pwc-archive/papers-with-abstracts huggingface dataset:
An On-line Variational Bayesian Model for Multi-Person Tracking from Cluttered Scenes
Authors: Sileye . Ba, Xavier Alameda-Pineda, Alessio Xompero, Radu Horaud
Date: 2015-09-04 00:00:00
Tasks: Multiple Object Tracking, Object, Object Tracking

Object tracking is an ubiquitous problem that appears in many applications
such as remote sensing, audio processing, computer vision, human-machine
interfaces, human-robot interaction, etc. Although thoroughly investigated in
computer vision, tracking a time-varying number of persons remains a
challenging open problem. In this paper, we propose an on-line variational
Bayesian model for multi-person tracking from cluttered visual observations
provided by person detectors. The contributions of this paper are the
followings. First, we propose a variational Bayesian framework for tracking an
unknown and varying number of persons. Second, our model results in a
variational expectation-maximization (VEM) algorithm with closed-form
expressions for the posterior distributions of the latent variables and for the
estimation of the model parameters. Third, the proposed model exploits
observations from multiple detectors, and it is therefore multimodal by nature.
Finally, we propose to embed both object-birth and object-visibility processes
in an effort to robustly handle person appearances and disappearances over
time. Evaluated on classical multiple person tracking datasets, our method
shows competitive results with respect to state-of-the-art multiple-object
tracking models, such as the probability hypothesis density (PHD) filter among
others.
Swift: Compiled Inference for Probabilistic Programming Languages
Authors: Yi Wu, Lei LI, Stuart Russell, Rastislav Bodik
Date: 2016-06-30 00:00:00
Tasks: Management, Probabilistic Programming

A probabilistic program defines a probability measure over its semantic
structures. One common goal of probabilistic programming languages (PPLs) is to
compute posterior probabilities for arbitrary models and queries, given
observed evidence, using a generic inference engine. Most PPL inference
engines---even the compiled ones---incur significant runtime interpretation
overhead, especially for contingent and open-universe models. This paper
describes Swift, a compiler for the BLOG PPL. Swift-generated code incorporates
optimizations that eliminate interpretation overhead, maintain dynamic
dependencies efficiently, and handle memory management for possible worlds of
varying sizes. Experiments comparing Swift with other PPL engines on a variety
of inference problems demonstrate speedups ranging from 12x to 326x.
On Approximate Dynamic Programming with Multivariate Splines for Adaptive Control
Authors: Willem Eerland, Coen de Visser, Erik-Jan van Kampen
Date: 2016-06-30 00:00:00

We define a SDP framework based on the RLSTD algorithm and multivariate
simplex B-splines. We introduce a local forget factor capable of preserving the
continuity of the simplex splines. This local forget factor is integrated with
the RLSTD algorithm, resulting in a modified RLSTD algorithm that is capable of
tracking time-varying systems. We present the results of two numerical
experiments, one validating SDP and comparing it with NDP and another to show
the advantages of the modified RLSTD algorithm over the original. While SDP
requires more computations per time-step, the experiment shows that for the
same amount of function approximator parameters, there is an increase in
performance in terms of stability and learning rate compared to NDP. The second
experiment shows that SDP in combination with the modified RLSTD algorithm
allows for faster recovery compared to the original RLSTD algorithm when system
parameters are altered, paving the way for an adaptive high-performance
non-linear control method.
Recurrent neural network models for disease name recognition using domain invariant features
Authors: Sunil Kumar Sahu, Ashish Anand
Date: 2016-06-30 00:00:00
Proceeding: ACL 2016 8
Tasks: Feature Engineering, General Classification, Sentence

