pwc-archive/papers-with-abstracts huggingface dataset:
Latent Constrained Correlation Filters for Object Localization
Authors: Shangzhen Luan, Baochang Zhang, Jungong Han, Chen Chen, Ling Shao, Alessandro Perina, Linlin Shen
Date: 2016-06-07 00:00:00
Tasks: Object, Object Localization

There is a neglected fact in the traditional machine learning methods that
the data sampling can actually lead to the solution sampling. We consider this
observation to be important because having the solution sampling available
makes the variable distribution estimation, which is a problem in many
learning-related applications, more tractable. In this paper, we implement this
idea on correlation filter, which has attracted much attention in the past few
years due to its high performance with a low computational cost. More
specifically, we propose a new method, named latent constrained correlation
filters (LCCF) by mapping the correlation filters to a given latent subspace,
in which we establish a new learning framework that embeds distribution-related
constraints into the original problem. We further introduce a subspace based
alternating direction method of multipliers (SADMM) to efficiently solve the
optimization problem, which is proved to converge at the saddle point. Our
approach is successfully applied to two different tasks inclduing eye
localization and car detection. Extensive experiments demonstrate that LCCF
outperforms the state-of-the-art methods when samples are suffered from noise
and occlusion.
Discrete Gyrator Transforms: Computational Algorithms and Applications
Authors: Soo-Chang Pei, Shih-Gu Huang, Jian-Jiun Ding
Date: 2017-06-03 00:00:00

As an extension of the 2D fractional Fourier transform (FRFT) and a special
case of the 2D linear canonical transform (LCT), the gyrator transform was
introduced to produce rotations in twisted space/spatial-frequency planes. It
is a useful tool in optics, signal processing and image processing. In this
paper, we develop discrete gyrator transforms (DGTs) based on the 2D LCT.
Taking the advantage of the additivity property of the 2D LCT, we propose three
kinds of DGTs, each of which is a cascade of low-complexity operators. These
DGTs have different constraints, characteristics, and properties, and are
realized by different computational algorithms. Besides, we propose a kind of
DGT based on the eigenfunctions of the gyrator transform. This DGT is an
orthonormal transform, and thus its comprehensive properties, especially the
additivity property, make it more useful in many applications. We also develop
an efficient computational algorithm to significantly reduce the complexity of
this DGT. At the end, a brief review of some important applications of the DGTs
is presented, including mode conversion, sampling and reconstruction,
watermarking, and image encryption.
Learning by Association - A versatile semi-supervised training method for neural networks
Authors: Philip Häusser, Alexander Mordvintsev, Daniel Cremers
Date: 2017-06-03 00:00:00

In many real-world scenarios, labeled data for a specific machine learning
task is costly to obtain. Semi-supervised training methods make use of
abundantly available unlabeled data and a smaller number of labeled examples.
We propose a new framework for semi-supervised training of deep neural networks
inspired by learning in humans. "Associations" are made from embeddings of
labeled samples to those of unlabeled ones and back. The optimization schedule
encourages correct association cycles that end up at the same class from which
the association was started and penalizes wrong associations ending at a
different class. The implementation is easy to use and can be added to any
existing end-to-end training setup. We demonstrate the capabilities of learning
by association on several data sets and show that it can improve performance on
classification tasks tremendously by making use of additionally available
unlabeled data. In particular, for cases with few labeled data, our training
scheme outperforms the current state of the art on SVHN.
SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient
Authors: Lam M. Nguyen, Jie Liu, Katya Scheinberg, Martin Takáč
Date: 2017-03-01 00:00:00
Proceeding: ICML 2017 8
Tasks: BIG-bench Machine Learning

In this paper, we propose a StochAstic Recursive grAdient algoritHm (SARAH),
as well as its practical variant SARAH+, as a novel approach to the finite-sum
minimization problems. Different from the vanilla SGD and other modern
stochastic methods such as SVRG, S2GD, SAG and SAGA, SARAH admits a simple
recursive framework for updating stochastic gradient estimates; when comparing
to SAG/SAGA, SARAH does not require a storage of past gradients. The linear
convergence rate of SARAH is proven under strong convexity assumption. We also
prove a linear convergence rate (in the strongly convex case) for an inner loop
of SARAH, the property that SVRG does not possess. Numerical experiments
demonstrate the efficiency of our algorithm.
A Simplified Description of Fuzzy TOPSIS
Authors: Balwinder Sodhi, Prabhakar T. V
Date: 2012-05-23 00:00:00
Tasks: Decision Making

