pwc-archive/papers-with-abstracts huggingface dataset:
Learning Contextualized Music Semantics from Tags via a Siamese Network
Authors: Ubai Sandouk, Ke Chen
Date: 2015-04-29 00:00:00
Tasks: Information Retrieval, Music Information Retrieval, Retrieval, TAG, Topic Models

Music information retrieval faces a challenge in modeling contextualized
musical concepts formulated by a set of co-occurring tags. In this paper, we
investigate the suitability of our recently proposed approach based on a
Siamese neural network in fighting off this challenge. By means of tag features
and probabilistic topic models, the network captures contextualized semantics
from tags via unsupervised learning. This leads to a distributed semantics
space and a potential solution to the out of vocabulary problem which has yet
to be sufficiently addressed. We explore the nature of the resultant
music-based semantics and address computational needs. We conduct experiments
on three public music tag collections -namely, CAL500, MagTag5K and Million
Song Dataset- and compare our approach to a number of state-of-the-art
semantics learning approaches. Comparative results suggest that this approach
outperforms previous approaches in terms of semantic priming and music tag
completion.
Learning Mixtures of Plackett-Luce Models
Authors: Zhibing Zhao, Peter Piech, Lirong Xia
Date: 2016-03-23 00:00:00
Tasks: 2k

In this paper we address the identifiability and efficient learning problems of finite mixtures of Plackett-Luce models for rank data. We prove that for any $k\geq 2$, the mixture of $k$ Plackett-Luce models for no more than $2k-1$ alternatives is non-identifiable and this bound is tight for $k=2$. For generic identifiability, we prove that the mixture of $k$ Plackett-Luce models over $m$ alternatives is generically identifiable if $k\leq\lfloor\frac {m-2} 2\rfloor!$. We also propose an efficient generalized method of moments (GMM) algorithm to learn the mixture of two Plackett-Luce models and show that the algorithm is consistent. Our experiments show that our GMM algorithm is significantly faster than the EMM algorithm by Gormley and Murphy (2008), while achieving competitive statistical efficiency.
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
Authors: Xi Li, Liming Zhao, Lina Wei, Ming-Hsuan Yang, Fei Wu, Yueting Zhuang, Haibin Ling, Jingdong Wang
Date: 2015-10-19 00:00:00
Tasks: Image Segmentation, Multi-Task Learning, Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection, Semantic Segmentation

A key problem in salient object detection is how to effectively model the
semantic properties of salient objects in a data-driven manner. In this paper,
we propose a multi-task deep saliency model based on a fully convolutional
neural network (FCNN) with global input (whole raw images) and global output
(whole saliency maps). In principle, the proposed saliency model takes a
data-driven strategy for encoding the underlying saliency prior information,
and then sets up a multi-task learning scheme for exploring the intrinsic
correlations between saliency detection and semantic image segmentation.
Through collaborative feature learning from such two correlated tasks, the
shared fully convolutional layers produce effective features for object
perception. Moreover, it is capable of capturing the semantic information on
salient objects across different levels using the fully convolutional layers,
which investigate the feature-sharing properties of salient object detection
with great feature redundancy reduction. Finally, we present a graph Laplacian
regularized nonlinear regression model for saliency refinement. Experimental
results demonstrate the effectiveness of our approach in comparison with the
state-of-the-art approaches.
A Novel Approach for Phase Identification in Smart Grids Using Graph Theory and Principal Component Analysis
Authors: P Satya Jayadev, Aravind Rajeswaran, Nirav P Bhatt, Ramkrishna Pasumarthy
Date: 2015-11-19 00:00:00

Consumers with low demand, like households, are generally supplied
single-phase power by connecting their service mains to one of the phases of a
distribution transformer. The distribution companies face the problem of
keeping a record of consumer connectivity to a phase due to uninformed changes
that happen. The exact phase connectivity information is important for the
efficient operation and control of distribution system. We propose a new data
driven approach to the problem based on Principal Component Analysis (PCA) and
its Graph Theoretic interpretations, using energy measurements in equally timed
short intervals, generated from smart meters. We propose an algorithm for
inferring phase connectivity from noisy measurements. The algorithm is
demonstrated using simulated data for phase connectivities in distribution
networks.
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Authors: Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello
Date: 2016-06-07 00:00:00
Tasks: Efficient Neural Network, Real-Time Semantic Segmentation, Semantic Segmentation

