Manipulation Trajectories from Crowd-sourcing in 3D Pointclouds
Authors: Jaeyong Sung, Seok Hyun Jin, Ashutosh Saxena
Date: 2015-04-13 00:00:00
Tasks: Structured Prediction

There is a large variety of objects and appliances in human environments,
such as stoves, coffee dispensers, juice extractors, and so on. It is
challenging for a roboticist to program a robot for each of these object types
and for each of their instantiations. In this work, we present a novel approach
to manipulation planning based on the idea that many household objects share
similarly-operated object parts. We formulate the manipulation planning as a
structured prediction problem and design a deep learning model that can handle
large noise in the manipulation demonstrations and learns features from three
different modalities: point-clouds, language and trajectory. In order to
collect a large number of manipulation demonstrations for different objects, we
developed a new crowd-sourcing platform called Robobarista. We test our model
on our dataset consisting of 116 objects with 249 parts along with 250 language
instructions, for which there are 1225 crowd-sourced manipulation
demonstrations. We further show that our robot can even manipulate objects it
has never seen before.
Word, graph and manifold embedding from Markov processes
Authors: Tatsunori B. Hashimoto, David Alvarez-Melis, Tommi S. Jaakkola
Date: 2015-09-18 00:00:00
Tasks: Dimensionality Reduction, Word Embeddings

Continuous vector representations of words and objects appear to carry
surprisingly rich semantic content. In this paper, we advance both the
conceptual and theoretical understanding of word embeddings in three ways.
First, we ground embeddings in semantic spaces studied in
cognitive-psychometric literature and introduce new evaluation tasks. Second,
in contrast to prior work, we take metric recovery as the key object of study,
unify existing algorithms as consistent metric recovery methods based on
co-occurrence counts from simple Markov random walks, and propose a new
recovery algorithm. Third, we generalize metric recovery to graphs and
manifolds, relating co-occurence counts on random walks in graphs and random
processes on manifolds to the underlying metric to be recovered, thereby
reconciling manifold estimation and embedding algorithms. We compare embedding
algorithms across a range of tasks, from nonlinear dimensionality reduction to
three semantic language tasks, including analogies, sequence completion, and
classification.
"Oddball SGD": Novelty Driven Stochastic Gradient Descent for Training Deep Neural Networks
Authors: Andrew J. R. Simpson
Date: 2015-09-18 00:00:00

Stochastic Gradient Descent (SGD) is arguably the most popular of the machine
learning methods applied to training deep neural networks (DNN) today. It has
recently been demonstrated that SGD can be statistically biased so that certain
elements of the training set are learned more rapidly than others. In this
article, we place SGD into a feedback loop whereby the probability of selection
is proportional to error magnitude. This provides a novelty-driven oddball SGD
process that learns more rapidly than traditional SGD by prioritising those
elements of the training set with the largest novelty (error). In our DNN
example, oddball SGD trains some 50x faster than regular SGD.
Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses
Authors: Carlo Baldassi, Alessandro Ingrosso, Carlo Lucibello, Luca Saglietti, Riccardo Zecchina
Date: 2015-09-18 00:00:00

We show that discrete synaptic weights can be efficiently used for learning
in large scale neural systems, and lead to unanticipated computational
performance. We focus on the representative case of learning random patterns
with binary synapses in single layer networks. The standard statistical
analysis shows that this problem is exponentially dominated by isolated
solutions that are extremely hard to find algorithmically. Here, we introduce a
novel method that allows us to find analytical evidence for the existence of
subdominant and extremely dense regions of solutions. Numerical experiments
confirm these findings. We also show that the dense regions are surprisingly
accessible by simple learning protocols, and that these synaptic configurations
are robust to perturbations and generalize better than typical solutions. These
outcomes extend to synapses with multiple states and to deeper neural
architectures. The large deviation measure also suggests how to design novel
algorithmic schemes for optimization based on local entropy maximization.
Evaluation of Protein-protein Interaction Predictors with Noisy Partially Labeled Data Sets
Authors: Haohan Wang, Madhavi K. Ganapathiraju
Date: 2015-09-18 00:00:00

Protein-protein interaction (PPI) prediction is an important problem in
machine learning and computational biology. However, there is no data set for
training or evaluation purposes, where all the instances are accurately
labeled. Instead, what is available are instances of positive class (with
possibly noisy labels) and no instances of negative class. The non-availability
of negative class data is typically handled with the observation that randomly
chosen protein-pairs have a nearly 100% chance of being negative class, as only
1 in 1,500 protein pairs expected is expected to be an interacting pair. In
this paper, we focused on the problem that non-availability of accurately
labeled testing data sets in the domain of protein-protein interaction (PPI)
prediction may lead to biased evaluation results. We first showed that not
acknowledging the inherent skew in the interactome (i.e. rare occurrence of
positive instances) leads to an over-estimated accuracy of the predictor. Then
we show that, with the belief that positive interactions are a rare category,
sampling random pairs of proteins excluding known interacting proteins set as
the negative testing data set could lead to an under-estimated evaluation
result. We formalized those two problems to validate the above claim, and based
on the formalization, we proposed a balancing method to cancel out the
over-estimation with under-estimation. Finally, our experiments validated the
theoretical aspects and showed that this balancing evaluation could evaluate
the exact performance without availability of golden standard data sets.