pwc-archive/papers-with-abstracts huggingface dataset:
The Cityscapes Dataset for Semantic Urban Scene Understanding
Authors: Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, Bernt Schiele
Date: 2016-04-06 00:00:00
Proceeding: CVPR 2016 6
Tasks: object-detection, Object Detection, Scene Understanding

Visual understanding of complex urban street scenes is an enabling factor for
a wide range of applications. Object detection has benefited enormously from
large-scale datasets, especially in the context of deep learning. For semantic
urban scene understanding, however, no current dataset adequately captures the
complexity of real-world urban scenes.
  To address this, we introduce Cityscapes, a benchmark suite and large-scale
dataset to train and test approaches for pixel-level and instance-level
semantic labeling. Cityscapes is comprised of a large, diverse set of stereo
video sequences recorded in streets from 50 different cities. 5000 of these
images have high quality pixel-level annotations; 20000 additional images have
coarse annotations to enable methods that leverage large volumes of
weakly-labeled data. Crucially, our effort exceeds previous attempts in terms
of dataset size, annotation richness, scene variability, and complexity. Our
accompanying empirical study provides an in-depth analysis of the dataset
characteristics, as well as a performance evaluation of several
state-of-the-art approaches based on our benchmark.
3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching
Authors: Pasquale Coscia, Francesco A. N. Palmieri, Francesco Castaldo, Alberto Cavallo
Date: 2016-04-07 00:00:00
Tasks: Hand Pose Estimation, Pose Estimation

We present a novel appearance-based approach for pose estimation of a human
hand using the point clouds provided by the low-cost Microsoft Kinect sensor.
Both the free-hand case, in which the hand is isolated from the surrounding
environment, and the hand-object case, in which the different types of
interactions are classified, have been considered. The hand-object case is
clearly the most challenging task having to deal with multiple tracks. The
approach proposed here belongs to the class of partial pose estimation where
the estimated pose in a frame is used for the initialization of the next one.
The pose estimation is obtained by applying a modified version of the Iterative
Closest Point (ICP) algorithm to synthetic models to obtain the rigid
transformation that aligns each model with respect to the input data. The
proposed framework uses a "pure" point cloud as provided by the Kinect sensor
without any other information such as RGB values or normal vector components.
For this reason, the proposed method can also be applied to data obtained from
other types of depth sensor, or RGB-D camera.
Neural Architectures for Named Entity Recognition
Authors: Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, Chris Dyer
Date: 2016-03-04 00:00:00
Proceeding: NAACL 2016 6
Tasks: Named Entity Recognition, Named Entity Recognition (NER)

State-of-the-art named entity recognition systems rely heavily on
hand-crafted features and domain-specific knowledge in order to learn
effectively from the small, supervised training corpora that are available. In
this paper, we introduce two new neural architectures---one based on
bidirectional LSTMs and conditional random fields, and the other that
constructs and labels segments using a transition-based approach inspired by
shift-reduce parsers. Our models rely on two sources of information about
words: character-based word representations learned from the supervised corpus
and unsupervised word representations learned from unannotated corpora. Our
models obtain state-of-the-art performance in NER in four languages without
resorting to any language-specific knowledge or resources such as gazetteers.
LocNet: Improving Localization Accuracy for Object Detection
Authors: Spyros Gidaris, Nikos Komodakis
Date: 2015-11-24 00:00:00
Proceeding: CVPR 2016 6
Tasks: Object, object-detection, Object Detection, Object Localization

