pwc-archive/papers-with-abstracts huggingface dataset:
ROCKET: Robust Confidence Intervals via Kendall's Tau for Transelliptical Graphical Models
Authors: Rina Foygel Barber, Mladen Kolar
Date: 2015-02-26 00:00:00

Undirected graphical models are used extensively in the biological and social
sciences to encode a pattern of conditional independences between variables,
where the absence of an edge between two nodes $a$ and $b$ indicates that the
corresponding two variables $X_a$ and $X_b$ are believed to be conditionally
independent, after controlling for all other measured variables. In the
Gaussian case, conditional independence corresponds to a zero entry in the
precision matrix $\Omega$ (the inverse of the covariance matrix $\Sigma$). Real
data often exhibits heavy tail dependence between variables, which cannot be
captured by the commonly-used Gaussian or nonparanormal (Gaussian copula)
graphical models. In this paper, we study the transelliptical model, an
elliptical copula model that generalizes Gaussian and nonparanormal models to a
broader family of distributions. We propose the ROCKET method, which constructs
an estimator of $\Omega_{ab}$ that we prove to be asymptotically normal under
mild assumptions. Empirically, ROCKET outperforms the nonparanormal and
Gaussian models in terms of achieving accurate inference on simulated data. We
also compare the three methods on real data (daily stock returns), and find
that the ROCKET estimator is the only method whose behavior across subsamples
agrees with the distribution predicted by the theory.
End-to-End Multi-View Lipreading
Authors: Stavros Petridis, Yujiang Wang, Zuwei Li, Maja Pantic
Date: 2017-09-01 00:00:00
Tasks: General Classification, Lipreading

Non-frontal lip views contain useful information which can be used to enhance
the performance of frontal view lipreading. However, the vast majority of
recent lipreading works, including the deep learning approaches which
significantly outperform traditional approaches, have focused on frontal mouth
images. As a consequence, research on joint learning of visual features and
speech classification from multiple views is limited. In this work, we present
an end-to-end multi-view lipreading system based on Bidirectional Long-Short
Memory (BLSTM) networks. To the best of our knowledge, this is the first model
which simultaneously learns to extract features directly from the pixels and
performs visual speech classification from multiple views and also achieves
state-of-the-art performance. The model consists of multiple identical streams,
one for each view, which extract features directly from different poses of
mouth images. The temporal dynamics in each stream/view are modelled by a BLSTM
and the fusion of multiple streams/views takes place via another BLSTM. An
absolute average improvement of 3% and 3.8% over the frontal view performance
is reported on the OuluVS2 database when the best two (frontal and profile) and
three views (frontal, profile, 45) are combined, respectively. The best
three-view model results in a 10.5% absolute improvement over the current
multi-view state-of-the-art performance on OuluVS2, without using external
databases for training, achieving a maximum classification accuracy of 96.9%.
Bringing Salary Transparency to the World: Computing Robust Compensation Insights via LinkedIn Salary
Authors: Krishnaram Kenthapadi, Stuart Ambler, Liang Zhang, Deepak Agarwal
Date: 2017-03-29 00:00:00

The recently launched LinkedIn Salary product has been designed with the goal
of providing compensation insights to the world's professionals and thereby
helping them optimize their earning potential. We describe the overall design
and architecture of the statistical modeling system underlying this product. We
focus on the unique data mining challenges while designing and implementing the
system, and describe the modeling components such as Bayesian hierarchical
smoothing that help to compute and present robust compensation insights to
users. We report on extensive evaluation with nearly one year of de-identified
compensation data collected from over one million LinkedIn users, thereby
demonstrating the efficacy of the statistical models. We also highlight the
lessons learned through the deployment of our system at LinkedIn.
Signal Recovery from Unlabeled Samples
Authors: Saeid Haghighatshoar, Giuseppe Caire
Date: 2017-01-30 00:00:00
Tasks: compressed sensing

