pwc-archive/papers-with-abstracts huggingface dataset:
Embracing Error to Enable Rapid Crowdsourcing
Authors: Ranjay Krishna, Kenji Hata, Stephanie Chen, Joshua Kravitz, David A. Shamma, Li Fei-Fei, Michael S. Bernstein
Date: 2016-02-14 00:00:00
Tasks: General Classification, Sentiment Analysis, Topic Classification, Word Similarity

Microtask crowdsourcing has enabled dataset advances in social science and
machine learning, but existing crowdsourcing schemes are too expensive to scale
up with the expanding volume of data. To scale and widen the applicability of
crowdsourcing, we present a technique that produces extremely rapid judgments
for binary and categorical labels. Rather than punishing all errors, which
causes workers to proceed slowly and deliberately, our technique speeds up
workers' judgments to the point where errors are acceptable and even expected.
We demonstrate that it is possible to rectify these errors by randomizing task
order and modeling response latency. We evaluate our technique on a breadth of
common labeling tasks such as image verification, word similarity, sentiment
analysis and topic classification. Where prior work typically achieves a 0.25x
to 1x speedup over fixed majority vote, our approach often achieves an order of
magnitude (10x) speedup.
Can we still avoid automatic face detection?
Authors: Michael J. Wilber, Vitaly Shmatikov, Serge Belongie
Date: 2016-02-14 00:00:00
Tasks: Face Detection, Face Recognition

After decades of study, automatic face detection and recognition systems are now accurate and widespread. Naturally, this means users who wish to avoid automatic recognition are becoming less able to do so. Where do we stand in this cat-and-mouse race? We currently live in a society where everyone carries a camera in their pocket. Many people willfully upload most or all of the pictures they take to social networks which invest heavily in automatic face recognition systems. In this setting, is it still possible for privacy-conscientious users to avoid automatic face detection and recognition? If so, how? Must evasion techniques be obvious to be effective, or are there still simple measures that users can use to protect themselves? In this work, we find ways to evade face detection on Facebook, a representative example of a popular social network that uses automatic face detection to enhance their service. We challenge widely-held beliefs about evading face detection: do our old techniques such as blurring the face region or wearing "privacy glasses" still work? We show that in general, state-of-the-art detectors can often find faces even if the subject wears occluding clothing or even if the uploader damages the photo to prevent faces from being detected.
Do We Need Binary Features for 3D Reconstruction?
Authors: Bin Fan, Qingqun Kong, Wei Sui, Zhiheng Wang, Xinchao Wang, Shiming Xiang, Chunhong Pan, Pascal Fua
Date: 2016-02-14 00:00:00
Tasks: 3D Reconstruction

Binary features have been incrementally popular in the past few years due to
their low memory footprints and the efficient computation of Hamming distance
between binary descriptors. They have been shown with promising results on some
real time applications, e.g., SLAM, where the matching operations are relative
few. However, in computer vision, there are many applications such as 3D
reconstruction requiring lots of matching operations between local features.
Therefore, a natural question is that is the binary feature still a promising
solution to this kind of applications? To get the answer, this paper conducts a
comparative study of binary features and their matching methods on the context
of 3D reconstruction in a recently proposed large scale mutliview stereo
dataset. Our evaluations reveal that not all binary features are capable of
this task. Most of them are inferior to the classical SIFT based method in
terms of reconstruction accuracy and completeness with a not significant better
computational performance.
Convolutional Tables Ensemble: classification in microseconds
Authors: Aharon Bar-Hillel, Eyal Krupka, Noam Bloom
Date: 2016-02-14 00:00:00
Tasks: Classification, CPU, General Classification, Object, Object Recognition