A simplified description of Fuzzy TOPSIS (Technique for Order Preference by
Similarity to Ideal Situation) is presented. We have adapted the TOPSIS
description from existing Fuzzy theory literature and distilled the bare
minimum concepts required for understanding and applying TOPSIS. An example has
been worked out to illustrate the application of TOPSIS for a multi-criteria
group decision making scenario.
Learning Person Trajectory Representations for Team Activity Analysis
Authors: Nazanin Mehrasa, Yatao Zhong, Frederick Tung, Luke Bornn, Greg Mori
Date: 2017-06-03 00:00:00

Activity analysis in which multiple people interact across a large space is
challenging due to the interplay of individual actions and collective group
dynamics. We propose an end-to-end approach for learning person trajectory
representations for group activity analysis. The learned representations encode
rich spatio-temporal dependencies and capture useful motion patterns for
recognizing individual events, as well as characteristic group dynamics that
can be used to identify groups from their trajectories alone. We develop our
deep learning approach in the context of team sports, which provide
well-defined sets of events (e.g. pass, shot) and groups of people (teams).
Analysis of events and team formations using NHL hockey and NBA basketball
datasets demonstrate the generality of our approach.
Spectrum-based deep neural networks for fraud detection
Authors: Shuhan Yuan, Xintao Wu, Jun Li, Aidong Lu
Date: 2017-06-03 00:00:00
Tasks: Fraud Detection

In this paper, we focus on fraud detection on a signed graph with only a
small set of labeled training data. We propose a novel framework that combines
deep neural networks and spectral graph analysis. In particular, we use the
node projection (called as spectral coordinate) in the low dimensional spectral
space of the graph's adjacency matrix as input of deep neural networks.
Spectral coordinates in the spectral space capture the most useful topology
information of the network. Due to the small dimension of spectral coordinates
(compared with the dimension of the adjacency matrix derived from a graph),
training deep neural networks becomes feasible. We develop and evaluate two
neural networks, deep autoencoder and convolutional neural network, in our
fraud detection framework. Experimental results on a real signed graph show
that our spectrum based deep neural networks are effective in fraud detection.
Wikipedia Vandal Early Detection: from User Behavior to User Embedding
Authors: Shuhan Yuan, Panpan Zheng, Xintao Wu, Yang Xiang
Date: 2017-06-03 00:00:00
Tasks: Articles

Wikipedia is the largest online encyclopedia that allows anyone to edit
articles. In this paper, we propose the use of deep learning to detect vandals
based on their edit history. In particular, we develop a multi-source
long-short term memory network (M-LSTM) to model user behaviors by using a
variety of user edit aspects as inputs, including the history of edit reversion
information, edit page titles and categories. With M-LSTM, we can encode each
user into a low dimensional real vector, called user embedding. Meanwhile, as a
sequential model, M-LSTM updates the user embedding each time after the user
commits a new edit. Thus, we can predict whether a user is benign or vandal
dynamically based on the up-to-date user embedding. Furthermore, those user
embeddings are crucial to discover collaborative vandals.
Task-specific Word Identification from Short Texts Using a Convolutional Neural Network
Authors: Shuhan Yuan, Xintao Wu, Yang Xiang
Date: 2017-06-03 00:00:00