The ability to perform pixel-wise semantic segmentation in real-time is of
paramount importance in mobile applications. Recent deep neural networks aimed
at this task have the disadvantage of requiring a large number of floating
point operations and have long run-times that hinder their usability. In this
paper, we propose a novel deep neural network architecture named ENet
(efficient neural network), created specifically for tasks requiring low
latency operation. ENet is up to 18$\times$ faster, requires 75$\times$ less
FLOPs, has 79$\times$ less parameters, and provides similar or better accuracy
to existing models. We have tested it on CamVid, Cityscapes and SUN datasets
and report on comparisons with existing state-of-the-art methods, and the
trade-offs between accuracy and processing time of a network. We present
performance measurements of the proposed architecture on embedded systems and
suggest possible software improvements that could make ENet even faster.
Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions
Authors: Keunwoo Choi, George Fazekas, Mark Sandler
Date: 2016-06-07 00:00:00

We introduce a novel playlist generation algorithm that focuses on the
quality of transitions using a recurrent neural network (RNN). The proposed
model assumes that optimal transitions between tracks can be modelled and
predicted by internal transitions within music tracks. We introduce modelling
sequences of high-level music descriptors using RNNs and discuss an experiment
involving different similarity functions, where the sequences are provided by a
musical structural analysis algorithm. Qualitative observations show that the
proposed approach can effectively model transitions of music tracks in
playlists.
Joint Recursive Monocular Filtering of Camera Motion and Disparity Map
Authors: Johannes Berger, Christoph Schnörr
Date: 2016-06-07 00:00:00
Tasks: Autonomous Driving

Monocular scene reconstruction is essential for modern applications such as
robotics or autonomous driving. Although stereo methods usually result in
better accuracy than monocular methods, they are more expensive and more
difficult to calibrate. In this work, we present a novel second order optimal
minimum energy filter that jointly estimates the camera motion, the disparity
map and also higher order kinematics recursively on a product Lie group
containing a novel disparity group. This mathematical framework enables to cope
with non-Euclidean state spaces, non-linear observations and high dimensions
which is infeasible for most classical filters. To be robust against outliers,
we use a generalized Charbonnier energy function in this framework rather than
a quadratic energy function as proposed in related work. Experiments confirm
that our method enables accurate reconstructions on-par with state-of-the-art.
Improving Hypernymy Detection with an Integrated Path-based and Distributional Method
Authors: Vered Shwartz, Yoav Goldberg, Ido Dagan
Date: 2016-03-19 00:00:00
Proceeding: ACL 2016 8

Detecting hypernymy relations is a key task in NLP, which is addressed in the
literature using two complementary approaches. Distributional methods, whose
supervised variants are the current best performers, and path-based methods,
which received less research attention. We suggest an improved path-based
algorithm, in which the dependency paths are encoded using a recurrent neural
network, that achieves results comparable to distributional methods. We then
extend the approach to integrate both path-based and distributional signals,
significantly improving upon the state-of-the-art on this task.
Regret Bounds for Non-decomposable Metrics with Missing Labels
Authors: Prateek Jain, Nagarajan Natarajan
Date: 2016-06-07 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Collaborative Filtering, Missing Labels

