pwc-archive/papers-with-abstracts huggingface dataset:
Convergence Analysis of Gradient EM for Multi-component Gaussian Mixture
Authors: Bowei Yan, Mingzhang Yin, Purnamrita Sarkar
Date: 2017-05-23 00:00:00
Tasks: Learning Theory

In this paper, we study convergence properties of the gradient
Expectation-Maximization algorithm \cite{lange1995gradient} for Gaussian
Mixture Models for general number of clusters and mixing coefficients. We
derive the convergence rate depending on the mixing coefficients, minimum and
maximum pairwise distances between the true centers and dimensionality and
number of components; and obtain a near-optimal local contraction radius. While
there have been some recent notable works that derive local convergence rates
for EM in the two equal mixture symmetric GMM, in the more general case, the
derivations need structurally different and non-trivial arguments. We use
recent tools from learning theory and empirical processes to achieve our
theoretical results.
Iterative Deep Learning for Network Topology Extraction
Authors: Carles Ventura, Jordi Pont-Tuset, Sergi Caelles, Kevis-Kokitsi Maninis, Luc van Gool
Date: 2017-12-04 00:00:00
Tasks: Deep Learning

This paper tackles the task of estimating the topology of filamentary
networks such as retinal vessels and road networks. Building on top of a global
model that performs a dense semantical classification of the pixels of the
image, we design a Convolutional Neural Network (CNN) that predicts the local
connectivity between the central pixel of an input patch and its border points.
By iterating this local connectivity we sweep the whole image and infer the
global topology of the filamentary network, inspired by a human delineating a
complex network with the tip of their finger.
  We perform an extensive and comprehensive qualitative and quantitative
evaluation on two tasks: retinal veins and arteries topology extraction and
road network estimation. In both cases, represented by two publicly available
datasets (DRIVE and Massachusetts Roads), we show superior performance to very
strong baselines.
An Encoder-Decoder Model for ICD-10 Coding of Death Certificates
Authors: Elena Tutubalina, Zulfat Miftahutdinov
Date: 2017-12-04 00:00:00
Tasks: Decoder, Translation

Information extraction from textual documents such as hospital records and
healthrelated user discussions has become a topic of intense interest. The task
of medical concept coding is to map a variable length text to medical concepts
and corresponding classification codes in some external system or ontology. In
this work, we utilize recurrent neural networks to automatically assign ICD-10
codes to fragments of death certificates written in English. We develop
end-to-end neural architectures directly tailored to the task, including basic
encoder-decoder architecture for statistical translation. In order to
incorporate prior knowledge, we concatenate cosine similarities vector among
the text and dictionary entry to the encoded state. Being applied to a standard
benchmark from CLEF eHealth 2017 challenge, our model achieved F-measure of
85.01% on a full test set with significant improvement as compared to the
average score of 62.2% for all official participants approaches.
Speaker identification from the sound of the human breath
Authors: Wenbo Zhao, Yang Gao, Rita Singh
Date: 2017-12-01 00:00:00
Tasks: Speaker Identification, Speaker Recognition

This paper examines the speaker identification potential of breath sounds in
continuous speech. Speech is largely produced during exhalation. In order to
replenish air in the lungs, speakers must periodically inhale. When inhalation
occurs in the midst of continuous speech, it is generally through the mouth.
Intra-speech breathing behavior has been the subject of much study, including
the patterns, cadence, and variations in energy levels. However, an often
ignored characteristic is the {\em sound} produced during the inhalation phase
of this cycle. Intra-speech inhalation is rapid and energetic, performed with
open mouth and glottis, effectively exposing the entire vocal tract to enable
maximum intake of air. This results in vocal tract resonances evoked by
turbulence that are characteristic of the speaker's speech-producing apparatus.
Consequently, the sounds of inhalation are expected to carry information about
the speaker's identity. Moreover, unlike other spoken sounds which are subject
to active control, inhalation sounds are generally more natural and less
affected by voluntary influences. The goal of this paper is to demonstrate that
breath sounds are indeed bio-signatures that can be used to identify speakers.
We show that these sounds by themselves can yield remarkably accurate speaker
recognition with appropriate feature representations and classification
frameworks.
Fast Top-k Area Topics Extraction with Knowledge Base
Authors: Fang Zhang, Xiaochen Wang, Jingfei Han, Jie Tang, Shiyin Wang, Marie-Francine Moens
Date: 2017-10-13 00:00:00