We study classifiers operating under severe classification time constraints,
corresponding to 1-1000 CPU microseconds, using Convolutional Tables Ensemble
(CTE), an inherently fast architecture for object category recognition. The
architecture is based on convolutionally-applied sparse feature extraction,
using trees or ferns, and a linear voting layer. Several structure and
optimization variants are considered, including novel decision functions, tree
learning algorithm, and distillation from CNN to CTE architecture. Accuracy
improvements of 24-45% over related art of similar speed are demonstrated on
standard object recognition benchmarks. Using Pareto speed-accuracy curves, we
show that CTE can provide better accuracy than Convolutional Neural Networks
(CNN) for a certain range of classification time constraints, or alternatively
provide similar error rates with 5-200X speedup.
Learning Representations of Affect from Speech
Authors: Sayan Ghosh, Eugene Laksana, Louis-Philippe Morency, Stefan Scherer
Date: 2015-11-15 00:00:00
Tasks: Denoising, Emotion Classification, Representation Learning, speech-recognition, Speech Recognition

There has been a lot of prior work on representation learning for speech
recognition applications, but not much emphasis has been given to an
investigation of effective representations of affect from speech, where the
paralinguistic elements of speech are separated out from the verbal content. In
this paper, we explore denoising autoencoders for learning paralinguistic
attributes i.e. categorical and dimensional affective traits from speech. We
show that the representations learnt by the bottleneck layer of the autoencoder
are highly discriminative of activation intensity and at separating out
negative valence (sadness and anger) from positive valence (happiness). We
experiment with different input speech features (such as FFT and log-mel
spectrograms with temporal context windows), and different autoencoder
architectures (such as stacked and deep autoencoders). We also learn utterance
specific representations by a combination of denoising autoencoders and BLSTM
based recurrent autoencoders. Emotion classification is performed with the
learnt temporal/dynamic representations to evaluate the quality of the
representations. Experiments on a well-established real-life speech dataset
(IEMOCAP) show that the learnt representations are comparable to state of the
art feature extractors (such as voice quality features and MFCCs) and are
competitive with state-of-the-art approaches at emotion and dimensional affect
recognition.
Action Recognition using Visual Attention
Authors: Shikhar Sharma, Ryan Kiros, Ruslan Salakhutdinov
Date: 2015-11-12 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, Temporal Action Localization

We propose a soft attention based model for the task of action recognition in
videos. We use multi-layered Recurrent Neural Networks (RNNs) with Long
Short-Term Memory (LSTM) units which are deep both spatially and temporally.
Our model learns to focus selectively on parts of the video frames and
classifies videos after taking a few glimpses. The model essentially learns
which parts in the frames are relevant for the task at hand and attaches higher
importance to them. We evaluate the model on UCF-11 (YouTube Action), HMDB-51
and Hollywood2 datasets and analyze how the model focuses its attention
depending on the scene and the action being performed.
Large-Scale Reasoning with OWL
Authors: Michael Ruster
Date: 2016-02-14 00:00:00

With the growth of the Semantic Web in size and importance, more and more
knowledge is stored in machine-readable formats such as the Web Ontology
Language OWL. This paper outlines common approaches for efficient reasoning on
large-scale data consisting of billions ($10^9$) of triples. Therefore, OWL and
its sublanguages, as well as forward and backward chaining techniques are
presented. The WebPIE reasoner is discussed in detail as an example for forward
chaining using MapReduce for materialisation. Moreover, the QueryPIE reasoner
is presented as a backward chaining/hybrid approach which uses query rewriting.
Furthermore, an overview on other reasoners is given such as OWLIM and TrOWL.
Distributed Training of Structured SVM
Authors: Ching-pei Lee, Kai-Wei Chang, Shyam Upadhyay, Dan Roth
Date: 2015-06-08 00:00:00
Tasks: Structured Prediction

Training structured prediction models is time-consuming. However, most
existing approaches only use a single machine, thus, the advantage of computing
power and the capacity for larger data sets of multiple machines have not been
exploited. In this work, we propose an efficient algorithm for distributedly
training structured support vector machines based on a distributed
block-coordinate descent method. Both theoretical and experimental results
indicate that our method is efficient.
Random Forest Based Approach for Concept Drift Handling
Authors: A. Zhukov, D. Sidorov, A. Foley
Date: 2016-02-14 00:00:00
Tasks: Ensemble Pruning, General Classification