We propose a novel object localization methodology with the purpose of
boosting the localization accuracy of state-of-the-art object detection
systems. Our model, given a search region, aims at returning the bounding box
of an object of interest inside this region. To accomplish its goal, it relies
on assigning conditional probabilities to each row and column of this region,
where these probabilities provide useful information regarding the location of
the boundaries of the object inside the search region and allow the accurate
inference of the object bounding box under a simple probabilistic framework.
  For implementing our localization model, we make use of a convolutional
neural network architecture that is properly adapted for this task, called
LocNet. We show experimentally that LocNet achieves a very significant
improvement on the mAP for high IoU thresholds on PASCAL VOC2007 test set and
that it can be very easily coupled with recent state-of-the-art object
detection systems, helping them to boost their performance. Finally, we
demonstrate that our detection approach can achieve high detection accuracy
even when it is given as input a set of sliding windows, thus proving that it
is independent of box proposal methods.
Edge Detection Based Shape Identification
Authors: Vivek Kumar, Sumit Pandey, Amrindra Pal, Sandeep Sharma
Date: 2016-04-07 00:00:00
Tasks: Edge Detection, Object, Object Recognition

Image recognition is the need of the hour. In order to be able to recognize
an image, it is of immense importance that the image should be distinguishable
from the background. In the present work, an approach is presented for
automatic detection and recognition of regular 2D shapes in low noise
environments. The work has a large number of direct applications in the real
world. The algorithm proposed is based on locating the edges and thus, in turn
calculating the area of the object helps in identification of a specified
shape. The results were simulated using MATLAB tool are encouraging and
validate the proposed algorithm.
  Index Terms: Edge Detection, Area Calculation, Shape Detection, Object
Recognition
Longitudinal Analysis of Discussion Topics in an Online Breast Cancer Community using Convolutional Neural Networks
Authors: Shaodian Zhang, Edouard Grave, Elizabeth Sklar, Noemie Elhadad
Date: 2016-03-28 00:00:00
Tasks: General Classification, Topic Classification

Identifying topics of discussions in online health communities (OHC) is
critical to various applications, but can be difficult because topics of OHC
content are usually heterogeneous and domain-dependent. In this paper, we
provide a multi-class schema, an annotated dataset, and supervised classifiers
based on convolutional neural network (CNN) and other models for the task of
classifying discussion topics. We apply the CNN classifier to the most popular
breast cancer online community, and carry out a longitudinal analysis to show
topic distributions and topic changes throughout members' participation. Our
experimental results suggest that CNN outperforms other classifiers in the task
of topic classification, and that certain trajectories can be detected with
respect to topic changes.
On the uniform one-dimensional fragment
Authors: Antti Kuusisto
Date: 2016-04-06 00:00:00
Tasks: Survey

The uniform one-dimensional fragment of first-order logic, U1, is a formalism that extends two-variable logic in a natural way to contexts with relations of all arities. We survey properties of U1 and investigate its relationship to description logics designed to accommodate higher arity relations, with particular attention given to DLR_reg. We also define a description logic version of a variant of U1 and prove a range of new results concerning the expressivity of U1 and related logics.
An Analysis of General Fuzzy Logic and Fuzzy Reasoning Method
Authors: Kwak Son Il
Date: 2016-04-07 00:00:00

In this article, we describe the fuzzy logic, fuzzy language and algorithms
as the basis of fuzzy reasoning, one of the intelligent information processing
method, and then describe the general fuzzy reasoning method.
On the Complexity of Inner Product Similarity Join
Authors: Thomas D. Ahle, Rasmus Pagh, Ilya Razenshteyn, Francesco Silvestri
Date: 2015-10-09 00:00:00
Tasks: Information Retrieval, Recommendation Systems, Retrieval

A number of tasks in classification, information retrieval, recommendation
systems, and record linkage reduce to the core problem of inner product
similarity join (IPS join): identifying pairs of vectors in a collection that
have a sufficiently large inner product. IPS join is well understood when
vectors are normalized and some approximation of inner products is allowed.
However, the general case where vectors may have any length appears much more
challenging. Recently, new upper bounds based on asymmetric locality-sensitive
hashing (ALSH) and asymmetric embeddings have emerged, but little has been
known on the lower bound side. In this paper we initiate a systematic study of
inner product similarity join, showing new lower and upper bounds. Our main
results are:
  * Approximation hardness of IPS join in subquadratic time, assuming the
strong exponential time hypothesis.
  * New upper and lower bounds for (A)LSH-based algorithms. In particular, we
show that asymmetry can be avoided by relaxing the LSH definition to only
consider the collision probability of distinct elements.
  * A new indexing method for IPS based on linear sketches, implying that our
hardness results are not far from being tight.
  Our technical contributions include new asymmetric embeddings that may be of
independent interest. At the conceptual level we strive to provide greater
clarity, for example by distinguishing among signed and unsigned variants of
IPS join and shedding new light on the effect of asymmetry.
Monitoring Chinese Population Migration in Consecutive Weekly Basis from Intra-city scale to Inter-province scale by Didi's Bigdata
Authors: Renyu Zhao
Date: 2016-04-07 00:00:00