What are the most popular research topics in Artificial Intelligence (AI)? We
formulate the problem as extracting top-$k$ topics that can best represent a
given area with the help of knowledge base. We theoretically prove that the
problem is NP-hard and propose an optimization model, FastKATE, to address this
problem by combining both explicit and latent representations for each topic.
We leverage a large-scale knowledge base (Wikipedia) to generate topic
embeddings using neural networks and use this kind of representations to help
capture the representativeness of topics for given areas. We develop a fast
heuristic algorithm to efficiently solve the problem with a provable error
bound. We evaluate the proposed model on three real-world datasets.
Experimental results demonstrate our model's effectiveness, robustness,
real-timeness (return results in $<1$s), and its superiority over several
alternative methods.
Interacting with Acoustic Simulation and Fabrication
Authors: Dingzeyu Li
Date: 2017-08-09 00:00:00

Incorporating accurate physics-based simulation into interactive design tools
is challenging. However, adding the physics accurately becomes crucial to
several emerging technologies. For example, in virtual/augmented reality
(VR/AR) videos, the faithful reproduction of surrounding audios is required to
bring the immersion to the next level. Similarly, as personal fabrication is
made possible with accessible 3D printers, more intuitive tools that respect
the physical constraints can help artists to prototype designs. One main hurdle
is the sheer amount of computation complexity to accurately reproduce the
real-world phenomena through physics-based simulation. In my thesis research, I
develop interactive tools that implement efficient physics-based simulation
algorithms for automatic optimization and intuitive user interaction.
Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks
Authors: Kunal Swami, Pranav P. Deshpande, Gaurav Khandelwal, Ajay Vijayvargiya
Date: 2017-12-04 00:00:00
Tasks: Object Recognition, Scene Understanding

Image orientation detection requires high-level scene understanding. Humans
use object recognition and contextual scene information to correctly orient
images. In literature, the problem of image orientation detection is mostly
confronted by using low-level vision features, while some approaches
incorporate few easily detectable semantic cues to gain minor improvements. The
vast amount of semantic content in images makes orientation detection
challenging, and therefore there is a large semantic gap between existing
methods and human behavior. Also, existing methods in literature report highly
discrepant detection rates, which is mainly due to large differences in
datasets and limited variety of test images used for evaluation. In this work,
for the first time, we leverage the power of deep learning and adapt
pre-trained convolutional neural networks using largest training dataset
to-date for the image orientation detection task. An extensive evaluation of
our model on different public datasets shows that it remarkably generalizes to
correctly orient a large set of unconstrained images; it also significantly
outperforms the state-of-the-art and achieves accuracy very close to that of
humans.
Episodic memory for continual model learning
Authors: David G. Nagy, Gergő Orbán
Date: 2017-12-04 00:00:00
Tasks: Bayesian Inference, model, Model Selection

Both the human brain and artificial learning agents operating in real-world
or comparably complex environments are faced with the challenge of online model
selection. In principle this challenge can be overcome: hierarchical Bayesian
inference provides a principled method for model selection and it converges on
the same posterior for both off-line (i.e. batch) and online learning. However,
maintaining a parameter posterior for each model in parallel has in general an
even higher memory cost than storing the entire data set and is consequently
clearly unfeasible. Alternatively, maintaining only a limited set of models in
memory could limit memory requirements. However, sufficient statistics for one
model will usually be insufficient for fitting a different kind of model,
meaning that the agent loses information with each model change. We propose
that episodic memory can circumvent the challenge of limited memory-capacity
online model selection by retaining a selected subset of data points. We design
a method to compute the quantities necessary for model selection even when the
data is discarded and only statistics of one (or few) learnt models are
available. We demonstrate on a simple model that a limited-sized episodic
memory buffer, when the content is optimised to retain data with statistics not
matching the current representation, can resolve the fundamental challenge of
online model selection.
Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening
Authors: Mohsen Ahmadi Fahandar, Eyke Hüllermeier, Inés Couso
Date: 2017-12-04 00:00:00
Proceeding: ICML 2017 8

