pwc-archive/papers-with-abstracts huggingface dataset:
Provable Bayesian Inference via Particle Mirror Descent
Authors: Bo Dai, Niao He, Hanjun Dai, Le Song
Date: 2015-06-09 00:00:00
Tasks: Bayesian Inference, Gaussian Processes

Bayesian methods are appealing in their flexibility in modeling complex data
and ability in capturing uncertainty in parameters. However, when Bayes' rule
does not result in tractable closed-form, most approximate inference algorithms
lack either scalability or rigorous guarantees. To tackle this challenge, we
propose a simple yet provable algorithm, \emph{Particle Mirror Descent} (PMD),
to iteratively approximate the posterior density. PMD is inspired by stochastic
functional mirror descent where one descends in the density space using a small
batch of data points at each iteration, and by particle filtering where one
uses samples to approximate a function. We prove result of the first kind that,
with $m$ particles, PMD provides a posterior density estimator that converges
in terms of $KL$-divergence to the true posterior in rate $O(1/\sqrt{m})$. We
demonstrate competitive empirical performances of PMD compared to several
approximate inference algorithms in mixture models, logistic regression, sparse
Gaussian processes and latent Dirichlet allocation on large scale datasets.
Clustering on the Edge: Learning Structure in Graphs
Authors: Matt Barnes, Artur Dubrawski
Date: 2016-05-05 00:00:00
Tasks: Clustering, Entity Resolution, Image Segmentation, Semantic Segmentation

With the recent popularity of graphical clustering methods, there has been an
increased focus on the information between samples. We show how learning
cluster structure using edge features naturally and simultaneously determines
the most likely number of clusters and addresses data scale issues. These
results are particularly useful in instances where (a) there are a large number
of clusters and (b) we have some labeled edges. Applications in this domain
include image segmentation, community discovery and entity resolution. Our
model is an extension of the planted partition model and our solution uses
results of correlation clustering, which achieves a partition O(log(n))-close
to the log-likelihood of the true clustering.
We Are Humor Beings: Understanding and Predicting Visual Humor
Authors: Arjun Chandrasekaran, Ashwin K. Vijayakumar, Stanislaw Antol, Mohit Bansal, Dhruv Batra, C. Lawrence Zitnick, Devi Parikh
Date: 2015-12-14 00:00:00
Proceeding: CVPR 2016 6

Humor is an integral part of human lives. Despite being tremendously
impactful, it is perhaps surprising that we do not have a detailed
understanding of humor yet. As interactions between humans and AI systems
increase, it is imperative that these systems are taught to understand
subtleties of human expressions such as humor. In this work, we are interested
in the question - what content in a scene causes it to be funny? As a first
step towards understanding visual humor, we analyze the humor manifested in
abstract scenes and design computational models for them. We collect two
datasets of abstract scenes that facilitate the study of humor at both the
scene-level and the object-level. We analyze the funny scenes and explore the
different types of humor depicted in them via human studies. We model two tasks
that we believe demonstrate an understanding of some aspects of visual humor.
The tasks involve predicting the funniness of a scene and altering the
funniness of a scene. We show that our models perform well quantitatively, and
qualitatively through human studies. Our datasets are publicly available.
Rank Ordered Autoencoders
Authors: Paul Bertens
Date: 2016-05-05 00:00:00

A new method for the unsupervised learning of sparse representations using
autoencoders is proposed and implemented by ordering the output of the hidden
units by their activation value and progressively reconstructing the input in
this order. This can be done efficiently in parallel with the use of cumulative
sums and sorting only slightly increasing the computational costs. Minimizing
the difference of this progressive reconstruction with respect to the input can
be seen as minimizing the number of active output units required for the
reconstruction of the input. The model thus learns to reconstruct optimally
using the least number of active output units. This leads to high sparsity
without the need for extra hyperparameters, the amount of sparsity is instead
implicitly learned by minimizing this progressive reconstruction error. Results
of the trained model are given for patches of the CIFAR10 dataset, showing
rapid convergence of features and extremely sparse output activations while
maintaining a minimal reconstruction error and showing extreme robustness to
overfitting. Additionally the reconstruction as function of number of active
units is presented which shows the autoencoder learns a rank order code over
the input where the highest ranked units correspond to the highest decrease in
reconstruction error.
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit
Authors: Chong You, Daniel P. Robinson, Rene Vidal
Date: 2015-07-05 00:00:00
Proceeding: CVPR 2016 6
Tasks: Clustering, Face Clustering, Image Clustering