Population migration is valuable information which leads to proper decision
in urban-planning strategy, massive investment, and many other fields. For
instance, inter-city migration is a posterior evidence to see if the
government's constrain of population works, and inter-community immigration
might be a prior evidence of real estate price hike. With timely data, it is
also impossible to compare which city is more favorable for the people, suppose
the cities release different new regulations, we could also compare the
customers of different real estate development groups, where they come from,
where they probably will go. Unfortunately these data was not available.
  In this paper, leveraging the data generated by positioning team in Didi, we
propose a novel approach that timely monitoring population migration from
community scale to provincial scale. Migration can be detected as soon as in a
week. It could be faster, the setting of a week is for statistical purpose. A
monitoring system is developed, then applied nation wide in China, some
observations derived from the system will be presented in this paper.
  This new method of migration perception is origin from the insight that
nowadays people mostly moving with their personal Access Point (AP), also known
as WiFi hotspot. Assume that the ratio of AP moving to the migration of
population is constant, analysis of comparative population migration would be
feasible. More exact quantitative research would also be done with few sample
research and model regression.
  The procedures of processing data includes many steps: eliminating the impact
of pseudo-migration AP, for instance pocket WiFi, and second-hand traded
router; distinguishing moving of population with moving of companies;
identifying shifting of AP by the finger print clusters, etc..
Deep Online Convex Optimization with Gated Games
Authors: David Balduzzi
Date: 2016-04-07 00:00:00

Methods from convex optimization are widely used as building blocks for deep
learning algorithms. However, the reasons for their empirical success are
unclear, since modern convolutional networks (convnets), incorporating
rectifier units and max-pooling, are neither smooth nor convex. Standard
guarantees therefore do not apply. This paper provides the first convergence
rates for gradient descent on rectifier convnets. The proof utilizes the
particular structure of rectifier networks which consists in binary
active/inactive gates applied on top of an underlying linear network. The
approach generalizes to max-pooling, dropout and maxout. In other words, to
precisely the neural networks that perform best empirically. The key step is to
introduce gated games, an extension of convex games with similar convergence
properties that capture the gating function of rectifiers. The main result is
that rectifier convnets converge to a critical point at a rate controlled by
the gated-regret of the units in the network. Corollaries of the main result
include: (i) a game-theoretic description of the representations learned by a
neural network; (ii) a logarithmic-regret algorithm for training neural nets;
and (iii) a formal setting for analyzing conditional computation in neural nets
that can be applied to recently developed models of attention.
Optimizing Performance of Recurrent Neural Networks on GPUs
Authors: Jeremy Appleyard, Tomas Kocisky, Phil Blunsom
Date: 2016-04-07 00:00:00

As recurrent neural networks become larger and deeper, training times for
single networks are rising into weeks or even months. As such there is a
significant incentive to improve the performance and scalability of these
networks. While GPUs have become the hardware of choice for training and
deploying recurrent models, the implementations employed often make use of only
basic optimizations for these architectures. In this article we demonstrate
that by exposing parallelism between operations within the network, an order of
magnitude speedup across a range of network sizes can be achieved over a naive
implementation. We describe three stages of optimization that have been
incorporated into the fifth release of NVIDIA's cuDNN: firstly optimizing a
single cell, secondly a single layer, and thirdly the entire network.
Relaxed Leverage Sampling for Low-rank Matrix Completion
Authors: Abhisek Kundu
Date: 2015-03-22 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion

