pwc-archive/papers-with-abstracts huggingface dataset:
Fast Algorithms for Segmented Regression
Authors: Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
Date: 2016-07-14 00:00:00
Tasks: regression

We study the fixed design segmented regression problem: Given noisy samples
from a piecewise linear function $f$, we want to recover $f$ up to a desired
accuracy in mean-squared error.
  Previous rigorous approaches for this problem rely on dynamic programming
(DP) and, while sample efficient, have running time quadratic in the sample
size. As our main contribution, we provide new sample near-linear time
algorithms for the problem that -- while not being minimax optimal -- achieve a
significantly better sample-time tradeoff on large datasets compared to the DP
approach. Our experimental evaluation shows that, compared with the DP
approach, our algorithms provide a convergence rate that is only off by a
factor of $2$ to $4$, while achieving speedups of three orders of magnitude.
Tight Lower Bounds for Multiplicative Weights Algorithmic Families
Authors: Nick Gravin, Yuval Peres, Balasubramanian Sivan
Date: 2016-07-11 00:00:00

We study the fundamental problem of prediction with expert advice and develop
regret lower bounds for a large family of algorithms for this problem. We
develop simple adversarial primitives, that lend themselves to various
combinations leading to sharp lower bounds for many algorithmic families. We
use these primitives to show that the classic Multiplicative Weights Algorithm
(MWA) has a regret of $\sqrt{\frac{T \ln k}{2}}$, there by completely closing
the gap between upper and lower bounds. We further show a regret lower bound of
$\frac{2}{3}\sqrt{\frac{T\ln k}{2}}$ for a much more general family of
algorithms than MWA, where the learning rate can be arbitrarily varied over
time, or even picked from arbitrary distributions over time. We also use our
primitives to construct adversaries in the geometric horizon setting for MWA to
precisely characterize the regret at $\frac{0.391}{\sqrt{\delta}}$ for the case
of $2$ experts and a lower bound of $\frac{1}{2}\sqrt{\frac{\ln k}{2\delta}}$
for the case of arbitrary number of experts $k$.
Hierarchical Bayesian Noise Inference for Robust Real-time Probabilistic Object Classification
Authors: Shayegan Omidshafiei, Brett T. Lopez, Jonathan P. How, John Vian
Date: 2016-05-03 00:00:00
Tasks: Classification, Decision Making, General Classification, Object, object-detection, Object Detection

Robust environment perception is essential for decision-making on robots
operating in complex domains. Principled treatment of uncertainty sources in a
robot's observation model is necessary for accurate mapping and object
detection. This is important not only for low-level observations (e.g.,
accelerometer data), but for high-level observations such as semantic object
labels as well. This paper presents an approach for filtering sequences of
object classification probabilities using online modeling of the noise
characteristics of the classifier outputs. A hierarchical Bayesian approach is
used to model per-class noise distributions, while simultaneously allowing
sharing of high-level noise characteristics between classes. The proposed
filtering scheme, called Hierarchical Bayesian Noise Inference (HBNI), is shown
to outperform classification accuracy of existing methods. The paper also
presents real-time filtered classification hardware experiments running fully
onboard a moving quadrotor, where the proposed approach is demonstrated to work
in a challenging domain where noise-agnostic filtering fails.
Gland Instance Segmentation by Deep Multichannel Side Supervision
Authors: Yan Xu, Yang Li, Mingyuan Liu, Yipei Wang, Maode Lai, Eric I-Chao Chang
Date: 2016-07-12 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

In this paper, we propose a new image instance segmentation method that
segments individual glands (instances) in colon histology images. This is a
task called instance segmentation that has recently become increasingly
important. The problem is challenging since not only do the glands need to be
segmented from the complex background, they are also required to be
individually identified. Here we leverage the idea of image-to-image prediction
in recent deep learning by building a framework that automatically exploits and
fuses complex multichannel information, regional and boundary patterns, with
side supervision (deep supervision on side responses) in gland histology
images. Our proposed system, deep multichannel side supervision (DMCS),
alleviates heavy feature design due to the use of convolutional neural networks
guided by side supervision. Compared to methods reported in the 2015 MICCAI
Gland Segmentation Challenge, we observe state-of-the-art results based on a
number of evaluation metrics.
Resource Planning For Rescue Operations
Authors: Mona Khaffaf, Arshia Khaffaf
Date: 2016-07-14 00:00:00