Subspace clustering methods based on $\ell_1$, $\ell_2$ or nuclear norm
regularization have become very popular due to their simplicity, theoretical
guarantees and empirical success. However, the choice of the regularizer can
greatly impact both theory and practice. For instance, $\ell_1$ regularization
is guaranteed to give a subspace-preserving affinity (i.e., there are no
connections between points from different subspaces) under broad conditions
(e.g., arbitrary subspaces and corrupted data). However, it requires solving a
large scale convex optimization problem. On the other hand, $\ell_2$ and
nuclear norm regularization provide efficient closed form solutions, but
require very strong assumptions to guarantee a subspace-preserving affinity,
e.g., independent subspaces and uncorrupted data. In this paper we study a
subspace clustering method based on orthogonal matching pursuit. We show that
the method is both computationally efficient and guaranteed to give a
subspace-preserving affinity under broad conditions. Experiments on synthetic
data verify our theoretical analysis, and applications in handwritten digit and
face clustering show that our approach achieves the best trade off between
accuracy and efficiency.
Biobjective Performance Assessment with the COCO Platform
Authors: Dimo Brockhoff, Tea Tušar, Dejan Tušar, Tobias Wagner, Nikolaus Hansen, Anne Auger
Date: 2016-05-05 00:00:00

This document details the rationales behind assessing the performance of
numerical black-box optimizers on multi-objective problems within the COCO
platform and in particular on the biobjective test suite bbob-biobj. The
evaluation is based on a hypervolume of all non-dominated solutions in the
archive of candidate solutions and measures the runtime until the hypervolume
value succeeds prescribed target values.
Improving Automated Patent Claim Parsing: Dataset, System, and Experiments
Authors: Mengke Hu, David Cinciruk, John MacLaren Walsh
Date: 2016-05-05 00:00:00
Tasks: Articles, TAG

Off-the-shelf natural language processing software performs poorly when
parsing patent claims owing to their use of irregular language relative to the
corpora built from news articles and the web typically utilized to train this
software. Stopping short of the extensive and expensive process of accumulating
a large enough dataset to completely retrain parsers for patent claims, a
method of adapting existing natural language processing software towards patent
claims via forced part of speech tag correction is proposed. An Amazon
Mechanical Turk collection campaign organized to generate a public corpus to
train such an improved claim parsing system is discussed, identifying lessons
learned during the campaign that can be of use in future NLP dataset collection
campaigns with AMT. Experiments utilizing this corpus and other patent claim
sets measure the parsing performance improvement garnered via the claim parsing
system. Finally, the utility of the improved claim parsing system within other
patent processing applications is demonstrated via experiments showing improved
automated patent subject classification when the new claim parsing system is
utilized to generate the features.
A note on adjusting $R^2$ for using with cross-validation
Authors: Indre Zliobaite, Nikolaj Tatti
Date: 2016-05-05 00:00:00

We show how to adjust the coefficient of determination ($R^2$) when used for
measuring predictive accuracy via leave-one-out cross-validation.
Brain Emotional Learning-Based Prediction Model (For Long-Term Chaotic Prediction Applications)
Authors: Mahboobeh Parsapoor
Date: 2016-05-05 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