We consider the problem of statistical inference for ranking data,
specifically rank aggregation, under the assumption that samples are incomplete
in the sense of not comprising all choice alternatives. In contrast to most
existing methods, we explicitly model the process of turning a full ranking
into an incomplete one, which we call the coarsening process. To this end, we
propose the concept of rank-dependent coarsening, which assumes that incomplete
rankings are produced by projecting a full ranking to a random subset of ranks.
For a concrete instantiation of our model, in which full rankings are drawn
from a Plackett-Luce distribution and observations take the form of pairwise
preferences, we study the performance of various rank aggregation methods. In
addition to predictive accuracy in the finite sample setting, we address the
theoretical question of consistency, by which we mean the ability to recover a
target ranking when the sample size goes to infinity, despite a potential bias
in the observations caused by the (unknown) coarsening.
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection
Authors: Ruimin Sun, Xiaoyong Yuan, Pan He, Qile Zhu, Aokun Chen, Andre Gregio, Daniela Oliveira, Xiaolin Li
Date: 2017-12-04 00:00:00
Tasks: CPU, General Classification, Malware Detection

Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead. In this paper, we introduce PROPEDEUTICA, a framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) techniques. In PROPEDEUTICA, all software start execution are considered as benign and monitored by a conventional ML classifier for fast detection. If the software receives a borderline classification from the ML detector (e.g. the software is 50% likely to be benign and 50% likely to be malicious), the software will be transferred to a more accurate, yet performance demanding DL detector. To address spatial-temporal dynamics and software execution heterogeneity, we introduce a novel DL architecture (DEEPMALWARE) for PROPEDEUTICA with multi-stream inputs. We evaluated PROPEDEUTICA with 9,115 malware samples and 1,338 benign software from various categories for the Windows OS. With a borderline interval of [30%-70%], PROPEDEUTICA achieves an accuracy of 94.34% and a false-positive rate of 8.75%, with 41.45% of the samples moved for DEEPMALWARE analysis. Even using only CPU, PROPEDEUTICA can detect malware within less than 0.1 seconds.
SeeThrough: Finding Chairs in Heavily Occluded Indoor Scene Images
Authors: Moos Hueting, Pradyumna Reddy, Vladimir Kim, Ersin Yumer, Nathan Carr, Niloy Mitra
Date: 2017-10-28 00:00:00
Tasks: object-detection, Object Detection

Discovering 3D arrangements of objects from single indoor images is important
given its many applications including interior design, content creation, etc.
Although heavily researched in the recent years, existing approaches break down
under medium or heavy occlusion as the core object detection module starts
failing in absence of directly visible cues. Instead, we take into account
holistic contextual 3D information, exploiting the fact that objects in indoor
scenes co-occur mostly in typical near-regular configurations. First, we use a
neural network trained on real indoor annotated images to extract 2D keypoints,
and feed them to a 3D candidate object generation stage. Then, we solve a
global selection problem among these 3D candidates using pairwise co-occurrence
statistics discovered from a large 3D scene database. We iterate the process
allowing for candidates with low keypoint response to be incrementally detected
based on the location of the already discovered nearby objects. Focusing on
chairs, we demonstrate significant performance improvement over combinations of
state-of-the-art methods, especially for scenes with moderately to severely
occluded objects.
Inferring agent objectives at different scales of a complex adaptive system
Authors: Dieter Hendricks, Adam Cobb, Richard Everett, Jonathan Downing, Stephen J. Roberts
Date: 2017-12-04 00:00:00
Tasks: Reinforcement Learning