In this paper, we study the recovery of a signal from a set of noisy linear
projections (measurements), when such projections are unlabeled, that is, the
correspondence between the measurements and the set of projection vectors
(i.e., the rows of the measurement matrix) is not known a priori. We consider a
special case of unlabeled sensing referred to as Unlabeled Ordered Sampling
(UOS) where the ordering of the measurements is preserved. We identify a
natural duality between this problem and classical Compressed Sensing (CS),
where we show that the unknown support (location of nonzero elements) of a
sparse signal in CS corresponds to the unknown indices of the measurements in
UOS. While in CS it is possible to recover a sparse signal from an
under-determined set of linear equations (less equations than the signal
dimension), successful recovery in UOS requires taking more samples than the
dimension of the signal. Motivated by this duality, we develop a Restricted
Isometry Property (RIP) similar to that in CS. We also design a low-complexity
Alternating Minimization algorithm that achieves a stable signal recovery under
the established RIP. We analyze our proposed algorithm for different signal
dimensions and number of measurements theoretically and investigate its
performance empirically via simulations. The results are reminiscent of
phase-transition similar to that occurring in CS.
A new image compression by gradient Haar wavelet
Authors: Yaser Sadra
Date: 2017-04-28 00:00:00
Tasks: Image Compression, SSIM

With the development of human communications the usage of Visual
Communications has also increased. The advancement of image compression methods
is one of the main reasons for the enhancement. This paper first presents main
modes of image compression methods such as JPEG and JPEG2000 without
mathematical details. Also, the paper describes gradient Haar wavelet
transforms in order to construct a preliminary image compression algorithm.
Then, a new image compression method is proposed based on the preliminary image
compression algorithm that can improve standards of image compression. The new
method is compared with original modes of JPEG and JPEG2000 (based on Haar
wavelet) by image quality measures such as MAE, PSNAR, and SSIM. The image
quality and statistical results confirm that can boost image compression
standards. It is suggested that the new method is used in a part or all of an
image compression standard.
Convergence, Continuity and Recurrence in Dynamic Epistemic Logic
Authors: Dominik Klein, Rasmus K. Rendsvig
Date: 2017-09-01 00:00:00

The paper analyzes dynamic epistemic logic from a topological perspective.
The main contribution consists of a framework in which dynamic epistemic logic
satisfies the requirements for being a topological dynamical system thus
interfacing discrete dynamic logics with continuous mappings of dynamical
systems. The setting is based on a notion of logical convergence,
demonstratively equivalent with convergence in Stone topology. Presented is a
flexible, parametrized family of metrics inducing the latter, used as an
analytical aid. We show maps induced by action model transformations continuous
with respect to the Stone topology and present results on the recurrent
behavior of said maps.
Inferring Networked Device Categories from Low-Level Activity Indicators
Authors: Kyumars Sheykh Esmaili, Jaideep Chandrashekar, Pascal Le Guyadec
Date: 2017-09-01 00:00:00

We study the problem of inferring the type of a networked device in a home
network by leveraging low level traffic activity indicators seen at commodity
home gateways. We analyze a dataset of detailed device network activity
obtained from 240 subscriber homes of a large European ISP and extract a number
of traffic and spatial fingerprints for individual devices. We develop a two
level taxonomy to describe devices onto which we map individual devices using a
number of heuristics. We leverage the heuristically derived labels to train
classifiers that distinguish device classes based on the traffic and spatial
fingerprints of a device. Our results show an accuracy level up to 91% for the
coarse level category and up to 84% for the fine grained category. By
incorporating information from other sources (e.g., MAC OUI), we are able to
further improve accuracy to above 97% and 92%, respectively. Finally, we also
extract a set of simple and human-readable rules that concisely capture the
behaviour of these distinct device categories.
Content-Based Video-Music Retrieval Using Soft Intra-Modal Structure Constraint
Authors: Sungeun Hong, Woobin Im, Hyun S. Yang
Date: 2017-04-22 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval

