pwc-archive/papers-with-abstracts huggingface dataset:
Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
Authors: Pushpankar Kumar Pushp, Muktabh Mayank Srivastava
Date: 2017-12-16 00:00:00
Tasks: Classification, General Classification, Sentence, TAG, text-classification, Text Classification, Zero-Shot Learning

Zero-shot Learners are models capable of predicting unseen classes. In this
work, we propose a Zero-shot Learning approach for text categorization. Our
method involves training model on a large corpus of sentences to learn the
relationship between a sentence and embedding of sentence's tags. Learning such
relationship makes the model generalize to unseen sentences, tags, and even new
datasets provided they can be put into same embedding space. The model learns
to predict whether a given sentence is related to a tag or not; unlike other
classifiers that learn to classify the sentence as one of the possible classes.
We propose three different neural networks for the task and report their
accuracy on the test set of the dataset used for training them as well as two
other standard datasets for which no retraining was done. We show that our
models generalize well across new unseen classes in both cases. Although the
models do not achieve the accuracy level of the state of the art supervised
models, yet it evidently is a step forward towards general intelligence in
natural language processing.
A Framework for Enriching Lexical Semantic Resources with Distributional Semantics
Authors: Chris Biemann, Stefano Faralli, Alexander Panchenko, Simone Paolo Ponzetto
Date: 2017-12-23 00:00:00
Tasks: Specificity, Word Sense Disambiguation

We present an approach to combining distributional semantic representations
induced from text corpora with manually constructed lexical-semantic networks.
While both kinds of semantic resources are available with high lexical
coverage, our aligned resource combines the domain specificity and availability
of contextual information from distributional models with the conciseness and
high quality of manually crafted lexical networks. We start with a
distributional representation of induced senses of vocabulary terms, which are
accompanied with rich context information given by related lexical items. We
then automatically disambiguate such representations to obtain a full-fledged
proto-conceptualization, i.e. a typed graph of induced word senses. In a final
step, this proto-conceptualization is aligned to a lexical ontology, resulting
in a hybrid aligned resource. Moreover, unmapped induced senses are associated
with a semantic type in order to connect them to the core resource. Manual
evaluations against ground-truth judgments for different stages of our method
as well as an extrinsic evaluation on a knowledge-based Word Sense
Disambiguation benchmark all indicate the high quality of the new hybrid
resource. Additionally, we show the benefits of enriching top-down lexical
knowledge resources with bottom-up distributional information from text for
addressing high-end knowledge acquisition tasks such as cleaning hypernym
graphs and learning taxonomies from scratch.
Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction
Authors: Xingguo Li, Raman Arora, Han Liu, Jarvis Haupt, Tuo Zhao
Date: 2016-05-09 00:00:00
Tasks: parameter estimation, Sparse Learning, Stochastic Optimization

We propose a stochastic variance reduced optimization algorithm for solving
sparse learning problems with cardinality constraints. Sufficient conditions
are provided, under which the proposed algorithm enjoys strong linear
convergence guarantees and optimal estimation accuracy in high dimensions. We
further extend the proposed algorithm to an asynchronous parallel variant with
a near linear speedup. Numerical experiments demonstrate the efficiency of our
algorithm in terms of both parameter estimation and computational performance.
Iterative Random Forests to detect predictive and stable high-order interactions
Authors: Sumanta Basu, Karl Kumbier, James B. Brown, Bin Yu
Date: 2017-06-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Genomics has revolutionized biology, enabling the interrogation of whole
transcriptomes, genome-wide binding sites for proteins, and many other
molecular processes. However, individual genomic assays measure elements that
interact in vivo as components of larger molecular machines. Understanding how
these high-order interactions drive gene expression presents a substantial
statistical challenge. Building on Random Forests (RF), Random Intersection
Trees (RITs), and through extensive, biologically inspired simulations, we
developed the iterative Random Forest algorithm (iRF). iRF trains a
feature-weighted ensemble of decision trees to detect stable, high-order
interactions with same order of computational cost as RF. We demonstrate the
utility of iRF for high-order interaction discovery in two prediction problems:
enhancer activity in the early Drosophila embryo and alternative splicing of
primary transcripts in human derived cell lines. In Drosophila, among the 20
pairwise transcription factor interactions iRF identifies as stable (returned
in more than half of bootstrap replicates), 80% have been previously reported
as physical interactions. Moreover, novel third-order interactions, e.g.
between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order
relationships that are candidates for follow-up experiments. In human-derived
cells, iRF re-discovered a central role of H3K36me3 in chromatin-mediated
splicing regulation, and identified novel 5th and 6th order interactions,
indicative of multi-valent nucleosomes with specific roles in splicing
regulation. By decoupling the order of interactions from the computational cost
of identification, iRF opens new avenues of inquiry into the molecular
mechanisms underlying genome biology.
Are words easier to learn from infant- than adult-directed speech? A quantitative corpus-based investigation
Authors: Adriana Guevara-Rukoz, Alejandrina Cristia, Bogdan Ludusan, Roland Thiollière, Andrew Martin, Reiko Mazuka, Emmanuel Dupoux
Date: 2017-12-23 00:00:00