Hand-crafted features based on linguistic and domain-knowledge play crucial
role in determining the performance of disease name recognition systems. Such
methods are further limited by the scope of these features or in other words,
their ability to cover the contexts or word dependencies within a sentence. In
this work, we focus on reducing such dependencies and propose a
domain-invariant framework for the disease name recognition task. In
particular, we propose various end-to-end recurrent neural network (RNN) models
for the tasks of disease name recognition and their classification into four
pre-defined categories. We also utilize convolution neural network (CNN) in
cascade of RNN to get character-based embedded features and employ it with
word-embedded features in our model. We compare our models with the
state-of-the-art results for the two tasks on NCBI disease dataset. Our results
for the disease mention recognition task indicate that state-of-the-art
performance can be obtained without relying on feature engineering. Further the
proposed models obtained improved performance on the classification task of
disease names.
Relation extraction from clinical texts using domain invariant convolutional neural network
Authors: Sunil Kumar Sahu, Ashish Anand, Krishnadev Oruganty, Mahanandeeshwar Gattu
Date: 2016-06-30 00:00:00
Proceeding: WS 2016 8
Tasks: Articles, Feature Engineering, Relation, Relation Extraction

In recent years extracting relevant information from biomedical and clinical
texts such as research articles, discharge summaries, or electronic health
records have been a subject of many research efforts and shared challenges.
Relation extraction is the process of detecting and classifying the semantic
relation among entities in a given piece of texts. Existing models for this
task in biomedical domain use either manually engineered features or kernel
methods to create feature vector. These features are then fed to classifier for
the prediction of the correct class. It turns out that the results of these
methods are highly dependent on quality of user designed features and also
suffer from curse of dimensionality. In this work we focus on extracting
relations from clinical discharge summaries. Our main objective is to exploit
the power of convolution neural network (CNN) to learn features automatically
and thus reduce the dependency on manual feature engineering. We evaluate
performance of the proposed model on i2b2-2010 clinical relation extraction
challenge dataset. Our results indicate that convolution neural network can be
a good model for relation exaction in clinical text without being dependent on
expert's knowledge on defining quality features.
Parking Stall Vacancy Indicator System Based on Deep Convolutional Neural Networks
Authors: Sepehr Valipour, Mennatullah Siam, Eleni Stroulia, Martin Jagersand
Date: 2016-06-30 00:00:00
Tasks: Management

Parking management systems, and vacancy-indication services in particular,
can play a valuable role in reducing traffic and energy waste in large cities.
Visual detection methods represent a cost-effective option, since they can take
advantage of hardware usually already available in many parking lots, namely
cameras. However, visual detection methods can be fragile and not easily
generalizable. In this paper, we present a robust detection algorithm based on
deep convolutional neural networks. We implemented and tested our algorithm on
a large baseline dataset, and also on a set of image feeds from actual cameras
already installed in parking lots. We have developed a fully functional system,
from server-side image analysis to front-end user interface, to demonstrate the
practicality of our method.
Towards universal neural nets: Gibbs machines and ACE
Authors: Galin Georgiev
Date: 2015-08-26 00:00:00
Tasks: Density Estimation, General Classification

We study from a physics viewpoint a class of generative neural nets, Gibbs
machines, designed for gradual learning. While including variational
auto-encoders, they offer a broader universal platform for incrementally adding
newly learned features, including physical symmetries. Their direct connection
to statistical physics and information geometry is established. A variational
Pythagorean theorem justifies invoking the exponential/Gibbs class of
probabilities for creating brand new objects. Combining these nets with
classifiers, gives rise to a brand of universal generative neural nets -
stochastic auto-classifier-encoders (ACE). ACE have state-of-the-art
performance in their class, both for classification and density estimation for
the MNIST data set.
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation
Authors: Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen, John W. Fisher III, Lars Kai Hansen
Date: 2015-10-09 00:00:00
Tasks: Data Augmentation, Feature Engineering