We consider the problem of recommending relevant labels (items) for a given
data point (user). In particular, we are interested in the practically
important setting where the evaluation is with respect to non-decomposable
(over labels) performance metrics like the $F_1$ measure, and the training data
has missing labels. To this end, we propose a generic framework that given a
performance metric $\Psi$, can devise a regularized objective function and a
threshold such that all the values in the predicted score vector above and only
above the threshold are selected to be positive. We show that the regret or
generalization error in the given metric $\Psi$ is bounded ultimately by
estimation error of certain underlying parameters. In particular, we derive
regret bounds under three popular settings: a) collaborative filtering, b)
multilabel classification, and c) PU (positive-unlabeled) learning. For each of
the above problems, we can obtain precise non-asymptotic regret bound which is
small even when a large fraction of labels is missing. Our empirical results on
synthetic and benchmark datasets demonstrate that by explicitly modeling for
missing labels and optimizing the desired performance metric, our algorithm
indeed achieves significantly better performance (like $F_1$ score) when
compared to methods that do not model missing label information carefully.
Pattern recognition on the quantum Bloch sphere
Authors: Giuseppe Sergioli, Enrica Santucci, Luca Didaci, Jaroslaw A. Miszczak, Roberto Giuntini
Date: 2016-03-01 00:00:00

We introduce a framework suitable for describing pattern recognition task
using the mathematical language of density matrices. In particular, we provide
a one-to-one correspondence between patterns and pure density operators. This
correspondence enables us to: i) represent the Nearest Mean Classifier (NMC) in
terms of quantum objects, ii) introduce a Quantum Classifier (QC). By comparing
the QC with the NMC on different 2D datasets, we show the first classifier can
provide additional information that are particularly beneficial on a classical
computer with respect to the second classifier.
Application of the Signature Method to Pattern Recognition in the CEQUEL Clinical Trial
Authors: A. B. Kormilitzin, K. E. A. Saunders, P. J. Harrison, J. R. Geddes, T. J. Lyons
Date: 2016-06-07 00:00:00
Tasks: Classification, General Classification

The classification procedure of streaming data usually requires various ad
hoc methods or particular heuristic models. We explore a novel non-parametric
and systematic approach to analysis of heterogeneous sequential data. We
demonstrate an application of this method to classification of the delays in
responding to the prompts, from subjects with bipolar disorder collected during
a clinical trial, using both synthetic and real examples. We show how this
method can provide a natural and systematic way to extract characteristic
features from sequential data.
Distributed Clustering of Linear Bandits in Peer to Peer Networks
Authors: Nathan Korda, Balazs Szorenyi, Shuai Li
Date: 2016-04-26 00:00:00
Tasks: Clustering

We provide two distributed confidence ball algorithms for solving linear
bandit problems in peer to peer networks with limited communication
capabilities. For the first, we assume that all the peers are solving the same
linear bandit problem, and prove that our algorithm achieves the optimal
asymptotic regret rate of any centralised algorithm that can instantly
communicate information between the peers. For the second, we assume that there
are clusters of peers solving the same bandit problem within each cluster, and
we prove that our algorithm discovers these clusters, while achieving the
optimal asymptotic regret rate within each one. Through experiments on several
real-world datasets, we demonstrate the performance of proposed algorithms
compared to the state-of-the-art.
Gradient Descent Only Converges to Minimizers: Non-Isolated Critical Points and Invariant Regions
Authors: Ioannis Panageas, Georgios Piliouras
Date: 2016-05-02 00:00:00
Tasks: Open-Ended Question Answering

Given a non-convex twice differentiable cost function f, we prove that the
set of initial conditions so that gradient descent converges to saddle points
where \nabla^2 f has at least one strictly negative eigenvalue has (Lebesgue)
measure zero, even for cost functions f with non-isolated critical points,
answering an open question in [Lee, Simchowitz, Jordan, Recht, COLT2016].
Moreover, this result extends to forward-invariant convex subspaces, allowing
for weak (non-globally Lipschitz) smoothness assumptions. Finally, we produce
an upper bound on the allowable step-size.
Enhanced high dynamic range 3D shape measurement based on generalized phase-shifting algorithm
Authors: Minmin Wang, Guangliang Du, Canlin Zhou, Chaorui Zhang, Shuchun Si, Hui Li, Zhenkun Lei, YanJie Li
Date: 2016-06-07 00:00:00
Tasks: Retrieval