Task-specific word identification aims to choose the task-related words that
best describe a short text. Existing approaches require well-defined seed words
or lexical dictionaries (e.g., WordNet), which are often unavailable for many
applications such as social discrimination detection and fake review detection.
However, we often have a set of labeled short texts where each short text has a
task-related class label, e.g., discriminatory or non-discriminatory, specified
by users or learned by classification algorithms. In this paper, we focus on
identifying task-specific words and phrases from short texts by exploiting
their class labels rather than using seed words or lexical dictionaries. We
consider the task-specific word and phrase identification as feature learning.
We train a convolutional neural network over a set of labeled texts and use
score vectors to localize the task-specific words and phrases. Experimental
results on sentiment word identification show that our approach significantly
outperforms existing methods. We further conduct two case studies to show the
effectiveness of our approach. One case study on a crawled tweets dataset
demonstrates that our approach can successfully capture the
discrimination-related words/phrases. The other case study on fake review
detection shows that our approach can identify the fake-review words/phrases.
MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU
Authors: Qingqing Cao, Niranjan Balasubramanian, Aruna Balasubramanian
Date: 2017-06-03 00:00:00
Tasks: Activity Recognition, GPU, Machine Translation, Translation

In this paper, we explore optimizations to run Recurrent Neural Network (RNN)
models locally on mobile devices. RNN models are widely used for Natural
Language Processing, Machine Translation, and other tasks. However, existing
mobile applications that use RNN models do so on the cloud. To address privacy
and efficiency concerns, we show how RNN models can be run locally on mobile
devices. Existing work on porting deep learning models to mobile devices focus
on Convolution Neural Networks (CNNs) and cannot be applied directly to RNN
models. In response, we present MobiRNN, a mobile-specific optimization
framework that implements GPU offloading specifically for mobile GPUs.
Evaluations using an RNN model for activity recognition shows that MobiRNN does
significantly decrease the latency of running RNN models on phones.
Testing Quantum Models of Conjunction Fallacy on the World Wide Web
Authors: Diederik Aerts, Jonito Aerts Arguëlles, Lester Beltran, Lyneth Beltran, Massimiliano Sassoli de Bianchi, Sandro Sozzo, Tomas Veloz
Date: 2016-09-25 00:00:00

The 'conjunction fallacy' has been extensively debated by scholars in
cognitive science and, in recent times, the discussion has been enriched by the
proposal of modeling the fallacy using the quantum formalism. Two major quantum
approaches have been put forward: the first assumes that respondents use a
two-step sequential reasoning and that the fallacy results from the presence of
'question order effects'; the second assumes that respondents evaluate the
cognitive situation as a whole and that the fallacy results from the 'emergence
of new meanings', as an 'effect of overextension' in the conceptual
conjunction. Thus, the question arises as to determine whether and to what
extent conjunction fallacies would result from 'order effects' or, instead,
from 'emergence effects'. To help clarify this situation, we propose to use the
World Wide Web as an 'information space' that can be interrogated both in a
sequential and non-sequential way, to test these two quantum approaches. We
find that 'emergence effects', and not 'order effects', should be considered
the main cognitive mechanism producing the observed conjunction fallacies.
Multiple Kernel Learning and Automatic Subspace Relevance Determination for High-dimensional Neuroimaging Data
Authors: Murat Seckin Ayhan, Vijay Raghavan, Alzheimer's Disease Neuroimaging Initiative
Date: 2017-06-02 00:00:00
Tasks: Gaussian Processes, General Classification, Hippocampus

Alzheimer's disease is a major cause of dementia. Its diagnosis requires
accurate biomarkers that are sensitive to disease stages. In this respect, we
regard probabilistic classification as a method of designing a probabilistic
biomarker for disease staging. Probabilistic biomarkers naturally support the
interpretation of decisions and evaluation of uncertainty associated with them.
In this paper, we obtain probabilistic biomarkers via Gaussian Processes.
Gaussian Processes enable probabilistic kernel machines that offer flexible
means to accomplish Multiple Kernel Learning. Exploiting this flexibility, we
propose a new variation of Automatic Relevance Determination and tackle the
challenges of high dimensionality through multiple kernels. Our research
results demonstrate that the Gaussian Process models are competitive with or
better than the well-known Support Vector Machine in terms of classification
performance even in the cases of single kernel learning. Extending the basic
scheme towards the Multiple Kernel Learning, we improve the efficacy of the
Gaussian Process models and their interpretability in terms of the known
anatomical correlates of the disease. For instance, the disease pathology
starts in and around the hippocampus and entorhinal cortex. Through the use of
Gaussian Processes and Multiple Kernel Learning, we have automatically and
efficiently determined those portions of neuroimaging data. In addition to
their interpretability, our Gaussian Process models are competitive with recent
deep learning solutions under similar settings.
Sparse canonical correlation analysis
Authors: Xiaotong Suo, Victor Minden, Bradley Nelson, Robert Tibshirani, Michael Saunders
Date: 2017-05-30 00:00:00