Data augmentation is a key element in training high-dimensional models. In
this approach, one synthesizes new observations by applying pre-specified
transformations to the original training data; e.g.~new images are formed by
rotating old ones. Current augmentation schemes, however, rely on manual
specification of the applied transformations, making data augmentation an
implicit form of feature engineering. With an eye towards true end-to-end
learning, we suggest learning the applied transformations on a per-class basis.
Particularly, we align image pairs within each class under the assumption that
the spatial transformation between images belongs to a large class of
diffeomorphisms. We then learn a class-specific probabilistic generative models
of the transformations in a Riemannian submanifold of the Lie group of
diffeomorphisms. We demonstrate significant performance improvements in
training deep neural nets over manually-specified augmentation schemes. Our
code and augmented datasets are available online.
Zero-Shot Learning with Multi-Battery Factor Analysis
Authors: Zhong Ji, Yuzhong Xie, Yanwei Pang, Lei Chen, Zhongfei Zhang
Date: 2016-06-30 00:00:00
Tasks: image-classification, Image Classification, Zero-Shot Learning

Zero-shot learning (ZSL) extends the conventional image classification
technique to a more challenging situation where the test image categories are
not seen in the training samples. Most studies on ZSL utilize side information
such as attributes or word vectors to bridge the relations between the seen
classes and the unseen classes. However, existing approaches on ZSL typically
exploit a shared space for each type of side information independently, which
cannot make full use of the complementary knowledge of different types of side
information. To this end, this paper presents an MBFA-ZSL approach to embed
different types of side information as well as the visual feature into one
shared space. Specifically, we first develop an algorithm named Multi-Battery
Factor Analysis (MBFA) to build a unified semantic space, and then employ
multiple types of side information in it to achieve the ZSL. The close-form
solution makes MBFA-ZSL simple to implement and efficient to run on large
datasets. Extensive experiments on the popular AwA, CUB, and SUN datasets show
its significant superiority over the state-of-the-art approaches.
Dependency Parsing with LSTMs: An Empirical Evaluation
Authors: Adhiguna Kuncoro, Yuichiro Sawai, Kevin Duh, Yuji Matsumoto
Date: 2016-04-22 00:00:00
Tasks: Dependency Parsing

We propose a transition-based dependency parser using Recurrent Neural
Networks with Long Short-Term Memory (LSTM) units. This extends the feedforward
neural network parser of Chen and Manning (2014) and enables modelling of
entire sequences of shift/reduce transition decisions. On the Google Web
Treebank, our LSTM parser is competitive with the best feedforward parser on
overall accuracy and notably achieves more than 3% improvement for long-range
dependencies, which has proved difficult for previous transition-based parsers
due to error propagation and limited context information. Our findings
additionally suggest that dropout regularisation on the embedding layer is
crucial to improve the LSTM's generalisation.
Dimension-Free Iteration Complexity of Finite Sum Optimization Problems
Authors: Yossi Arjevani, Ohad Shamir
Date: 2016-06-30 00:00:00
Proceeding: NeurIPS 2016 12

Many canonical machine learning problems boil down to a convex optimization
problem with a finite sum structure. However, whereas much progress has been
made in developing faster algorithms for this setting, the inherent limitations
of these problems are not satisfactorily addressed by existing lower bounds.
Indeed, current bounds focus on first-order optimization algorithms, and only
apply in the often unrealistic regime where the number of iterations is less
than $\mathcal{O}(d/n)$ (where $d$ is the dimension and $n$ is the number of
samples). In this work, we extend the framework of (Arjevani et al., 2015) to
provide new lower bounds, which are dimension-free, and go beyond the
assumptions of current bounds, thereby covering standard finite sum
optimization methods, e.g., SAG, SAGA, SVRG, SDCA without duality, as well as
stochastic coordinate-descent methods, such as SDCA and accelerated proximal
SDCA.
Neutrosophic Overset, Neutrosophic Underset, and Neutrosophic Offset. Similarly for Neutrosophic Over-/Under-/Off- Logic, Probability, and Statistics
Authors: Florentin Smarandache
Date: 2016-06-30 00:00:00