We consider the problem of exact recovery of any $m\times n$ matrix of rank
$\varrho$ from a small number of observed entries via the standard nuclear norm
minimization framework. Such low-rank matrices have degrees of freedom
$(m+n)\varrho - \varrho^2$. We show that any arbitrary low-rank matrices can be
recovered exactly from a $\Theta\left(((m+n)\varrho -
\varrho^2)\log^2(m+n)\right)$ randomly sampled entries, thus matching the lower
bound on the required number of entries (in terms of degrees of freedom), with
an additional factor of $O(\log^2(m+n))$. To achieve this bound on sample size
we observe each entry with probabilities proportional to the sum of
corresponding row and column leverage scores, minus their product. We show that
this relaxation in sampling probabilities (as opposed to sum of leverage scores
in Chen et al, 2014) can give us an $O(\varrho^2\log^2(m+n))$ additive
improvement on the (best known) sample size obtained by Chen et al, 2014, for
the nuclear norm minimization. Experiments on real data corroborate the
theoretical improvement on sample size. Further, exact recovery of $(a)$
incoherent matrices (with restricted leverage scores), and $(b)$ matrices with
only one of the row or column spaces to be incoherent, can be performed using
our relaxed leverage score sampling, via nuclear norm minimization, without
knowing the leverage scores a priori. In such settings also we can achieve
improvement on sample size.
A Novel Scene Text Detection Algorithm Based On Convolutional Neural Network
Authors: Xiaohang Ren, Kai Chen, Jun Sun
Date: 2016-04-07 00:00:00
Tasks: Scene Text Detection, Text Detection

Candidate text region extraction plays a critical role in convolutional
neural network (CNN) based text detection from natural images. In this paper,
we propose a CNN based scene text detection algorithm with a new text region
extractor. The so called candidate text region extractor I-MSER is based on
Maximally Stable Extremal Region (MSER), which can improve the independency and
completeness of the extracted candidate text regions. Design of I-MSER is
motivated by the observation that text MSERs have high similarity and are close
to each other. The independency of candidate text regions obtained by I-MSER is
guaranteed by selecting the most representative regions from a MSER tree which
is generated according to the spatial overlapping relationship among the MSERs.
A multi-layer CNN model is trained to score the confidence value of the
extracted regions extracted by the I-MSER for text detection. The new text
detection algorithm based on I-MSER is evaluated with wide-used ICDAR 2011 and
2013 datasets and shows improved detection performance compared to the existing
algorithms.
Generative Concatenative Nets Jointly Learn to Write and Classify Reviews
Authors: Zachary C. Lipton, Sharad Vikram, Julian McAuley
Date: 2015-11-11 00:00:00
Tasks: Negation

A recommender system's basic task is to estimate how users will respond to
unseen items. This is typically modeled in terms of how a user might rate a
product, but here we aim to extend such approaches to model how a user would
write about the product. To do so, we design a character-level Recurrent Neural
Network (RNN) that generates personalized product reviews. The network
convincingly learns styles and opinions of nearly 1000 distinct authors, using
a large corpus of reviews from BeerAdvocate.com. It also tailors reviews to
describe specific items, categories, and star ratings. Using a simple input
replication strategy, the Generative Concatenative Network (GCN) preserves the
signal of static auxiliary inputs across wide sequence intervals. Without any
additional training, the generative model can classify reviews, identifying the
author of the review, the product category, and the sentiment (rating), with
remarkable accuracy. Our evaluation shows the GCN captures complex dynamics in
text, such as the effect of negation, misspellings, slang, and large
vocabularies gracefully absent any machinery explicitly dedicated to the
purpose.
A CNN Based Scene Chinese Text Recognition Algorithm With Synthetic Data Engine
Authors: Xiaohang Ren, Kai Chen, Jun Sun
Date: 2016-04-07 00:00:00
Tasks: Scene Text Recognition