Canonical correlation analysis was proposed by Hotelling [6] and it measures
linear relationship between two multidimensional variables. In high dimensional
setting, the classical canonical correlation analysis breaks down. We propose a
sparse canonical correlation analysis by adding l1 constraints on the canonical
vectors and show how to solve it efficiently using linearized alternating
direction method of multipliers (ADMM) and using TFOCS as a black box. We
illustrate this idea on simulated data.
CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
Authors: Xiang Ren, Zeqiu Wu, Wenqi He, Meng Qu, Clare R. Voss, Heng Ji, Tarek F. Abdelzaher, Jiawei Han
Date: 2016-10-27 00:00:00
Tasks: Joint Entity and Relation Extraction, Relation, Relation Extraction, Text Segmentation

Extracting entities and relations for types of interest from text is
important for understanding massive text corpora. Traditionally, systems of
entity relation extraction have relied on human-annotated corpora for training
and adopted an incremental pipeline. Such systems require additional human
expertise to be ported to a new domain, and are vulnerable to errors cascading
down the pipeline. In this paper, we investigate joint extraction of typed
entities and relations with labeled data heuristically obtained from knowledge
bases (i.e., distant supervision). As our algorithm for type labeling via
distant supervision is context-agnostic, noisy training data poses unique
challenges for the task. We propose a novel domain-independent framework,
called CoType, that runs a data-driven text segmentation algorithm to extract
entity mentions, and jointly embeds entity mentions, relation mentions, text
features and type labels into two low-dimensional spaces (for entity and
relation mentions respectively), where, in each space, objects whose types are
close will also have similar representations. CoType, then using these learned
embeddings, estimates the types of test (unlinkable) mentions. We formulate a
joint optimization problem to learn embeddings from text corpora and knowledge
bases, adopting a novel partial-label loss function for noisy labeled data and
introducing an object "translation" function to capture the cross-constraints
of entities and relations on each other. Experiments on three public datasets
demonstrate the effectiveness of CoType across different domains (e.g., news,
biomedical), with an average of 25% improvement in F1 score compared to the
next best method.
Unsupervised Feature Selection Based on the Morisita Estimator of Intrinsic Dimension
Authors: Jean Golay, Mikhail Kanevski
Date: 2016-08-19 00:00:00
Tasks: Dimensionality Reduction, feature selection

This paper deals with a new filter algorithm for selecting the smallest
subset of features carrying all the information content of a data set (i.e. for
removing redundant features). It is an advanced version of the fractal
dimension reduction technique, and it relies on the recently introduced
Morisita estimator of Intrinsic Dimension (ID). Here, the ID is used to
quantify dependencies between subsets of features, which allows the effective
processing of highly non-linear data. The proposed algorithm is successfully
tested on simulated and real world case studies. Different levels of sample
size and noise are examined along with the variability of the results. In
addition, a comprehensive procedure based on random forests shows that the data
dimensionality is significantly reduced by the algorithm without loss of
relevant information. And finally, comparisons with benchmark feature selection
techniques demonstrate the promising performance of this new filter.
Information, Privacy and Stability in Adaptive Data Analysis
Authors: Adam Smith
Date: 2017-06-02 00:00:00
Tasks: valid