This study suggests a new prediction model for chaotic time series inspired
by the brain emotional learning of mammals. We describe the structure and
function of this model, which is referred to as BELPM (Brain Emotional
Learning-Based Prediction Model). Structurally, the model mimics the connection
between the regions of the limbic system, and functionally it uses weighted k
nearest neighbors to imitate the roles of those regions. The learning algorithm
of BELPM is defined using steepest descent (SD) and the least square estimator
(LSE). Two benchmark chaotic time series, Lorenz and Henon, have been used to
evaluate the performance of BELPM. The obtained results have been compared with
those of other prediction methods. The results show that BELPM has the
capability to achieve a reasonable accuracy for long-term prediction of chaotic
time series, using a limited amount of training data and a reasonably low
computational time.
Phase 2: DCL System Using Deep Learning Approaches for Land-based or Ship-based Real-Time Recognition and Localization of Marine Mammals - Machine Learning Detection Algorithms
Authors: Peter J. Dugan, Christopher W. Clark, Yann André LeCun, Sofie M. Van Parijs
Date: 2016-05-03 00:00:00
Tasks: General Classification

Overarching goals for this work aim to advance the state of the art for
detection, classification and localization (DCL) in the field of bioacoustics.
This goal is primarily achieved by building a generic framework for
detection-classification (DC) using a fast, efficient and scalable
architecture, demonstrating the capabilities of this system using on a variety
of low-frequency mid-frequency cetacean sounds. Two primary goals are to
develop transferable technologies for detection and classification in, one: the
area of advanced algorithms, such as deep learning and other methods; and two:
advanced systems, capable of real-time and archival processing. For each key
area, we will focus on producing publications from this work and providing
tools and software to the community where/when possible. Currently massive
amounts of acoustic data are being collected by various institutions,
corporations and national defense agencies. The long-term goal is to provide
technical capability to analyze the data using automatic algorithms for (DC)
based on machine intelligence. The goal of the automation is to provide
effective and efficient mechanisms by which to process large acoustic datasets
for understanding the bioacoustic behaviors of marine mammals. This capability
will provide insights into the potential ecological impacts and influences of
anthropogenic ocean sounds. This work focuses on building technologies using a
maturity model based on DARPA 6.1 and 6.2 processes, for basic and applied
research, respectively.
Learning Action Maps of Large Environments via First-Person Vision
Authors: Nicholas Rhinehart, Kris M. Kitani
Date: 2016-05-05 00:00:00
Proceeding: CVPR 2016 6

When people observe and interact with physical spaces, they are able to
associate functionality to regions in the environment. Our goal is to automate
dense functional understanding of large spaces by leveraging sparse activity
demonstrations recorded from an ego-centric viewpoint. The method we describe
enables functionality estimation in large scenes where people have behaved, as
well as novel scenes where no behaviors are observed. Our method learns and
predicts "Action Maps", which encode the ability for a user to perform
activities at various locations. With the usage of an egocentric camera to
observe human activities, our method scales with the size of the scene without
the need for mounting multiple static surveillance cameras and is well-suited
to the task of observing activities up-close. We demonstrate that by capturing
appearance-based attributes of the environment and associating these attributes
with activity demonstrations, our proposed mathematical framework allows for
the prediction of Action Maps in new environments. Additionally, we offer a
preliminary glance of the applicability of Action Maps by demonstrating a
proof-of-concept application in which they are used in concert with activity
detections to perform localization.
Parallels of human language in the behavior of bottlenose dolphins
Authors: R. Ferrer-i-Cancho, D. Lusseau, B. McCowan
Date: 2016-05-05 00:00:00

A short review of similarities between dolphins and humans with the help of quantitative linguistics and information theory.
Stance and Sentiment in Tweets
Authors: Saif M. Mohammad, Parinaz Sobhani, Svetlana Kiritchenko
Date: 2016-05-05 00:00:00
Tasks: General Classification, Stance Classification, Stance Detection, Word Embeddings