We introduce a framework to study the effective objectives at different time
scales of financial market microstructure. The financial market can be regarded
as a complex adaptive system, where purposeful agents collectively and
simultaneously create and perceive their environment as they interact with it.
It has been suggested that multiple agent classes operate in this system, with
a non-trivial hierarchy of top-down and bottom-up causation classes with
different effective models governing each level. We conjecture that agent
classes may in fact operate at different time scales and thus act differently
in response to the same perceived market state. Given scale-specific temporal
state trajectories and action sequences estimated from aggregate market
behaviour, we use Inverse Reinforcement Learning to compute the effective
reward function for the aggregate agent class at each scale, allowing us to
assess the relative attractiveness of feature vectors across different scales.
Differences in reward functions for feature vectors may indicate different
objectives of market participants, which could assist in finding the scale
boundary for agent classes. This has implications for learning algorithms
operating in this domain.
New Techniques for Inferring L-Systems Using Genetic Algorithm
Authors: Jason Bernard, Ian McQuillan
Date: 2017-12-01 00:00:00

Lindenmayer systems (L-systems) are a formal grammar system that iteratively
rewrites all symbols of a string, in parallel. When visualized with a graphical
interpretation, the images have self-similar shapes that appear frequently in
nature, and they have been particularly successful as a concise, reusable
technique for simulating plants. The L-system inference problem is to find an
L-system to simulate a given plant. This is currently done mainly by experts,
but this process is limited by the availability of experts, the complexity that
may be solved by humans, and time. This paper introduces the Plant Model
Inference Tool (PMIT) that infers deterministic context-free L-systems from an
initial sequence of strings generated by the system using a genetic algorithm.
PMIT is able to infer more complex systems than existing approaches. Indeed,
while existing approaches are limited to L-systems with a total sum of 20
combined symbols in the productions, PMIT can infer almost all L-systems tested
where the total sum is 140 symbols. This was validated using a test bed of 28
previously developed L-system models, in addition to models created
artificially by bootstrapping larger models.
Image Decomposition Using a Robust Regression Approach
Authors: Shervin Minaee, Yao Wang
Date: 2016-09-13 00:00:00
Tasks: Clustering, Foreground Segmentation, regression, Segmentation

This paper considers how to separate text and/or graphics from smooth
background in screen content and mixed content images and proposes an algorithm
to perform this segmentation task. The proposed methods make use of the fact
that the background in each block is usually smoothly varying and can be
modeled well by a linear combination of a few smoothly varying basis functions,
while the foreground text and graphics create sharp discontinuity. This
algorithm separates the background and foreground pixels by trying to fit pixel
values in the block into a smooth function using a robust regression method.
The inlier pixels that can be well represented with the smooth model will be
considered as background, while remaining outlier pixels will be considered
foreground. We have also created a dataset of screen content images extracted
from HEVC standard test sequences for screen content coding with their ground
truth segmentation result which can be used for this task. The proposed
algorithm has been tested on the dataset mentioned above and is shown to have
superior performance over other methods, such as the hierarchical k-means
clustering algorithm, shape primitive extraction and coding, and the least
absolute deviation fitting scheme for foreground segmentation.
Natural Langevin Dynamics for Neural Networks
Authors: Gaétan Marceau-Caron, Yann Ollivier
Date: 2017-12-04 00:00:00