After an earthquake, disaster sites pose a multitude of health and safety
concerns. A rescue operation of people trapped in the ruins after an earthquake
disaster requires a series of intelligent behavior, including planning. For a
successful rescue operation, given a limited number of available actions and
regulations, the role of planning in rescue operations is crucial. Fortunately,
recent developments in automated planning by artificial intelligence community
can help different organization in this crucial task. Due to the number of
rules and regulations, we believe that a rule based system for planning can be
helpful for this specific planning problem. In this research work, we use logic
rules to represent rescue and related regular regulations, together with a
logic based planner to solve this complicated problem. Although this research
is still in the prototyping and modeling stage, it clearly shows that rule
based languages can be a good infrastructure for this computational task. The
results of this research can be used by different organizations, such as
Iranian Red Crescent Society and International Institute of Seismology and
Earthquake Engineering (IISEE).
Automatic Generation of Probabilistic Programming from Time Series Data
Authors: Anh Tong, Jaesik Choi
Date: 2016-07-04 00:00:00
Tasks: Descriptive, Probabilistic Programming, regression, Time Series, Time Series Analysis

Probabilistic programming languages represent complex data with intermingled
models in a few lines of code. Efficient inference algorithms in probabilistic
programming languages make possible to build unified frameworks to compute
interesting probabilities of various large, real-world problems. When the
structure of model is given, constructing a probabilistic program is rather
straightforward. Thus, main focus have been to learn the best model parameters
and compute marginal probabilities. In this paper, we provide a new perspective
to build expressive probabilistic program from continue time series data when
the structure of model is not given. The intuition behind of our method is to
find a descriptive covariance structure of time series data in nonparametric
Gaussian process regression. We report that such descriptive covariance
structure efficiently derives a probabilistic programming description
accurately.
Concatenated image completion via tensor augmentation and completion
Authors: Johann A. Bengua, Hoang D. Tuan, Ho N. Phien, Minh N. Do
Date: 2016-07-14 00:00:00

This paper proposes a novel framework called concatenated image completion
via tensor augmentation and completion (ICTAC), which recovers missing entries
of color images with high accuracy. Typical images are second- or third-order
tensors (2D/3D) depending if they are grayscale or color, hence tensor
completion algorithms are ideal for their recovery. The proposed framework
performs image completion by concatenating copies of a single image that has
missing entries into a third-order tensor, applying a dimensionality
augmentation technique to the tensor, utilizing a tensor completion algorithm
for recovering its missing entries, and finally extracting the recovered image
from the tensor. The solution relies on two key components that have been
recently proposed to take advantage of the tensor train (TT) rank: A tensor
augmentation tool called ket augmentation (KA) that represents a low-order
tensor by a higher-order tensor, and the algorithm tensor completion by
parallel matrix factorization via tensor train (TMac-TT), which has been
demonstrated to outperform state-of-the-art tensor completion algorithms.
Simulation results for color image recovery show the clear advantage of our
framework against current state-of-the-art tensor completion algorithms.
Ensemble preconditioning for Markov chain Monte Carlo simulation
Authors: Charles Matthews, Jonathan Weare, Benedict Leimkuhler
Date: 2016-07-13 00:00:00

We describe parallel Markov chain Monte Carlo methods that propagate a
collective ensemble of paths, with local covariance information calculated from
neighboring replicas. The use of collective dynamics eliminates multiplicative
noise and stabilizes the dynamics thus providing a practical approach to
difficult anisotropic sampling problems in high dimensions. Numerical
experiments with model problems demonstrate that dramatic potential speedups,
compared to various alternative schemes, are attainable.
Recurrent Neural Networks for Dialogue State Tracking
Authors: Ondřej Plátek, Petr Bělohlávek, Vojtěch Hudeček, Filip Jurčíček
Date: 2016-06-28 00:00:00
Tasks: Dialogue State Tracking