It is a challenge for Phase Measurement Profilometry (PMP) to measure objects
with a large range of reflectivity variation across the surface. Saturated or
dark pixels in the deformed fringe patterns captured by the camera will lead to
phase fluctuations and errors. Jiang et al. proposed a high dynamic range
real-time 3D shape measurement method without changing camera exposures. Three
inverted phase-shifted fringe patterns are used to complement three regular
phase-shifted fringe patterns for phase retrieval when any of the regular
fringe patterns are saturated. But Jiang's method still has some drawbacks: (1)
The phases in saturated pixels are respectively estimated by different formulas
for different cases. It is shortage of an universal formula; (2) it cannot be
extended to four-step phase-shifting algorithm because inverted fringe patterns
are the repetition of regular fringe patterns; (3) only three unsaturated
intensity values at every pixel of fringe patterns are chosen for phase
demodulation, lying idle the other unsaturated ones. We proposed a method for
enhanced high dynamic range 3D shape measurement based on generalized
phase-shifting algorithm, which combines the complementary technique of
inverted and regular fringe patterns with generalized phase-shifting algorithm.
Firstly, two sets of complementary phase-shifted fringe patterns, namely
regular and inverted fringe patterns are projected and collected. Then all
unsaturated intensity values at the same camera pixel from two sets of fringe
patterns are selected, and employed to retrieve the phase by generalized
phase-shifting algorithm. Finally, simulations and experiments are conducted to
prove the validity of the proposed method. The results are analyzed and
compared with Jiang's method, which demonstrate that the proposed method not
only expands the scope of Jiang's method, but also improves the measurement
accuracy.
Sorting out symptoms: design and evaluation of the 'babylon check' automated triage system
Authors: Katherine Middleton, Mobasher Butt, Nils Hammerla, Steven Hamblin, Karan Mehta, Ali Parsa
Date: 2016-06-07 00:00:00

Prior to seeking professional medical care it is increasingly common for
patients to use online resources such as automated symptom checkers. Many such
systems attempt to provide a differential diagnosis based on the symptoms
elucidated from the user, which may lead to anxiety if life or limb-threatening
conditions are part of the list, a phenomenon termed 'cyberchondria' [1].
Systems that provide advice on where to seek help, rather than a diagnosis, are
equally popular, and in our view provide the most useful information. In this
technical report we describe how such a triage system can be modelled
computationally, how medical insights can be translated into triage flows, and
how such systems can be validated and tested. We present babylon check, our
commercially deployed automated triage system, as a case study, and illustrate
its performance in a large, semi-naturalistic deployment study.
Exploiting Lists of Names for Named Entity Identification of Financial Institutions from Unstructured Documents
Authors: Zheng Xu, Douglas Burdick, Louiqa Raschid
Date: 2016-02-14 00:00:00
Tasks: Entity Resolution, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

There is a wealth of information about financial systems that is embedded in
document collections. In this paper, we focus on a specialized text extraction
task for this domain. The objective is to extract mentions of names of
financial institutions, or FI names, from financial prospectus documents, and
to identify the corresponding real world entities, e.g., by matching against a
corpus of such entities. The tasks are Named Entity Recognition (NER) and
Entity Resolution (ER); both are well studied in the literature. Our
contribution is to develop a rule-based approach that will exploit lists of FI
names for both tasks; our solution is labeled Dict-based NER and Rank-based ER.
Since the FI names are typically represented by a root, and a suffix that
modifies the root, we use these lists of FI names to create specialized root
and suffix dictionaries. To evaluate the effectiveness of our specialized
solution for extracting FI names, we compare Dict-based NER with a general
purpose rule-based NER solution, ORG NER. Our evaluation highlights the
benefits and limitations of specialized versus general purpose approaches, and
presents additional suggestions for tuning and customization for FI name
extraction. To our knowledge, our proposed solutions, Dict-based NER and
Rank-based ER, and the root and suffix dictionaries, are the first attempt to
exploit specialized knowledge, i.e., lists of FI names, for rule-based NER and
ER.
Human vs. Computer Go: Review and Prospect
Authors: Chang-Shing Lee, Mei-Hui Wang, Shi-Jim Yen, Ting-Han Wei, I-Chen Wu, Ping-Chiang Chou, Chun-Hsun Chou, Ming-Wan Wang, Tai-Hsiung Yang
Date: 2016-06-07 00:00:00
Tasks: Game of Go