Up to now, only limited research has been conducted on cross-modal retrieval
of suitable music for a specified video or vice versa. Moreover, much of the
existing research relies on metadata such as keywords, tags, or associated
description that must be individually produced and attached posterior. This
paper introduces a new content-based, cross-modal retrieval method for video
and music that is implemented through deep neural networks. We train the
network via inter-modal ranking loss such that videos and music with similar
semantics end up close together in the embedding space. However, if only the
inter-modal ranking constraint is used for embedding, modality-specific
characteristics can be lost. To address this problem, we propose a novel soft
intra-modal structure loss that leverages the relative distance relationship
between intra-modal samples before embedding. We also introduce reasonable
quantitative and qualitative experimental protocols to solve the lack of
standard protocols for less-mature video-music related tasks. Finally, we
construct a large-scale 200K video-music pair benchmark. All the datasets and
source code can be found in our online repository
(https://github.com/csehong/VM-NET).
LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions
Authors: Yu Wang, Jiayi Liu, Yuxiang Liu, Jun Hao, Yang He, Jinghe Hu, Weipeng P. Yan, Mantian Li
Date: 2017-08-18 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

We present LADDER, the first deep reinforcement learning agent that can
successfully learn control policies for large-scale real-world problems
directly from raw inputs composed of high-level semantic information. The agent
is based on an asynchronous stochastic variant of DQN (Deep Q Network) named
DASQN. The inputs of the agent are plain-text descriptions of states of a game
of incomplete information, i.e. real-time large scale online auctions, and the
rewards are auction profits of very large scale. We apply the agent to an
essential portion of JD's online RTB (real-time bidding) advertising business
and find that it easily beats the former state-of-the-art bidding policy that
had been carefully engineered and calibrated by human experts: during JD.com's
June 18th anniversary sale, the agent increased the company's ads revenue from
the portion by more than 50%, while the advertisers' ROI (return on investment)
also improved significantly.
Too Far to See? Not Really! --- Pedestrian Detection with Scale-aware Localization Policy
Authors: Xiaowei Zhang, Li Cheng, Bo Li, Hai-Miao Hu
Date: 2017-09-01 00:00:00
Tasks: Pedestrian Detection, Region Proposal

A major bottleneck of pedestrian detection lies on the sharp performance
deterioration in the presence of small-size pedestrians that are relatively far
from the camera. Motivated by the observation that pedestrians of disparate
spatial scales exhibit distinct visual appearances, we propose in this paper an
active pedestrian detector that explicitly operates over multiple-layer
neuronal representations of the input still image. More specifically,
convolutional neural nets such as ResNet and faster R-CNNs are exploited to
provide a rich and discriminative hierarchy of feature representations as well
as initial pedestrian proposals. Here each pedestrian observation of distinct
size could be best characterized in terms of the ResNet feature representation
at a certain layer of the hierarchy; Meanwhile, initial pedestrian proposals
are attained by faster R-CNNs techniques, i.e. region proposal network and
follow-up region of interesting pooling layer employed right after the specific
ResNet convolutional layer of interest, to produce joint predictions on the
bounding-box proposals' locations and categories (i.e. pedestrian or not). This
is engaged as input to our active detector where for each initial pedestrian
proposal, a sequence of coordinate transformation actions is carried out to
determine its proper x-y 2D location and layer of feature representation, or
eventually terminated as being background. Empirically our approach is
demonstrated to produce overall lower detection errors on widely-used
benchmarks, and it works particularly well with far-scale pedestrians. For
example, compared with 60.51% log-average miss rate of the state-of-the-art
MS-CNN for far-scale pedestrians (those below 80 pixels in bounding-box height)
of the Caltech benchmark, the miss rate of our approach is 41.85%, with a
notable reduction of 18.68%.
Learning Multi-item Auctions with (or without) Samples
Authors: Yang Cai, Constantinos Daskalakis
Date: 2017-09-01 00:00:00