Traditional statistical theory assumes that the analysis to be performed on a
given data set is selected independently of the data themselves. This
assumption breaks downs when data are re-used across analyses and the analysis
to be performed at a given stage depends on the results of earlier stages. Such
dependency can arise when the same data are used by several scientific studies,
or when a single analysis consists of multiple stages.
  How can we draw statistically valid conclusions when data are re-used? This
is the focus of a recent and active line of work. At a high level, these
results show that limiting the information revealed by earlier stages of
analysis controls the bias introduced in later stages by adaptivity.
  Here we review some known results in this area and highlight the role of
information-theoretic concepts, notably several one-shot notions of mutual
information.
A watershed-based algorithm to segment and classify cells in fluorescence microscopy images
Authors: Lena R. Bartell, Lawrence J. Bonassar, Itai Cohen
Date: 2017-06-02 00:00:00

Imaging assays of cellular function, especially those using fluorescent
stains, are ubiquitous in the biological and medical sciences. Despite advances
in computer vision, such images are often analyzed using only manual or
rudimentary automated processes. Watershed-based segmentation is an effective
technique for identifying objects in images; it outperforms commonly used image
analysis methods, but requires familiarity with computer-vision techniques to
be applied successfully. In this report, we present and implement a
watershed-based image analysis and classification algorithm in a GUI, enabling
a broad set of users to easily understand the algorithm and adjust the
parameters to their specific needs. As an example, we implement this algorithm
to find and classify cells in a complex imaging assay for mitochondrial
function. In a second example, we demonstrate a workflow using manual
comparisons and receiver operator characteristics to optimize the algorithm
parameters for finding live and dead cells in a standard viability assay.
Overall, this watershed-based algorithm is more advanced than traditional
thresholding and can produce optimized, automated results. By incorporating
associated pre-processing steps in the GUI, the algorithm is also easily
adjusted, rendering it user-friendly.
Zero-Shot Visual Recognition via Bidirectional Latent Embedding
Authors: Qian Wang, Ke Chen
Date: 2016-07-07 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Zero-Shot Learning

Zero-shot learning for visual recognition, e.g., object and action
recognition, has recently attracted a lot of attention. However, it still
remains challenging in bridging the semantic gap between visual features and
their underlying semantics and transferring knowledge to semantic categories
unseen during learning. Unlike most of the existing zero-shot visual
recognition methods, we propose a stagewise bidirectional latent embedding
framework to two subsequent learning stages for zero-shot visual recognition.
In the bottom-up stage, a latent embedding space is first created by exploring
the topological and labeling information underlying training data of known
classes via a proper supervised subspace learning algorithm and the latent
embedding of training data are used to form landmarks that guide embedding
semantics underlying unseen classes into this learned latent space. In the
top-down stage, semantic representations of unseen-class labels in a given
label vocabulary are then embedded to the same latent space to preserve the
semantic relatedness between all different classes via our proposed
semi-supervised Sammon mapping with the guidance of landmarks. Thus, the
resultant latent embedding space allows for predicting the label of a test
instance with a simple nearest-neighbor rule. To evaluate the effectiveness of
the proposed framework, we have conducted extensive experiments on four
benchmark datasets in object and action recognition, i.e., AwA, CUB-200-2011,
UCF101 and HMDB51. The experimental results under comparative studies
demonstrate that our proposed approach yields the state-of-the-art performance
under inductive and transductive settings.
Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor
Authors: Robin Ruede, Markus Müller, Sebastian Stüker, Alex Waibel
Date: 2017-06-02 00:00:00
Tasks: Deep Learning

Using supporting backchannel (BC) cues can make human-computer interaction
more social. BCs provide a feedback from the listener to the speaker indicating
to the speaker that he is still listened to. BCs can be expressed in different
ways, depending on the modality of the interaction, for example as gestures or
acoustic cues. In this work, we only considered acoustic cues. We are proposing
an approach towards detecting BC opportunities based on acoustic input features
like power and pitch. While other works in the field rely on the use of a
hand-written rule set or specialized features, we made use of artificial neural
networks. They are capable of deriving higher order features from input
features themselves. In our setup, we first used a fully connected feed-forward
network to establish an updated baseline in comparison to our previously
proposed setup. We also extended this setup by the use of Long Short-Term
Memory (LSTM) networks which have shown to outperform feed-forward based setups
on various tasks. Our best system achieved an F1-Score of 0.37 using power and
pitch features. Adding linguistic information using word2vec, the score
increased to 0.39.