We can often detect from a person's utterances whether he/she is in favor of
or against a given target entity -- their stance towards the target. However, a
person may express the same stance towards a target by using negative or
positive language. Here for the first time we present a dataset of
tweet--target pairs annotated for both stance and sentiment. The targets may or
may not be referred to in the tweets, and they may or may not be the target of
opinion in the tweets. Partitions of this dataset were used as training and
test sets in a SemEval-2016 shared task competition. We propose a simple stance
detection system that outperforms submissions from all 19 teams that
participated in the shared task. Additionally, access to both stance and
sentiment annotations allows us to explore several research questions. We show
that while knowing the sentiment expressed by a tweet is beneficial for stance
classification, it alone is not sufficient. Finally, we use additional
unlabeled data through distant supervision techniques and word embeddings to
further improve stance classification.
LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues
Authors: Phong Le, Marc Dymetman, Jean-Michel Renders
Date: 2016-05-05 00:00:00
Proceeding: WS 2016 8
Tasks: Language Modeling, Language Modelling, Mixture-of-Experts, Question Answering

We introduce an LSTM-based method for dynamically integrating several
word-prediction experts to obtain a conditional language model which can be
good simultaneously at several subtasks. We illustrate this general approach
with an application to dialogue where we integrate a neural chat model, good at
conversational aspects, with a neural question-answering model, good at
retrieving precise information from a knowledge-base, and show how the
integration combines the strengths of the independent components. We hope that
this focused contribution will attract attention on the benefits of using such
mixtures of experts in NLP.
The IBM Speaker Recognition System: Recent Advances and Error Analysis
Authors: Seyed Omid Sadjadi, Jason Pelecanos, Sriram Ganapathy
Date: 2016-05-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speaker Recognition, speech-recognition, Speech Recognition

We present the recent advances along with an error analysis of the IBM
speaker recognition system for conversational speech. Some of the key
advancements that contribute to our system include: a nearest-neighbor
discriminant analysis (NDA) approach (as opposed to LDA) for intersession
variability compensation in the i-vector space, the application of speaker and
channel-adapted features derived from an automatic speech recognition (ASR)
system for speaker recognition, and the use of a DNN acoustic model with a very
large number of output units (~10k senones) to compute the frame-level soft
alignments required in the i-vector estimation process. We evaluate these
techniques on the NIST 2010 SRE extended core conditions (C1-C9), as well as
the 10sec-10sec condition. To our knowledge, results achieved by our system
represent the best performances published to date on these conditions. For
example, on the extended tel-tel condition (C5) the system achieves an EER of
0.59%. To garner further understanding of the remaining errors (on C5), we
examine the recordings associated with the low scoring target trials, where
various issues are identified for the problematic recordings/trials.
Interestingly, it is observed that correcting the pathological recordings not
only improves the scores for the target trials but also for the nontarget
trials.
On the Convergence of A Family of Robust Losses for Stochastic Gradient Descent
Authors: Bo Han, Ivor W. Tsang, Ling Chen
Date: 2016-05-05 00:00:00

The convergence of Stochastic Gradient Descent (SGD) using convex loss
functions has been widely studied. However, vanilla SGD methods using convex
losses cannot perform well with noisy labels, which adversely affect the update
of the primal variable in SGD methods. Unfortunately, noisy labels are
ubiquitous in real world applications such as crowdsourcing. To handle noisy
labels, in this paper, we present a family of robust losses for SGD methods. By
employing our robust losses, SGD methods successfully reduce negative effects
caused by noisy labels on each update of the primal variable. We not only
reveal that the convergence rate is O(1/T) for SGD methods using robust losses,
but also provide the robustness analysis on two representative robust losses.
Comprehensive experimental results on six real-world datasets show that SGD
methods using robust losses are obviously more robust than other baseline
methods in most situations with fast convergence.
Improving abcdSAT by At-Least-One Recently Used Clause Management Strategy
Authors: Jingchao Chen
Date: 2016-05-05 00:00:00
Tasks: Management

We improve further the 2015 version of abcdSAT by various heuristics such as
at-least-one recently used strategy, learnt clause database approximation
reduction etc. Based on the requirement of different tracks at the SAT
Competition 2016, we develop three versions of abcdSAT: drup, inc and lim,
which participate in the competition of main (agile), incremental library and
no-limit track, respectively.
MatConvNet - Convolutional Neural Networks for MATLAB
Authors: Andrea Vedaldi, Karel Lenc
Date: 2014-12-15 00:00:00
Tasks: CPU, GPU