Concept drift has potential in smart grid analysis because the socio-economic
behaviour of consumers is not governed by the laws of physics. Likewise there
are also applications in wind power forecasting. In this paper we present
decision tree ensemble classification method based on the Random Forest
algorithm for concept drift. The weighted majority voting ensemble aggregation
rule is employed based on the ideas of Accuracy Weighted Ensemble (AWE) method.
Base learner weight in our case is computed for each sample evaluation using
base learners accuracy and intrinsic proximity measure of Random Forest. Our
algorithm exploits both temporal weighting of samples and ensemble pruning as a
forgetting strategy. We present results of empirical comparison of our method
with original random forest with incorporated "replace-the-looser" forgetting
andother state-of-the-art concept-drfit classifiers like AWE2.
Frequency Analysis of Temporal Graph Signals
Authors: Andreas Loukas, Damien Foucard
Date: 2016-02-14 00:00:00

This letter extends the concept of graph-frequency to graph signals that
evolve with time. Our goal is to generalize and, in fact, unify the familiar
concepts from time- and graph-frequency analysis. To this end, we study a joint
temporal and graph Fourier transform (JFT) and demonstrate its attractive
properties. We build on our results to create filters which act on the joint
(temporal and graph) frequency domain, and show how these can be used to
perform interference cancellation. The proposed algorithms are distributed,
have linear complexity, and can approximate any desired joint filtering
objective.
Hi Detector, What's Wrong with that Object? Identifying Irregular Object From Images by Modelling the Detection Score Distribution
Authors: Peng Wang, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel, Heng Tao Shen
Date: 2016-02-14 00:00:00
Tasks: Gaussian Processes, Object

In this work, we study the challenging problem of identifying the irregular
status of objects from images in an "open world" setting, that is,
distinguishing the irregular status of an object category from its regular
status as well as objects from other categories in the absence of "irregular
object" training data. To address this problem, we propose a novel approach by
inspecting the distribution of the detection scores at multiple image regions
based on the detector trained from the "regular object" and "other objects".
The key observation motivating our approach is that for "regular object" images
as well as "other objects" images, the region-level scores follow their own
essential patterns in terms of both the score values and the spatial
distributions while the detection scores obtained from an "irregular object"
image tend to break these patterns. To model this distribution, we propose to
use Gaussian Processes (GP) to construct two separate generative models for the
case of the "regular object" and the "other objects". More specifically, we
design a new covariance function to simultaneously model the detection score at
a single region and the score dependencies at multiple regions. We finally
demonstrate the superior performance of our method on a large dataset newly
proposed in this paper.
Convolutional neural networks with low-rank regularization
Authors: Cheng Tai, Tong Xiao, Yi Zhang, Xiaogang Wang, Weinan E
Date: 2015-11-19 00:00:00
Tasks: Data Augmentation, Tensor Decomposition

Large CNNs have delivered impressive performance in various computer vision
applications. But the storage and computation requirements make it problematic
for deploying these models on mobile devices. Recently, tensor decompositions
have been used for speeding up CNNs. In this paper, we further develop the
tensor decomposition technique. We propose a new algorithm for computing the
low-rank tensor decomposition for removing the redundancy in the convolution
kernels. The algorithm finds the exact global optimizer of the decomposition
and is more effective than iterative methods. Based on the decomposition, we
further propose a new method for training low-rank constrained CNNs from
scratch. Interestingly, while achieving a significant speedup, sometimes the
low-rank constrained CNNs delivers significantly better performance than their
non-constrained counterparts. On the CIFAR-10 dataset, the proposed low-rank
NIN model achieves $91.31\%$ accuracy (without data augmentation), which also
improves upon state-of-the-art result. We evaluated the proposed method on
CIFAR-10 and ILSVRC12 datasets for a variety of modern CNNs, including AlexNet,
NIN, VGG and GoogleNet with success. For example, the forward time of VGG-16 is
reduced by half while the performance is still comparable. Empirical success
suggests that low-rank tensor decompositions can be a very useful tool for
speeding up large CNNs.
Convex Optimization For Non-Convex Problems via Column Generation
Authors: Julian Yarkony, Kamalika Chaudhuri
Date: 2016-02-14 00:00:00