This paper discusses models for dialogue state tracking using recurrent
neural networks (RNN). We present experiments on the standard dialogue state
tracking (DST) dataset, DSTC2. On the one hand, RNN models became the state of
the art models in DST, on the other hand, most state-of-the-art models are only
turn-based and require dataset-specific preprocessing (e.g. DSTC2-specific) in
order to achieve such results. We implemented two architectures which can be
used in incremental settings and require almost no preprocessing. We compare
their performance to the benchmarks on DSTC2 and discuss their properties. With
only trivial preprocessing, the performance of our models is close to the
state-of- the-art results.
Optimally-Weighted Herding is Bayesian Quadrature
Authors: Ferenc Huszar, David Duvenaud
Date: 2014-08-09 00:00:00

Herding and kernel herding are deterministic methods of choosing samples
which summarise a probability distribution. A related task is choosing samples
for estimating integrals using Bayesian quadrature. We show that the criterion
minimised when selecting samples in kernel herding is equivalent to the
posterior variance in Bayesian quadrature. We then show that sequential
Bayesian quadrature can be viewed as a weighted version of kernel herding which
achieves performance superior to any other weighted herding method. We
demonstrate empirically a rate of convergence faster than O(1/N). Our results
also imply an upper bound on the empirical error of the Bayesian quadrature
estimate.
Actionable and Political Text Classification using Word Embeddings and LSTM
Authors: Adithya Rao, Nemanja Spasojevic
Date: 2016-07-08 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification, Word Embeddings

In this work, we apply word embeddings and neural networks with Long
Short-Term Memory (LSTM) to text classification problems, where the
classification criteria are decided by the context of the application. We
examine two applications in particular. The first is that of Actionability,
where we build models to classify social media messages from customers of
service providers as Actionable or Non-Actionable. We build models for over 30
different languages for actionability, and most of the models achieve accuracy
around 85%, with some reaching over 90% accuracy. We also show that using LSTM
neural networks with word embeddings vastly outperform traditional techniques.
Second, we explore classification of messages with respect to political
leaning, where social media messages are classified as Democratic or
Republican. The model is able to classify messages with a high accuracy of
87.57%. As part of our experiments, we vary different hyperparameters of the
neural networks, and report the effect of such variation on the accuracy. These
actionability models have been deployed to production and help company agents
provide customer support by prioritizing which messages to respond to. The
model for political leaning has been opened and made available for wider use.
Tie-breaker: Using language models to quantify gender bias in sports journalism
Authors: Liye Fu, Cristian Danescu-Niculescu-Mizil, Lillian Lee
Date: 2016-07-13 00:00:00
Tasks: Language Modeling, Language Modelling

Gender bias is an increasingly important issue in sports journalism. In this
work, we propose a language-model-based approach to quantify differences in
questions posed to female vs. male athletes, and apply it to tennis post-match
interviews. We find that journalists ask male players questions that are
generally more focused on the game when compared with the questions they ask
their female counterparts. We also provide a fine-grained analysis of the
extent to which the salience of this bias depends on various factors, such as
question type, game outcome or player rank.
Differentiable Pooling for Unsupervised Acoustic Model Adaptation
Authors: Pawel Swietojanski, Steve Renals
Date: 2016-03-31 00:00:00
Tasks: model, speech-recognition, Speech Recognition