Neutrosophic Over-/Under-/Off-Set and -Logic were defined by the author in
1995 and published for the first time in 2007. We extended the neutrosophic set
respectively to Neutrosophic Overset {when some neutrosophic component is over
1}, Neutrosophic Underset {when some neutrosophic component is below 0}, and to
Neutrosophic Offset {when some neutrosophic components are off the interval [0,
1], i.e. some neutrosophic component over 1 and other neutrosophic component
below 0}. This is no surprise with respect to the classical fuzzy set/logic,
intuitionistic fuzzy set/logic, or classical/imprecise probability, where the
values are not allowed outside the interval [0, 1], since our real-world has
numerous examples and applications of over-/under-/off-neutrosophic components.
For example, person working overtime deserves a membership degree over 1, while
a person producing more damage than benefit to a company deserves a membership
below 0. Then, similarly, the Neutrosophic Logic/Measure/Probability/Statistics
etc. were extended to respectively Neutrosophic Over-/Under-/Off-Logic,
-Measure, -Probability, -Statistics etc. [Smarandache, 2007].
How Many Folders Do You Really Need?
Authors: Mihajlo Grbovic, Guy Halawi, Zohar Karnin, Yoelle Maarek
Date: 2016-06-29 00:00:00
Tasks: General Classification

Email classification is still a mostly manual task. Consequently, most Web
mail users never define a single folder. Recently however, automatic
classification offering the same categories to all users has started to appear
in some Web mail clients, such as AOL or Gmail. We adopt this approach, rather
than previous (unsuccessful) personalized approaches because of the change in
the nature of consumer email traffic, which is now dominated by (non-spam)
machine-generated email. We propose here a novel approach for (1) automatically
distinguishing between personal and machine-generated email and (2) classifying
messages into latent categories, without requiring users to have defined any
folder. We report how we have discovered that a set of 6 "latent" categories
(one for human- and the others for machine-generated messages) can explain a
significant portion of email traffic. We describe in details the steps involved
in building a Web-scale email categorization system, from the collection of
ground-truth labels, the selection of features to the training of models.
Experimental evaluation was performed on more than 500 billion messages
received during a period of six months by users of Yahoo mail service, who
elected to be part of such research studies. Our system achieved precision and
recall rates close to 90% and the latent categories we discovered were shown to
cover 70% of both email traffic and email search queries. We believe that these
results pave the way for a change of approach in the Web mail industry, and
could support the invention of new large-scale email discovery paradigms that
had not been possible before.
Multiphase Segmentation For Simultaneously Homogeneous and Textural Images
Authors: Duy Hoang Thai, Lucas Mentch
Date: 2016-06-29 00:00:00
Tasks: Segmentation

Segmentation remains an important problem in image processing. For
homogeneous (piecewise smooth) images, a number of important models have been
developed and refined over the past several decades. However, these models
often fail when applied to the substantially larger class of natural images
that simultaneously contain regions of both texture and homogeneity. This work
introduces a bi-level constrained minimization model for simultaneous
multiphase segmentation of images containing both homogeneous and textural
regions. We develop novel norms defined in different functional Banach spaces
for the segmentation which results in a non-convex minimization. Finally, we
develop a generalized notion of segmentation delving into approximation theory
and demonstrating that a more refined decomposition of these images results in
multiple meaningful components. Both theoretical results and demonstrations on
natural images are provided.
Compression of Neural Machine Translation Models via Pruning
Authors: Abigail See, Minh-Thang Luong, Christopher D. Manning
Date: 2016-06-29 00:00:00
Proceeding: CONLL 2016 8
Tasks: Machine Translation, NMT, Translation

Neural Machine Translation (NMT), like many other deep learning domains,
typically suffers from over-parameterization, resulting in large storage sizes.
This paper examines three simple magnitude-based pruning schemes to compress
NMT models, namely class-blind, class-uniform, and class-distribution, which
differ in terms of how pruning thresholds are computed for the different
classes of weights in the NMT architecture. We demonstrate the efficacy of
weight pruning as a compression technique for a state-of-the-art NMT system. We
show that an NMT model with over 200 million parameters can be pruned by 40%
with very little performance loss as measured on the WMT'14 English-German
translation task. This sheds light on the distribution of redundancy in the NMT
architecture. Our main result is that with retraining, we can recover and even
surpass the original performance with an 80%-pruned model.
Learning Concept Taxonomies from Multi-modal Data
Authors: Hao Zhang, Zhiting Hu, Yuntian Deng, Mrinmaya Sachan, Zhicheng Yan, Eric P. Xing
Date: 2016-06-29 00:00:00
Proceeding: ACL 2016 8
Tasks: Feature Engineering