Scene text recognition plays an important role in many computer vision
applications. The small size of available public available scene text datasets
is the main challenge when training a text recognition CNN model. In this
paper, we propose a CNN based Chinese text recognition algorithm. To enlarge
the dataset for training the CNN model, we design a synthetic data engine for
Chinese scene character generation, which generates representative character
images according to the fonts use frequency of Chinese texts. As the Chinese
text is more complex, the English text recognition CNN architecture is modified
for Chinese text. To ensure the small size nature character dataset and the
large size artificial character dataset are comparable in training, the CNN
model are trained progressively. The proposed Chinese text recognition
algorithm is evaluated with two Chinese text datasets. The algorithm achieves
better recognize accuracy compared to the baseline methods.
Reinterpreting the Transformation Posterior in Probabilistic Image Registration
Authors: Jie Luo, Karteek Popuri, Dana Cobzas, Hongyi Ding, Masashi Sugiyama
Date: 2016-04-07 00:00:00
Tasks: Image Registration

Probabilistic image registration methods estimate the posterior distribution
of transformation. The conventional way of interpreting the transformation
posterior is to use the mode as the most likely transformation and assign its
corresponding intensity to the registered voxel. Meanwhile, summary statistics
of the posterior are employed to evaluate the registration uncertainty, that is
the trustworthiness of the registered image. Despite the wide acceptance, this
convention has never been justified. In this paper, based on illustrative
examples, we question the correctness and usefulness of conventional methods.
In order to faithfully translate the transformation posterior, we propose to
encode the variability of values into a novel data type called ensemble fields.
Ensemble fields can serve as a complement to the registered image and a
foundation for developing advanced methods to characterize the uncertainty in
registration-based tasks. We demonstrate the potential of ensemble fields by
pilot examples
A Survey on Bayesian Deep Learning
Authors: Hao Wang, Dit-yan Yeung
Date: 2016-04-06 00:00:00
Tasks: Deep Learning, Object Recognition, Recommendation Systems, speech-recognition, Speech Recognition, Survey, Topic Models

A comprehensive artificial intelligence system needs to not only perceive the environment with different `senses' (e.g., seeing and hearing) but also infer the world's conditional (or even causal) relations and corresponding uncertainty. The past decade has seen major advances in many perception tasks such as visual object recognition and speech recognition using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. In recent years, Bayesian deep learning has emerged as a unified probabilistic framework to tightly integrate deep learning and Bayesian models. In this general framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in turn, the feedback from the inference process is able to enhance the perception of text or images. This survey provides a comprehensive introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, control, etc. Besides, we also discuss the relationship and differences between Bayesian deep learning and other related topics such as Bayesian treatment of neural networks. For a constantly updating project page, please refer to https://github.com/js05212/BayesianDeepLearning-Survey.
Active Learning Algorithms for Graphical Model Selection
Authors: Gautam Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Hyuk Park
Date: 2016-02-01 00:00:00
Tasks: Active Learning, model, Model Selection

The problem of learning the structure of a high dimensional graphical model
from data has received considerable attention in recent years. In many
applications such as sensor networks and proteomics it is often expensive to
obtain samples from all the variables involved simultaneously. For instance,
this might involve the synchronization of a large number of sensors or the
tagging of a large number of proteins. To address this important issue, we
initiate the study of a novel graphical model selection problem, where the goal
is to optimize the total number of scalar samples obtained by allowing the
collection of samples from only subsets of the variables. We propose a general
paradigm for graphical model selection where feedback is used to guide the
sampling to high degree vertices, while obtaining only few samples from the
ones with the low degrees. We instantiate this framework with two specific
active learning algorithms, one of which makes mild assumptions but is
computationally expensive, while the other is more computationally efficient
but requires stronger (nevertheless standard) assumptions. Whereas the sample
complexity of passive algorithms is typically a function of the maximum degree
of the graph, we show that the sample complexity of our algorithms is provable
smaller and that it depends on a novel local complexity measure that is akin to
the average degree of the graph. We finally demonstrate the efficacy of our
framework via simulations.