pwc-archive/papers-with-abstracts huggingface dataset:
Sketch-a-Net that Beats Humans
Authors: Qian Yu, Yongxin Yang, Yi-Zhe Song, Tao Xiang, Timothy Hospedales
Date: 2015-01-30 00:00:00
Tasks: Sketch Recognition

We propose a multi-scale multi-channel deep neural network framework that,
for the first time, yields sketch recognition performance surpassing that of
humans. Our superior performance is a result of explicitly embedding the unique
characteristics of sketches in our model: (i) a network architecture designed
for sketch rather than natural photo statistics, (ii) a multi-channel
generalisation that encodes sequential ordering in the sketching process, and
(iii) a multi-scale network ensemble with joint Bayesian fusion that accounts
for the different levels of abstraction exhibited in free-hand sketches. We
show that state-of-the-art deep networks specifically engineered for photos of
natural objects fail to perform well on sketch recognition, regardless whether
they are trained using photo or sketch. Our network on the other hand not only
delivers the best performance on the largest human sketch dataset to date, but
also is small in size making efficient training possible using just CPUs.
Kernel convolution model for decoding sounds from time-varying neural responses
Authors: Ali Faisal, Anni Nora, Jaeho Seol, Hanna Renvall, Riitta Salmelin
Date: 2015-07-21 00:00:00

In this study we present a kernel based convolution model to characterize
neural responses to natural sounds by decoding their time-varying acoustic
features. The model allows to decode natural sounds from high-dimensional
neural recordings, such as magnetoencephalography (MEG), that track timing and
location of human cortical signalling noninvasively across multiple channels.
We used the MEG responses recorded from subjects listening to acoustically
different environmental sounds. By decoding the stimulus frequencies from the
responses, our model was able to accurately distinguish between two different
sounds that it had never encountered before with 70% accuracy. Convolution
models typically decode frequencies that appear at a certain time point in the
sound signal by using neural responses from that time point until a certain
fixed duration of the response. Using our model, we evaluated several fixed
durations (time-lags) of the neural responses and observed auditory MEG
responses to be most sensitive to spectral content of the sounds at time-lags
of 250 ms to 500 ms. The proposed model should be useful for determining what
aspects of natural sounds are represented by high-dimensional neural responses
and may reveal novel properties of neural signals.
Data Representation using the Weyl Transform
Authors: Qiang Qiu, Andrew Thompson, Robert Calderbank, Guillermo Sapiro
Date: 2014-12-18 00:00:00
Tasks: General Classification, image-classification, Image Classification

The Weyl transform is introduced as a rich framework for data representation.
Transform coefficients are connected to the Walsh-Hadamard transform of
multiscale autocorrelations, and different forms of dyadic periodicity in a
signal are shown to appear as different features in its Weyl coefficients. The
Weyl transform has a high degree of symmetry with respect to a large group of
multiscale transformations, which allows compact yet discriminative
representations to be obtained by pooling coefficients. The effectiveness of
the Weyl transform is demonstrated through the example of textured image
classification.
Bandit-Based Task Assignment for Heterogeneous Crowdsourcing
Authors: Hao Zhang, Yao Ma, Masashi Sugiyama
Date: 2015-07-21 00:00:00
Tasks: Diversity

We consider a task assignment problem in crowdsourcing, which is aimed at
collecting as many reliable labels as possible within a limited budget. A
challenge in this scenario is how to cope with the diversity of tasks and the
task-dependent reliability of workers, e.g., a worker may be good at
recognizing the name of sports teams, but not be familiar with cosmetics
brands. We refer to this practical setting as heterogeneous crowdsourcing. In
this paper, we propose a contextual bandit formulation for task assignment in
heterogeneous crowdsourcing, which is able to deal with the
exploration-exploitation trade-off in worker selection. We also theoretically
investigate the regret bounds for the proposed method, and demonstrate its
practical usefulness experimentally.
Tri-Subject Kinship Verification: Understanding the Core of A Family
Authors: Xiaoqian Qin, Xiaoyang Tan, Songcan Chen
Date: 2015-01-12 00:00:00
Tasks: feature selection, Kinship Verification