MatConvNet is an implementation of Convolutional Neural Networks (CNNs) for
MATLAB. The toolbox is designed with an emphasis on simplicity and flexibility.
It exposes the building blocks of CNNs as easy-to-use MATLAB functions,
providing routines for computing linear convolutions with filter banks, feature
pooling, and many more. In this manner, MatConvNet allows fast prototyping of
new CNN architectures; at the same time, it supports efficient computation on
CPU and GPU allowing to train complex models on large datasets such as ImageNet
ILSVRC. This document provides an overview of CNNs and how they are implemented
in MatConvNet and gives the technical details of each computational block in
the toolbox.
Patch-based Texture Synthesis for Image Inpainting
Authors: Tao Zhou, Brian Johnson, Rui Li
Date: 2016-05-05 00:00:00
Tasks: Image Inpainting, Image Retrieval, Retrieval, Texture Synthesis

Image inpaiting is an important task in image processing and vision. In this
paper, we develop a general method for patch-based image inpainting by
synthesizing new textures from existing one. A novel framework is introduced to
find several optimal candidate patches and generate a new texture patch in the
process. We form it as an optimization problem that identifies the potential
patches for synthesis from an coarse-to-fine manner. We use the texture
descriptor as a clue in searching for matching patches from the known region.
To ensure the structure faithful to the original image, a geometric constraint
metric is formally defined that is applied directly to the patch synthesis
procedure. We extensively conducted our experiments on a wide range of testing
images on various scenarios and contents by arbitrarily specifying the target
the regions for inference followed by using existing evaluation metrics to
verify its texture coherency and structural consistency. Our results
demonstrate the high accuracy and desirable output that can be potentially used
for numerous applications: object removal, background subtraction, and image
retrieval.
Observational-Interventional Priors for Dose-Response Learning
Authors: Ricardo Silva
Date: 2016-05-05 00:00:00
Proceeding: NeurIPS 2016 12

Controlled interventions provide the most direct source of information for
learning causal effects. In particular, a dose-response curve can be learned by
varying the treatment level and observing the corresponding outcomes. However,
interventions can be expensive and time-consuming. Observational data, where
the treatment is not controlled by a known mechanism, is sometimes available.
Under some strong assumptions, observational data allows for the estimation of
dose-response curves. Estimating such curves nonparametrically is hard: sample
sizes for controlled interventions may be small, while in the observational
case a large number of measured confounders may need to be marginalized. In
this paper, we introduce a hierarchical Gaussian process prior that constructs
a distribution over the dose-response curve by learning from observational
data, and reshapes the distribution with a nonparametric affine transform
learned from controlled interventions. This function composition from different
sources is shown to speed-up learning, which we demonstrate with a thorough
sensitivity analysis and an application to modeling the effect of therapy on
cognitive skills of premature infants.
Classification of Human Whole-Body Motion using Hidden Markov Models
Authors: Matthias Plappert
Date: 2016-05-05 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Human motion plays an important role in many fields. Large databases exist
that store and make available recordings of human motions. However, annotating
each motion with multiple labels is a cumbersome and error-prone process. This
bachelor's thesis presents different approaches to solve the multi-label
classification problem using Hidden Markov Models (HMMs). First, different
features that can be directly obtained from the raw data are introduced. Next,
additional features are derived to improve classification performance. These
features are then used to perform the multi-label classification using two
different approaches. The first approach simply transforms the multi-label
problem into a multi-class problem. The second, novel approach solves the same
problem without the need to construct a transformation by predicting the labels
directly from the likelihood scores. The second approach scales linearly with
the number of labels whereas the first approach is subject to combinatorial
explosion. All aspects of the classification process are evaluated on a data
set that consists of 454 motions. System 1 achieves an accuracy of 98.02% and
system 2 an accuracy of 93.39% on the test set.