We apply column generation to approximating complex structured objects via a
set of primitive structured objects under either the cross entropy or L2 loss.
We use L1 regularization to encourage the use of few structured primitive
objects. We attack approximation using convex optimization over an infinite
number of variables each corresponding to a primitive structured object that
are generated on demand by easy inference in the Lagrangian dual. We apply our
approach to producing low rank approximations to large 3-way tensors.
Communication Efficient Distributed Kernel Principal Component Analysis
Authors: Maria-Florina Balcan, YIngyu Liang, Le Song, David Woodruff, Bo Xie
Date: 2015-03-23 00:00:00

Kernel Principal Component Analysis (KPCA) is a key machine learning
algorithm for extracting nonlinear features from data. In the presence of a
large volume of high dimensional data collected in a distributed fashion, it
becomes very costly to communicate all of this data to a single data center and
then perform kernel PCA. Can we perform kernel PCA on the entire dataset in a
distributed and communication efficient fashion while maintaining provable and
strong guarantees in solution quality?
  In this paper, we give an affirmative answer to the question by developing a
communication efficient algorithm to perform kernel PCA in the distributed
setting. The algorithm is a clever combination of subspace embedding and
adaptive sampling techniques, and we show that the algorithm can take as input
an arbitrary configuration of distributed datasets, and compute a set of global
kernel principal components with relative error guarantees independent of the
dimension of the feature space or the total number of data points. In
particular, computing $k$ principal components with relative error $\epsilon$
over $s$ workers has communication cost $\tilde{O}(s \rho k/\epsilon+s
k^2/\epsilon^3)$ words, where $\rho$ is the average number of nonzero entries
in each data point. Furthermore, we experimented the algorithm with large-scale
real world datasets and showed that the algorithm produces a high quality
kernel PCA solution while using significantly less communication than
alternative approaches.
Approximate Subspace-Sparse Recovery with Corrupted Data via Constrained $\ell_1$-Minimization
Authors: Ehsan Elhamifar, Mahdi Soltanolkotabi, Shankar Sastry
Date: 2014-12-23 00:00:00
Tasks: Clustering

High-dimensional data often lie in low-dimensional subspaces corresponding to
different classes they belong to. Finding sparse representations of data points
in a dictionary built using the collection of data helps to uncover
low-dimensional subspaces and address problems such as clustering,
classification, subset selection and more. In this paper, we address the
problem of recovering sparse representations for noisy data points in a
dictionary whose columns correspond to corrupted data lying close to a union of
subspaces. We consider a constrained $\ell_1$-minimization and study conditions
under which the solution of the proposed optimization satisfies the approximate
subspace-sparse recovery condition. More specifically, we show that each noisy
data point, perturbed from a subspace by a noise of the magnitude of
$\varepsilon$, will be reconstructed using data points from the same subspace
with a small error of the order of $O(\varepsilon)$ and that the coefficients
corresponding to data points in other subspaces will be sufficiently small,
\ie, of the order of $O(\varepsilon)$. We do not impose any randomness
assumption on the arrangement of subspaces or distribution of data points in
each subspace. Our framework is based on a novel generalization of the
null-space property to the setting where data lie in multiple subspaces, the
number of data points in each subspace exceeds the dimension of the subspace,
and all data points are corrupted by noise. Moreover, assuming a random
distribution for data points, we further show that coefficients from the
desired support not only reconstruct a given point with high accuracy, but also
have sufficiently large values, \ie, of the order of $O(1)$.
Semantic Scan: Detecting Subtle, Spatially Localized Events in Text Streams
Authors: Abhinav Maurya, Kenton Murray, Yandong Liu, Chris Dyer, William W. Cohen, Daniel B. Neill
Date: 2016-02-13 00:00:00
Tasks: Event Detection