One major challenge in computer vision is to go beyond the modeling of
individual objects and to investigate the bi- (one-versus-one) or tri-
(one-versus-two) relationship among multiple visual entities, answering such
questions as whether a child in a photo belongs to given parents. The
child-parents relationship plays a core role in a family and understanding such
kin relationship would have fundamental impact on the behavior of an artificial
intelligent agent working in the human world. In this work, we tackle the
problem of one-versus-two (tri-subject) kinship verification and our
contributions are three folds: 1) a novel relative symmetric bilinear model
(RSBM) introduced to model the similarity between the child and the parents, by
incorporating the prior knowledge that a child may resemble a particular parent
more than the other; 2) a spatially voted method for feature selection, which
jointly selects the most discriminative features for the child-parents pair,
while taking local spatial information into account; 3) a large scale
tri-subject kinship database characterized by over 1,000 child-parents
families. Extensive experiments on KinFaceW, Family101 and our newly released
kinship database show that the proposed method outperforms several previous
state of the art methods, while could also be used to significantly boost the
performance of one-versus-one kinship verification when the information about
both parents are available.
Gradient Importance Sampling
Authors: Ingmar Schuster
Date: 2015-07-21 00:00:00

Adaptive Monte Carlo schemes developed over the last years usually seek to
ensure ergodicity of the sampling process in line with MCMC tradition. This
poses constraints on what is possible in terms of adaptation. In the general
case ergodicity can only be guaranteed if adaptation is diminished at a certain
rate. Importance Sampling approaches offer a way to circumvent this limitation
and design sampling algorithms that keep adapting. Here I present a gradient
informed variant of SMC (and its special case Population Monte Carlo) for
static problems.
Understanding Intra-Class Knowledge Inside CNN
Authors: Donglai Wei, Bolei Zhou, Antonio Torrabla, William Freeman
Date: 2015-07-09 00:00:00
Tasks: Image Retrieval, Object, Retrieval

Convolutional Neural Network (CNN) has been successful in image recognition
tasks, and recent works shed lights on how CNN separates different classes with
the learned inter-class knowledge through visualization. In this work, we
instead visualize the intra-class knowledge inside CNN to better understand how
an object class is represented in the fully-connected layers.
  To invert the intra-class knowledge into more interpretable images, we
propose a non-parametric patch prior upon previous CNN visualization models.
With it, we show how different "styles" of templates for an object class are
organized by CNN in terms of location and content, and represented in a
hierarchical and ensemble way. Moreover, such intra-class knowledge can be used
in many interesting applications, e.g. style-based image retrieval and
style-based object completion.
Online Metric-Weighted Linear Representations for Robust Visual Tracking
Authors: Xi Li, Chunhua Shen, Anthony Dick, Zhongfei Zhang, Yueting Zhuang
Date: 2015-07-21 00:00:00
Tasks: Metric Learning, Object, Object Tracking, Visual Tracking

In this paper, we propose a visual tracker based on a metric-weighted linear
representation of appearance. In order to capture the interdependence of
different feature dimensions, we develop two online distance metric learning
methods using proximity comparison information and structured output learning.
The learned metric is then incorporated into a linear representation of
appearance.
  We show that online distance metric learning significantly improves the
robustness of the tracker, especially on those sequences exhibiting drastic
appearance changes. In order to bound growth in the number of training samples,
we design a time-weighted reservoir sampling method.
  Moreover, we enable our tracker to automatically perform object
identification during the process of object tracking, by introducing a
collection of static template samples belonging to several object classes of
interest. Object identification results for an entire video sequence are
achieved by systematically combining the tracking information and visual
recognition at each frame. Experimental results on challenging video sequences
demonstrate the effectiveness of the method for both inter-frame tracking and
object identification.
Rule Of Thumb: Deep derotation for improved fingertip detection
Authors: Aaron Wetzler, Ron Slossberg, Ron Kimmel
Date: 2015-07-21 00:00:00
Tasks: Fingertip Detection, General Classification, Hand Pose Estimation, Pose Estimation, regression