One way to avoid overfitting in machine learning is to use model parameters
distributed according to a Bayesian posterior given the data, rather than the
maximum likelihood estimator. Stochastic gradient Langevin dynamics (SGLD) is
one algorithm to approximate such Bayesian posteriors for large models and
datasets. SGLD is a standard stochastic gradient descent to which is added a
controlled amount of noise, specifically scaled so that the parameter converges
in law to the posterior distribution [WT11, TTV16]. The posterior predictive
distribution can be approximated by an ensemble of samples from the trajectory.
  Choice of the variance of the noise is known to impact the practical behavior
of SGLD: for instance, noise should be smaller for sensitive parameter
directions. Theoretically, it has been suggested to use the inverse Fisher
information matrix of the model as the variance of the noise, since it is also
the variance of the Bayesian posterior [PT13, AKW12, GC11]. But the Fisher
matrix is costly to compute for large- dimensional models.
  Here we use the easily computed Fisher matrix approximations for deep neural
networks from [MO16, Oll15]. The resulting natural Langevin dynamics combines
the advantages of Amari's natural gradient descent and Fisher-preconditioned
Langevin dynamics for large neural networks.
  Small-scale experiments on MNIST show that Fisher matrix preconditioning
brings SGLD close to dropout as a regularizing technique.
A Generalized Motion Pattern and FCN based approach for retinal fluid detection and segmentation
Authors: Shivin Yadav, Karthik Gopinath, Jayanthi Sivaswamy
Date: 2017-12-04 00:00:00
Tasks: Segmentation

SD-OCT is a non-invasive cross-sectional imaging modality used for diagnosis
of macular defects. Efficient detection and segmentation of the abnormalities
seen as biomarkers in OCT can help in analyzing the progression of the disease
and advising effective treatment for the associated disease. In this work, we
propose a fully automated Generalized Motion Pattern(GMP) based segmentation
method using a cascade of fully convolutional networks for detection and
segmentation of retinal fluids from SD-OCT scans. General methods for
segmentation depend on domain knowledge-based feature extraction, whereas we
propose a method based on Generalized Motion Pattern (GMP) which is derived by
inducing motion to an image to suppress the background.The proposed method is
parallelizable and handles inter-scanner variability efficiently. Our method
achieves a mean Dice score of 0.61,0.70 and 0.73 during segmentation and a mean
AUC of 0.85,0.84 and 0.87 during detection for the 3 types of fluids IRF, SRF
and PDE respectively.
Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images
Authors: Tribhuvanesh Orekondy, Mario Fritz, Bernt Schiele
Date: 2017-12-04 00:00:00
Proceeding: CVPR 2018 6

Images convey a broad spectrum of personal information. If such images are
shared on social media platforms, this personal information is leaked which
conflicts with the privacy of depicted persons. Therefore, we aim for automated
approaches to redact such private information and thereby protect privacy of
the individual. By conducting a user study we find that obfuscating the image
regions related to the private information leads to privacy while retaining
utility of the images. Moreover, by varying the size of the regions different
privacy-utility trade-offs can be achieved. Our findings argue for a "redaction
by segmentation" paradigm. Hence, we propose the first sizable dataset of
private images "in the wild" annotated with pixel and instance level labels
across a broad range of privacy classes. We present the first model for
automatic redaction of diverse private information.
SOT for MOT
Authors: Qizheng He, Jia-Nan Wu, Gang Yu, Chi Zhang
Date: 2017-12-04 00:00:00
Tasks: Multiple Object Tracking, Object, Object Tracking

In this paper we present a robust tracker to solve the multiple object
tracking (MOT) problem, under the framework of tracking-by-detection. As the
first contribution, we innovatively combine single object tracking (SOT)
algorithms with multiple object tracking algorithms, and our results show that
SOT is a general way to strongly reduce the number of false negatives,
regardless of the quality of detection. Another contribution is that we show
with a deep learning based appearance model, it is easy to associate detections
of the same object efficiently and also with high accuracy. This appearance
model plays an important role in our MOT algorithm to correctly associate
detections into long trajectories, and also in our SOT algorithm to discover
new detections mistakenly missed by the detector. The deep neural network based
model ensures the robustness of our tracking algorithm, which can perform data
association in a wide variety of scenes. We ran comprehensive experiments on a
large-scale and challenging dataset, the MOT16 benchmark, and results showed
that our tracker achieved state-of-the-art performance based on both public and
private detections.