Early detection and precise characterization of emerging topics in text
streams can be highly useful in applications such as timely and targeted public
health interventions and discovering evolving regional business trends. Many
methods have been proposed for detecting emerging events in text streams using
topic modeling. However, these methods have numerous shortcomings that make
them unsuitable for rapid detection of locally emerging events on massive text
streams. In this paper, we describe Semantic Scan (SS) that has been developed
specifically to overcome these shortcomings in detecting new spatially compact
events in text streams.
  Semantic Scan integrates novel contrastive topic modeling with online
document assignment and principled likelihood ratio-based spatial scanning to
identify emerging events with unexpected patterns of keywords hidden in text
streams. This enables more timely and accurate detection and characterization
of anomalous, spatially localized emerging events. Semantic Scan does not
require manual intervention or labeled training data, and is robust to noise in
real-world text data since it identifies anomalous text patterns that occur in
a cluster of new documents rather than an anomaly in a single new document.
  We compare Semantic Scan to alternative state-of-the-art methods such as
Topics over Time, Online LDA, and Labeled LDA on two real-world tasks: (i) a
disease surveillance task monitoring free-text Emergency Department chief
complaints in Allegheny County, and (ii) an emerging business trend detection
task based on Yelp reviews. On both tasks, we find that Semantic Scan provides
significantly better event detection and characterization accuracy than
competing approaches, while providing up to an order of magnitude speedup.
BPCMont: Business Process Change Management Ontology
Authors: Muhammad Fahad
Date: 2016-02-13 00:00:00
Tasks: Management

Change management for evolving collaborative business process development is
crucial when the business logic, transections and workflow change due to
changes in business strategies or organizational and technical environment.
During the change implementation, business processes are analyzed and improved
ensuring that they capture the proposed change and they do not contain any
undesired functionalities or change side-effects. This paper presents Business
Process Change Management approach for the efficient and effective
implementation of change in the business process. The key technology behind our
approach is our proposed Business Process Change Management Ontology (BPCMont)
which is the main contribution of this paper. BPCMont, as a formalized change
specification, helps to revert BP into a consistent state in case of system
crash, intermediate conflicting stage or unauthorized change done, aid in
change traceability in the new and old versions of business processes, change
effects can be seen and estimated effectively, ease for Stakeholders to
validate and verify change implementation, etc.
Look, Listen and Learn - A Multimodal LSTM for Speaker Identification
Authors: Jimmy Ren, Yongtao Hu, Yu-Wing Tai, Chuan Wang, Li Xu, Wenxiu Sun, Qiong Yan
Date: 2016-02-13 00:00:00
Tasks: Speaker Identification

Speaker identification refers to the task of localizing the face of a person
who has the same identity as the ongoing voice in a video. This task not only
requires collective perception over both visual and auditory signals, the
robustness to handle severe quality degradations and unconstrained content
variations are also indispensable. In this paper, we describe a novel
multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies
both visual and auditory modalities from the beginning of each sequence input.
The key idea is to extend the conventional LSTM by not only sharing weights
across time steps, but also sharing weights across modalities. We show that
modeling the temporal dependency across face and voice can significantly
improve the robustness to content quality degradations and variations. We also
found that our multimodal LSTM is robustness to distractors, namely the
non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory
dataset and showed that our system outperforms the state-of-the-art systems in
speaker identification with lower false alarm rate and higher recognition
accuracy.
Machine olfaction using time scattering of sensor multiresolution graphs
Authors: Leonid Gugel, Yoel Shkolnisky, Shai Dekel
Date: 2016-02-13 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

In this paper we construct a learning architecture for high dimensional time
series sampled by sensor arrangements. Using a redundant wavelet decomposition
on a graph constructed over the sensor locations, our algorithm is able to
construct discriminative features that exploit the mutual information between
the sensors. The algorithm then applies scattering networks to the time series
graphs to create the feature space. We demonstrate our method on a machine
olfaction problem, where one needs to classify the gas type and the location
where it originates from data sampled by an array of sensors. Our experimental
results clearly demonstrate that our method outperforms classical machine
learning techniques used in previous studies.