The Google DeepMind challenge match in March 2016 was a historic achievement
for computer Go development. This article discusses the development of
computational intelligence (CI) and its relative strength in comparison with
human intelligence for the game of Go. We first summarize the milestones
achieved for computer Go from 1998 to 2016. Then, the computer Go programs that
have participated in previous IEEE CIS competitions as well as methods and
techniques used in AlphaGo are briefly introduced. Commentaries from three
high-level professional Go players on the five AlphaGo versus Lee Sedol games
are also included. We conclude that AlphaGo beating Lee Sedol is a huge
achievement in artificial intelligence (AI) based largely on CI methods. In the
future, powerful computer Go programs such as AlphaGo are expected to be
instrumental in promoting Go education and AI real-world applications.
Hand Action Detection from Ego-centric Depth Sequences with Error-correcting Hough Transform
Authors: Chi Xu, Lakshmi Narasimhan Govindarajan, Li Cheng
Date: 2016-06-07 00:00:00
Tasks: Action Detection, Action Recognition, Temporal Action Localization

Detecting hand actions from ego-centric depth sequences is a practically
challenging problem, owing mostly to the complex and dexterous nature of hand
articulations as well as non-stationary camera motion. We address this problem
via a Hough transform based approach coupled with a discriminatively learned
error-correcting component to tackle the well known issue of incorrect votes
from the Hough transform. In this framework, local parts vote collectively for
the start $\&$ end positions of each action over time. We also construct an
in-house annotated dataset of 300 long videos, containing 3,177 single-action
subsequences over 16 action classes collected from 26 individuals. Our system
is empirically evaluated on this real-life dataset for both the action
recognition and detection tasks, and is shown to produce satisfactory results.
To facilitate reproduction, the new dataset and our implementation are also
provided online.
Towards stability and optimality in stochastic gradient descent
Authors: Panos Toulis, Dustin Tran, Edoardo M. Airoldi
Date: 2015-05-10 00:00:00
Tasks: parameter estimation

Iterative procedures for parameter estimation based on stochastic gradient
descent allow the estimation to scale to massive data sets. However, in both
theory and practice, they suffer from numerical instability. Moreover, they are
statistically inefficient as estimators of the true parameter value. To address
these two issues, we propose a new iterative procedure termed averaged implicit
SGD (AI-SGD). For statistical efficiency, AI-SGD employs averaging of the
iterates, which achieves the optimal Cram\'{e}r-Rao bound under strong
convexity, i.e., it is an optimal unbiased estimator of the true parameter
value. For numerical stability, AI-SGD employs an implicit update at each
iteration, which is related to proximal operators in optimization. In practice,
AI-SGD achieves competitive performance with other state-of-the-art procedures.
Furthermore, it is more stable than averaging procedures that do not employ
proximal updates, and is simple to implement as it requires fewer tunable
hyperparameters than procedures that do employ proximal updates.
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
Authors: Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille
Date: 2014-12-22 00:00:00
Tasks: GPU, image-classification, Image Segmentation, object-detection, Segmentation, Semantic Segmentation

Deep Convolutional Neural Networks (DCNNs) have recently shown state of the
art performance in high level vision tasks, such as image classification and
object detection. This work brings together methods from DCNNs and
probabilistic graphical models for addressing the task of pixel-level
classification (also called "semantic image segmentation"). We show that
responses at the final layer of DCNNs are not sufficiently localized for
accurate object segmentation. This is due to the very invariance properties
that make DCNNs good for high level tasks. We overcome this poor localization
property of deep networks by combining the responses at the final DCNN layer
with a fully connected Conditional Random Field (CRF). Qualitatively, our
"DeepLab" system is able to localize segment boundaries at a level of accuracy
which is beyond previous methods. Quantitatively, our method sets the new
state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching
71.6% IOU accuracy in the test set. We show how these results can be obtained
efficiently: Careful network re-purposing and a novel application of the 'hole'
algorithm from the wavelet community allow dense computation of neural net
responses at 8 frames per second on a modern GPU.