We study the problem of automatically building hypernym taxonomies from
textual and visual data. Previous works in taxonomy induction generally ignore
the increasingly prominent visual data, which encode important perceptual
semantics. Instead, we propose a probabilistic model for taxonomy induction by
jointly leveraging text and images. To avoid hand-crafted feature engineering,
we design end-to-end features based on distributed representations of images
and words. The model is discriminatively trained given a small set of existing
ontologies and is capable of building full taxonomies from scratch for a
collection of unseen conceptual label items with associated images. We evaluate
our model and features on the WordNet hierarchies, where our system outperforms
previous approaches by a large gap.
Penambahan emosi menggunakan metode manipulasi prosodi untuk sistem text to speech bahasa Indonesia
Authors: Salita Ulitia Prini, Ary Setijadi Prihatmanto
Date: 2016-06-29 00:00:00
Tasks: Sentence, text-to-speech, Text to Speech

Adding an emotions using prosody manipulation method for Indonesian text to
speech system. Text To Speech (TTS) is a system that can convert text in one
language into speech, accordance with the reading of the text in the language
used. The focus of this research is a natural sounding concept, the make
"humanize" for the pronunciation of voice synthesis system Text To Speech.
Humans have emotions / intonation that may affect the sound produced. The main
requirement for the system used Text To Speech in this research is eSpeak, the
database MBROLA using id1, Human Speech Corpus database from a website that
summarizes the words with the highest frequency (Most Common Words) used in a
country. And there are 3 types of emotional / intonation designed base. There
is a happy, angry and sad emotion. Method for develop the emotional filter is
manipulate the relevant features of prosody (especially pitch and duration
value) using a predetermined rate factor that has been established by analyzing
the differences between the standard output Text To Speech and voice recording
with emotional prosody / a particular intonation. The test results for the
perception tests of Human Speech Corpus for happy emotion is 95 %, 96.25 % for
angry emotion and 98.75 % for sad emotions. For perception test system carried
by intelligibility and naturalness test. Intelligibility test for the accuracy
of sound with the original sentence is 93.3%, and for clarity rate for each
sentence is 62.8%. For naturalness, accuracy emotional election amounted to
75.6 % for happy emotion, 73.3 % for angry emotion, and 60 % for sad emotions.
  -----
  Text To Speech (TTS) merupakan suatu sistem yang dapat mengonversi teks dalam
format suatu bahasa menjadi ucapan sesuai dengan pembacaan teks dalam bahasa
yang digunakan.
Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes
Authors: Satwik Kottur, Ramakrishna Vedantam, José M. F. Moura, Devi Parikh
Date: 2015-11-22 00:00:00
Proceeding: CVPR 2016 6
Tasks: Common Sense Reasoning, Image Retrieval, Retrieval, Visual Grounding, Word Embeddings

We propose a model to learn visually grounded word embeddings (vis-w2v) to
capture visual notions of semantic relatedness. While word embeddings trained
using text have been extremely successful, they cannot uncover notions of
semantic relatedness implicit in our visual world. For instance, although
"eats" and "stares at" seem unrelated in text, they share semantics visually.
When people are eating something, they also tend to stare at the food.
Grounding diverse relations like "eats" and "stares at" into vision remains
challenging, despite recent progress in vision. We note that the visual
grounding of words depends on semantics, and not the literal pixels. We thus
use abstract scenes created from clipart to provide the visual grounding. We
find that the embeddings we learn capture fine-grained, visually grounded
notions of semantic relatedness. We show improvements over text-only word
embeddings (word2vec) on three tasks: common-sense assertion classification,
visual paraphrasing and text-based image retrieval. Our code and datasets are
available online.