We present a deep neural network (DNN) acoustic model that includes
parametrised and differentiable pooling operators. Unsupervised acoustic model
adaptation is cast as the problem of updating the decision boundaries
implemented by each pooling operator. In particular, we experiment with two
types of pooling parametrisations: learned $L_p$-norm pooling and weighted
Gaussian pooling, in which the weights of both operators are treated as
speaker-dependent. We perform investigations using three different large
vocabulary speech recognition corpora: AMI meetings, TED talks and Switchboard
conversational telephone speech. We demonstrate that differentiable pooling
operators provide a robust and relatively low-dimensional way to adapt acoustic
models, with relative word error rates reductions ranging from 5--20% with
respect to unadapted systems, which themselves are better than the baseline
fully-connected DNN-based acoustic models. We also investigate how the proposed
techniques work under various adaptation conditions including the quality of
adaptation data and complementarity to other feature- and model-space
adaptation methods, as well as providing an analysis of the characteristics of
each of the proposed approaches.
Safe Policy Improvement by Minimizing Robust Baseline Regret
Authors: Marek Petrik, Yin-Lam Chow, Mohammad Ghavamzadeh
Date: 2016-07-13 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Decision Making, Decision Making Under Uncertainty, Sequential Decision Making

An important problem in sequential decision-making under uncertainty is to
use limited data to compute a safe policy, i.e., a policy that is guaranteed to
perform at least as well as a given baseline strategy. In this paper, we
develop and analyze a new model-based approach to compute a safe policy when we
have access to an inaccurate dynamics model of the system with known accuracy
guarantees. Our proposed robust method uses this (inaccurate) model to directly
minimize the (negative) regret w.r.t. the baseline policy. Contrary to the
existing approaches, minimizing the regret allows one to improve the baseline
policy in states with accurate dynamics and seamlessly fall back to the
baseline policy, otherwise. We show that our formulation is NP-hard and propose
an approximate algorithm. Our empirical results on several domains show that
even this relatively simple approximate algorithm can significantly outperform
standard approaches.
Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation
Authors: Pawel Swietojanski, Jinyu Li, Steve Renals
Date: 2016-01-12 00:00:00
Tasks: speech-recognition, Speech Recognition

This work presents a broad study on the adaptation of neural network acoustic
models by means of learning hidden unit contributions (LHUC) -- a method that
linearly re-combines hidden units in a speaker- or environment-dependent manner
using small amounts of unsupervised adaptation data. We also extend LHUC to a
speaker adaptive training (SAT) framework that leads to a more adaptable DNN
acoustic model, working both in a speaker-dependent and a speaker-independent
manner, without the requirements to maintain auxiliary speaker-dependent
feature extractors or to introduce significant speaker-dependent changes to the
DNN structure. Through a series of experiments on four different speech
recognition benchmarks (TED talks, Switchboard, AMI meetings, and Aurora4)
comprising 270 test speakers, we show that LHUC in both its test-only and SAT
variants results in consistent word error rate reductions ranging from 5% to
23% relative depending on the task and the degree of mismatch between training
and test data. In addition, we have investigated the effect of the amount of
adaptation data per speaker, the quality of unsupervised adaptation targets,
the complementarity to other adaptation techniques, one-shot adaptation, and an
extension to adapting DNNs trained in a sequence discriminative manner.
The KIT Motion-Language Dataset
Authors: Matthias Plappert, Christian Mandery, Tamim Asfour
Date: 2016-07-13 00:00:00

Linking human motion and natural language is of great interest for the
generation of semantic representations of human activities as well as for the
generation of robot activities based on natural language input. However, while
there have been years of research in this area, no standardized and openly
available dataset exists to support the development and evaluation of such
systems. We therefore propose the KIT Motion-Language Dataset, which is large,
open, and extensible. We aggregate data from multiple motion capture databases
and include them in our dataset using a unified representation that is
independent of the capture system or marker set, making it easy to work with
the data regardless of its origin. To obtain motion annotations in natural
language, we apply a crowd-sourcing approach and a web-based tool that was
specifically build for this purpose, the Motion Annotation Tool. We thoroughly
document the annotation process itself and discuss gamification methods that we
used to keep annotators motivated. We further propose a novel method,
perplexity-based selection, which systematically selects motions for further
annotation that are either under-represented in our dataset or that have
erroneous annotations. We show that our method mitigates the two aforementioned
problems and ensures a systematic annotation process. We provide an in-depth
analysis of the structure and contents of our resulting dataset, which, as of
October 10, 2016, contains 3911 motions with a total duration of 11.23 hours
and 6278 annotations in natural language that contain 52,903 words. We believe
this makes our dataset an excellent choice that enables more transparent and
comparable research in this important area.
Feature Extraction and Automated Classification of Heartbeats by Machine Learning
Authors: Choudur Lakshminarayan, Tony Basil
Date: 2016-07-13 00:00:00
Tasks: Arrhythmia Detection, BIG-bench Machine Learning, Classification, feature selection, General Classification