We investigate a novel global orientation regression approach for articulated
objects using a deep convolutional neural network. This is integrated with an
in-plane image derotation scheme, DeROT, to tackle the problem of per-frame
fingertip detection in depth images. The method reduces the complexity of
learning in the space of articulated poses which is demonstrated by using two
distinct state-of-the-art learning based hand pose estimation methods applied
to fingertip detection. Significant classification improvements are shown over
the baseline implementation. Our framework involves no tracking, kinematic
constraints or explicit prior model of the articulated object in hand. To
support our approach we also describe a new pipeline for high accuracy magnetic
annotation and labeling of objects imaged by a depth camera.
Gene expression modelling across multiple cell-lines with MapReduce
Authors: David M. Budden, Edmund J. Crampin
Date: 2015-07-21 00:00:00
Tasks: Computational Efficiency

With the wealth of high-throughput sequencing data generated by recent
large-scale consortia, predictive gene expression modelling has become an
important tool for integrative analysis of transcriptomic and epigenetic data.
However, sequencing data-sets are characteristically large, and previously
modelling frameworks are typically inefficient and unable to leverage
multi-core or distributed processing architectures. In this study, we detail an
efficient and parallelised MapReduce implementation of gene expression
modelling. We leverage the computational efficiency of this framework to
provide an integrative analysis of over fifty histone modification data-sets
across a variety of cancerous and non-cancerous cell-lines. Our results
demonstrate that the genome-wide relationships between histone modifications
and mRNA transcription are lineage, tissue and karyotype-invariant, and that
models trained on matched epigenetic/transcriptomic data from non-cancerous
cell-lines are able to predict cancerous expression with equivalent genome-wide
fidelity.
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
Authors: Baoguang Shi, Xiang Bai, Cong Yao
Date: 2015-07-21 00:00:00
Tasks: Optical Character Recognition (OCR), Scene Text Recognition

Image-based sequence recognition has been a long-standing research topic in
computer vision. In this paper, we investigate the problem of scene text
recognition, which is among the most important and challenging tasks in
image-based sequence recognition. A novel neural network architecture, which
integrates feature extraction, sequence modeling and transcription into a
unified framework, is proposed. Compared with previous systems for scene text
recognition, the proposed architecture possesses four distinctive properties:
(1) It is end-to-end trainable, in contrast to most of the existing algorithms
whose components are separately trained and tuned. (2) It naturally handles
sequences in arbitrary lengths, involving no character segmentation or
horizontal scale normalization. (3) It is not confined to any predefined
lexicon and achieves remarkable performances in both lexicon-free and
lexicon-based scene text recognition tasks. (4) It generates an effective yet
much smaller model, which is more practical for real-world application
scenarios. The experiments on standard benchmarks, including the IIIT-5K,
Street View Text and ICDAR datasets, demonstrate the superiority of the
proposed algorithm over the prior arts. Moreover, the proposed algorithm
performs well in the task of image-based music score recognition, which
evidently verifies the generality of it.
A neuromorphic hardware architecture using the Neural Engineering Framework for pattern recognition
Authors: Runchun Wang, Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson, Andre van Schaik
Date: 2015-07-21 00:00:00
Tasks: Handwriting Recognition, Handwritten Digit Recognition