We provide algorithms that learn simple auctions whose revenue is
approximately optimal in multi-item multi-bidder settings, for a wide range of
valuations including unit-demand, additive, constrained additive, XOS, and
subadditive. We obtain our learning results in two settings. The first is the
commonly studied setting where sample access to the bidders' distributions over
valuations is given, for both regular distributions and arbitrary distributions
with bounded support. Our algorithms require polynomially many samples in the
number of items and bidders. The second is a more general max-min learning
setting that we introduce, where we are given "approximate distributions," and
we seek to compute an auction whose revenue is approximately optimal
simultaneously for all "true distributions" that are close to the given ones.
These results are more general in that they imply the sample-based results, and
are also applicable in settings where we have no sample access to the
underlying distributions but have estimated them indirectly via market research
or by observation of previously run, potentially non-truthful auctions.
  Our results hold for valuation distributions satisfying the standard (and
necessary) independence-across-items property. They also generalize and improve
upon recent works, which have provided algorithms that learn approximately
optimal auctions in more restricted settings with additive, subadditive and
unit-demand valuations using sample access to distributions. We generalize
these results to the complete unit-demand, additive, and XOS setting, to i.i.d.
subadditive bidders, and to the max-min setting.
  Our results are enabled by new uniform convergence bounds for hypotheses
classes under product measures. Our bounds result in exponential savings in
sample complexity compared to bounds derived by bounding the VC dimension, and
are of independent interest.
Semantic Composition via Probabilistic Model Theory
Authors: Guy Emerson, Ann Copestake
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 1
Tasks: model, Semantic Composition, Word Similarity

Semantic composition remains an open problem for vector space models of
semantics. In this paper, we explain how the probabilistic graphical model used
in the framework of Functional Distributional Semantics can be interpreted as a
probabilistic version of model theory. Building on this, we explain how various
semantic phenomena can be recast in terms of conditional probabilities in the
graphical model. This connection between formal semantics and machine learning
is helpful in both directions: it gives us an explicit mechanism for modelling
context-dependent meanings (a challenge for formal semantics), and also gives
us well-motivated techniques for composing distributed representations (a
challenge for distributional semantics). We present results on two datasets
that go beyond word similarity, showing how these semantically-motivated
techniques improve on the performance of vector models.
Variational Inference for Logical Inference
Authors: Guy Emerson, Ann Copestake
Date: 2017-09-01 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity, Variational Inference

Functional Distributional Semantics is a framework that aims to learn, from
text, semantic representations which can be interpreted in terms of truth. Here
we make two contributions to this framework. The first is to show how a type of
logical inference can be performed by evaluating conditional probabilities. The
second is to make these calculations tractable by means of a variational
approximation. This approximation also enables faster convergence during
training, allowing us to close the gap with state-of-the-art vector space
models when evaluating on semantic similarity. We demonstrate promising
performance on two tasks.
NODDI-SH: a computational efficient NODDI extension for fODF estimation in diffusion MRI
Authors: Mauro Zucchelli, Maxime Descoteaux, Gloria Menegaz
Date: 2017-08-28 00:00:00
Tasks: Benchmarking, Diffusion  MRI

Diffusion Magnetic Resonance Imaging (DMRI) is the only non-invasive imaging
technique which is able to detect the principal directions of water diffusion
as well as neurites density in the human brain. Exploiting the ability of
Spherical Harmonics (SH) to model spherical functions, we propose a new
reconstruction model for DMRI data which is able to estimate both the fiber
Orientation Distribution Function (fODF) and the relative volume fractions of
the neurites in each voxel, which is robust to multiple fiber crossings. We
consider a Neurite Orientation Dispersion and Density Imaging (NODDI) inspired
single fiber diffusion signal to be derived from three compartments:
intracellular, extracellular, and cerebrospinal fluid. The model, called
NODDI-SH, is derived by convolving the single fiber response with the fODF in
each voxel. NODDI-SH embeds the calculation of the fODF and the neurite density
in a unified mathematical model providing efficient, robust and accurate
results. Results were validated on simulated data and tested on
\textit{in-vivo} data of human brain, and compared to and Constrained Spherical
Deconvolution (CSD) for benchmarking. Results revealed competitive performance
in all respects and inherent adaptivity to local microstructure, while sensibly
reducing the computational cost. We also investigated NODDI-SH performance when
only a limited number of samples are available for the fitting, demonstrating
that 60 samples are enough to obtain reliable results. The fast computational
time and the low number of signal samples required, make NODDI-SH feasible for
clinical application.
DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation
Authors: Ruirui Li, Wenjie Liu, Lei Yang, Shihao Sun, Wei Hu, Fan Zhang, Wei Li
Date: 2017-09-01 00:00:00
Tasks: Segmentation, Semantic Segmentation