We present algorithms for the detection of a class of heart arrhythmias with
the goal of eventual adoption by practicing cardiologists. In clinical
practice, detection is based on a small number of meaningful features extracted
from the heartbeat cycle. However, techniques proposed in the literature use
high dimensional vectors consisting of morphological, and time based features
for detection. Using electrocardiogram (ECG) signals, we found smaller subsets
of features sufficient to detect arrhythmias with high accuracy. The features
were found by an iterative step-wise feature selection method. We depart from
common literature in the following aspects: 1. As opposed to a high dimensional
feature vectors, we use a small set of features with meaningful clinical
interpretation, 2. we eliminate the necessity of short-duration
patient-specific ECG data to append to the global training data for
classification 3. We apply semi-parametric classification procedures (in an
ensemble framework) for arrhythmia detection, and 4. our approach is based on a
reduced sampling rate of ~ 115 Hz as opposed to 360 Hz in standard literature.
Kernel Density Estimation for Dynamical Systems
Authors: Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A. K. Suykens
Date: 2016-07-13 00:00:00
Tasks: Density Estimation

We study the density estimation problem with observations generated by
certain dynamical systems that admit a unique underlying invariant Lebesgue
density. Observations drawn from dynamical systems are not independent and
moreover, usual mixing concepts may not be appropriate for measuring the
dependence among these observations. By employing the $\mathcal{C}$-mixing
concept to measure the dependence, we conduct statistical analysis on the
consistency and convergence of the kernel density estimator. Our main results
are as follows: First, we show that with properly chosen bandwidth, the kernel
density estimator is universally consistent under $L_1$-norm; Second, we
establish convergence rates for the estimator with respect to several classes
of dynamical systems under $L_1$-norm. In the analysis, the density function
$f$ is only assumed to be H\"{o}lder continuous which is a weak assumption in
the literature of nonparametric density estimation and also more realistic in
the dynamical system context. Last but not least, we prove that the same
convergence rates of the estimator under $L_\infty$-norm and $L_1$-norm can be
achieved when the density function is H\"{o}lder continuous, compactly
supported and bounded. The bandwidth selection problem of the kernel density
estimator for dynamical system is also discussed in our study via numerical
simulations.
Application of Convolutional Neural Network for Image Classification on Pascal VOC Challenge 2012 dataset
Authors: Suyash Shetty
Date: 2016-07-13 00:00:00
Tasks: General Classification, GPU, image-classification, Image Classification

In this project we work on creating a model to classify images for the Pascal
VOC Challenge 2012. We use convolutional neural networks trained on a single
GPU instance provided by Amazon via their cloud service Amazon Web Services
(AWS) to classify images in the Pascal VOC 2012 data set. We train multiple
convolutional neural network models and finally settle on the best model which
produced a validation accuracy of 85.6% and a testing accuracy of 85.24%.
A Vector Space for Distributional Semantics for Entailment
Authors: James Henderson, Diana Nicoleta Popa
Date: 2016-07-13 00:00:00
Proceeding: ACL 2016 8
Tasks: Lexical Entailment, Semantic Similarity, Semantic Textual Similarity

Distributional semantics creates vector-space representations that capture
many forms of semantic similarity, but their relation to semantic entailment
has been less clear. We propose a vector-space model which provides a formal
foundation for a distributional semantics of entailment. Using a mean-field
approximation, we develop approximate inference procedures and entailment
operators over vectors of probabilities of features being known (versus
unknown). We use this framework to reinterpret an existing
distributional-semantic model (Word2Vec) as approximating an entailment-based
model of the distributions of words in contexts, thereby predicting lexical
entailment relations. In both unsupervised and semi-supervised experiments on
hyponymy detection, we get substantial improvements over previous results.