We present a hardware architecture that uses the Neural Engineering Framework
(NEF) to implement large-scale neural networks on Field Programmable Gate
Arrays (FPGAs) for performing pattern recognition in real time. NEF is a
framework that is capable of synthesising large-scale cognitive systems from
subnetworks. We will first present the architecture of the proposed neural
network implemented using fixed-point numbers and demonstrate a routine that
computes the decoding weights by using the online pseudoinverse update method
(OPIUM) in a parallel and distributed manner. The proposed system is
efficiently implemented on a compact digital neural core. This neural core
consists of 64 neurons that are instantiated by a single physical neuron using
a time-multiplexing approach. As a proof of concept, we combined 128 identical
neural cores together to build a handwritten digit recognition system using the
MNIST database and achieved a recognition rate of 96.55%. The system is
implemented on a state-of-the-art FPGA and can process 5.12 million digits per
second. The architecture is not limited to handwriting recognition, but is
generally applicable as an extremely fast pattern recognition processor for
various kinds of patterns such as speech and images.
Multi-Objective Optimization for Self-Adjusting Weighted Gradient in Machine Learning Tasks
Authors: Conrado Silva Miranda, Fernando José Von Zuben
Date: 2015-06-03 00:00:00
Tasks: BIG-bench Machine Learning, Denoising

Much of the focus in machine learning research is placed in creating new
architectures and optimization methods, but the overall loss function is seldom
questioned. This paper interprets machine learning from a multi-objective
optimization perspective, showing the limitations of the default linear
combination of loss functions over a data set and introducing the hypervolume
indicator as an alternative. It is shown that the gradient of the hypervolume
is defined by a self-adjusting weighted mean of the individual loss gradients,
making it similar to the gradient of a weighted mean loss but without requiring
the weights to be defined a priori. This enables an inner boosting-like
behavior, where the current model is used to automatically place higher weights
on samples with higher losses but without requiring the use of multiple models.
Results on a denoising autoencoder show that the new formulation is able to
achieve better mean loss than the direct optimization of the mean loss,
providing evidence to the conjecture that self-adjusting the weights creates a
smoother loss surface.
Notes About a More Aware Dependency Parser
Authors: Matteo Grella
Date: 2015-07-20 00:00:00
Tasks: Dependency Parsing

In this paper I explain the reasons that led me to research and conceive a
novel technology for dependency parsing, mixing together the strengths of
data-driven transition-based and constraint-based approaches. In particular I
highlight the problem to infer the reliability of the results of a data-driven
transition-based parser, which is extremely important for high-level processes
that expect to use correct parsing results. I then briefly introduce a number
of notes about a new parser model I'm working on, capable to proceed with the
analysis in a "more aware" way, with a more "robust" concept of robustness.
Parallel Correlation Clustering on Big Graphs
Authors: Xinghao Pan, Dimitris Papailiopoulos, Samet Oymak, Benjamin Recht, Kannan Ramchandran, Michael. I. Jordan
Date: 2015-07-17 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Clustering

Given a similarity graph between items, correlation clustering (CC) groups
similar items together and dissimilar ones apart. One of the most popular CC
algorithms is KwikCluster: an algorithm that serially clusters neighborhoods of
vertices, and obtains a 3-approximation ratio. Unfortunately, KwikCluster in
practice requires a large number of clustering rounds, a potential bottleneck
for large graphs.
  We present C4 and ClusterWild!, two algorithms for parallel correlation
clustering that run in a polylogarithmic number of rounds and achieve nearly
linear speedups, provably. C4 uses concurrency control to enforce
serializability of a parallel clustering process, and guarantees a
3-approximation ratio. ClusterWild! is a coordination free algorithm that
abandons consistency for the benefit of better scaling; this leads to a
provably small loss in the 3-approximation ratio.
  We provide extensive experimental results for both algorithms, where we
outperform the state of the art, both in terms of clustering accuracy and
running time. We show that our algorithms can cluster billion-edge graphs in
under 5 seconds on 32 cores, while achieving a 15x speedup.
3D Pose from Detections
Authors: Cosimo Rubino, Marco Crocco, Alessandro Perina, Vittorio Murino, Alessio Del Bue
Date: 2015-02-17 00:00:00