Semantic segmentation is a fundamental research in remote sensing image
processing. Because of the complex maritime environment, the sea-land
segmentation is a challenging task. Although the neural network has achieved
excellent performance in semantic segmentation in the last years, there are a
few of works using CNN for sea-land segmentation and the results could be
further improved. This paper proposes a novel deep convolution neural network
named DeepUNet. Like the U-Net, its structure has a contracting path and an
expansive path to get high resolution output. But differently, the DeepUNet
uses DownBlocks instead of convolution layers in the contracting path and uses
UpBlock in the expansive path. The two novel blocks bring two new connections
that are U-connection and Plus connection. They are promoted to get more
precise segmentation results. To verify our network architecture, we made a new
challenging sea-land dataset and compare the DeepUNet on it with the SegNet and
the U-Net. Experimental results show that DeepUNet achieved good performance
compared with other architectures, especially in high-resolution remote sensing
imagery.
A Two-Step Disentanglement Method
Authors: Naama Hadad, Lior Wolf, Moni Shahar
Date: 2017-09-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Disentanglement, Vocal Bursts Valence Prediction

We address the problem of disentanglement of factors that generate a given data into those that are correlated with the labeling and those that are not. Our solution is simpler than previous solutions and employs adversarial training. First, the part of the data that is correlated with the labels is extracted by training a classifier. Then, the other part is extracted such that it enables the reconstruction of the original data but does not contain label information. The utility of the new method is demonstrated on visual datasets as well as on financial data. Our code is available at https://github.com/naamahadad/A-Two-Step-Disentanglement-Method
On the Powerball Method for Optimization
Authors: Ye Yuan, Mu Li, Jun Liu, Claire J. Tomlin
Date: 2016-03-24 00:00:00

We propose a new method to accelerate the convergence of optimization
algorithms. This method simply adds a power coefficient $\gamma\in[0,1)$ to the
gradient during optimization. We call this the Powerball method and analyze the
convergence rate for the Powerball method for strongly convex functions. While
theoretically the Powerball method is guaranteed to have a linear convergence
rate in the same order of the gradient method, we show that empirically it
significantly outperforms the gradient descent and Newton's method, especially
during the initial iterations. We demonstrate that the Powerball method
provides a $10$-fold speedup of the convergence of both gradient descent and
L-BFGS on multiple real datasets.
A Unified View of Multi-Label Performance Measures
Authors: Xi-Zhu Wu, Zhi-Hua Zhou
Date: 2016-09-01 00:00:00
Proceeding: ICML 2017 8
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-label classification deals with the problem where each instance is
associated with multiple class labels. Because evaluation in multi-label
classification is more complicated than single-label setting, a number of
performance measures have been proposed. It is noticed that an algorithm
usually performs differently on different measures. Therefore, it is important
to understand which algorithms perform well on which measure(s) and why. In
this paper, we propose a unified margin view to revisit eleven performance
measures in multi-label classification. In particular, we define label-wise
margin and instance-wise margin, and prove that through maximizing these
margins, different corresponding performance measures will be optimized. Based
on the defined margins, a max-margin approach called LIMO is designed and
empirical results verify our theoretical findings.