We present a novel method to infer, in closed-form, a general 3D spatial
occupancy and orientation of a collection of rigid objects given 2D image
detections from a sequence of images. In particular, starting from 2D ellipses
fitted to bounding boxes, this novel multi-view problem can be reformulated as
the estimation of a quadric (ellipsoid) in 3D. We show that an efficient
solution exists in the dual-space using a minimum of three views while a
solution with two views is possible through the use of regularization. However,
this algebraic solution can be negatively affected in the presence of gross
inaccuracies in the bounding boxes estimation. To this end, we also propose a
robust ellipse fitting algorithm able to improve performance in the presence of
errors in the detected objects. Results on synthetic tests and on different
real datasets, involving real challenging scenarios, demonstrate the
applicability and potential of our method.
Subspace Alignment Based Domain Adaptation for RCNN Detector
Authors: Anant Raj, Vinay P. Namboodiri, Tinne Tuytelaars
Date: 2015-07-20 00:00:00
Tasks: Domain Adaptation, Object, object-detection, Object Detection, Unsupervised Domain Adaptation

In this paper, we propose subspace alignment based domain adaptation of the
state of the art RCNN based object detector. The aim is to be able to achieve
high quality object detection in novel, real world target scenarios without
requiring labels from the target domain. While, unsupervised domain adaptation
has been studied in the case of object classification, for object detection it
has been relatively unexplored. In subspace based domain adaptation for
objects, we need access to source and target subspaces for the bounding box
features. The absence of supervision (labels and bounding boxes are absent)
makes the task challenging. In this paper, we show that we can still adapt sub-
spaces that are localized to the object by obtaining detections from the RCNN
detector trained on source and applied on target. Then we form localized
subspaces from the detections and show that subspace alignment based adaptation
between these subspaces yields improved object detection. This evaluation is
done by considering challenging real world datasets of PASCAL VOC as source and
validation set of Microsoft COCO dataset as target for various categories.
How to Generate a Good Word Embedding?
Authors: Siwei Lai, Kang Liu, Liheng Xu, Jun Zhao
Date: 2015-07-20 00:00:00
Tasks: Word Embeddings

We analyze three critical components of word embedding training: the model,
the corpus, and the training parameters. We systematize existing
neural-network-based word embedding algorithms and compare them using the same
corpus. We evaluate each word embedding in three ways: analyzing its semantic
properties, using it as a feature for supervised tasks and using it to
initialize neural networks. We also provide several simple guidelines for
training word embeddings. First, we discover that corpus domain is more
important than corpus size. We recommend choosing a corpus in a suitable domain
for the desired task, after that, using a larger corpus yields better results.
Second, we find that faster models provide sufficient performance in most
cases, and more complex models can be used if the training corpus is
sufficiently large. Third, the early stopping metric for iterating should rely
on the development set of the desired task rather than the validation loss of
training embedding.
Community detection in multiplex networks using locally adaptive random walks
Authors: Zhana Kuncheva, Giovanni Montana
Date: 2015-07-06 00:00:00
Tasks: Community Detection

Multiplex networks, a special type of multilayer networks, are increasingly
applied in many domains ranging from social media analytics to biology. A
common task in these applications concerns the detection of community
structures. Many existing algorithms for community detection in multiplexes
attempt to detect communities which are shared by all layers. In this article
we propose a community detection algorithm, LART (Locally Adaptive Random
Transitions), for the detection of communities that are shared by either some
or all the layers in the multiplex. The algorithm is based on a random walk on
the multiplex, and the transition probabilities defining the random walk are
allowed to depend on the local topological similarity between layers at any
given node so as to facilitate the exploration of communities across layers.
Based on this random walk, a node dissimilarity measure is derived and nodes
are clustered based on this distance in a hierarchical fashion. We present
experimental results using networks simulated under various scenarios to
showcase the performance of LART in comparison to related community detection
algorithms.