We investigate whether infant-directed speech (IDS) could facilitate word
form learning when compared to adult-directed speech (ADS). To study this, we
examine the distribution of word forms at two levels, acoustic and
phonological, using a large database of spontaneous speech in Japanese. At the
acoustic level we show that, as has been documented before for phonemes, the
realizations of words are more variable and less discriminable in IDS than in
ADS. At the phonological level, we find an effect in the opposite direction:
the IDS lexicon contains more distinctive words (such as onomatopoeias) than
the ADS counterpart. Combining the acoustic and phonological metrics together
in a global discriminability score reveals that the bigger separation of
lexical categories in the phonological space does not compensate for the
opposite effect observed at the acoustic level. As a result, IDS word forms are
still globally less discriminable than ADS word forms, even though the effect
is numerically small. We discuss the implication of these findings for the view
that the functional role of IDS is to improve language learnability.
Efficient Yet Deep Convolutional Neural Networks for Semantic Segmentation
Authors: Sharif Amit Kamran, Ali Shihab Sabbir
Date: 2017-07-26 00:00:00
Tasks: GPU, Image Segmentation, Scene Segmentation, Segmentation, Semantic Segmentation

Semantic Segmentation using deep convolutional neural network pose more
complex challenge for any GPU intensive task. As it has to compute million of
parameters, it results to huge memory consumption. Moreover, extracting finer
features and conducting supervised training tends to increase the complexity.
With the introduction of Fully Convolutional Neural Network, which uses finer
strides and utilizes deconvolutional layers for upsampling, it has been a go to
for any image segmentation task. In this paper, we propose two segmentation
architecture which not only needs one-third the parameters to compute but also
gives better accuracy than the similar architectures. The model weights were
transferred from the popular neural net like VGG19 and VGG16 which were trained
on Imagenet classification data-set. Then we transform all the fully connected
layers to convolutional layers and use dilated convolution for decreasing the
parameters. Lastly, we add finer strides and attach four skip architectures
which are element-wise summed with the deconvolutional layers in steps. We
train and test on different sparse and fine data-sets like Pascal VOC2012,
Pascal-Context and NYUDv2 and show how better our model performs in this tasks.
On the other hand our model has a faster inference time and consumes less
memory for training and testing on NVIDIA Pascal GPUs, making it more efficient
and less memory consuming architecture for pixel-wise segmentation.
Merging $K$-means with hierarchical clustering for identifying general-shaped groups
Authors: Anna D. Peterson, Arka P. Ghosh, Ranjan Maitra
Date: 2017-12-23 00:00:00
Tasks: Clustering, Density Estimation, Speech Synthesis

Clustering partitions a dataset such that observations placed together in a
group are similar but different from those in other groups. Hierarchical and
$K$-means clustering are two approaches but have different strengths and
weaknesses. For instance, hierarchical clustering identifies groups in a
tree-like structure but suffers from computational complexity in large datasets
while $K$-means clustering is efficient but designed to identify homogeneous
spherically-shaped clusters. We present a hybrid non-parametric clustering
approach that amalgamates the two methods to identify general-shaped clusters
and that can be applied to larger datasets. Specifically, we first partition
the dataset into spherical groups using $K$-means. We next merge these groups
using hierarchical methods with a data-driven distance measure as a stopping
criterion. Our proposal has the potential to reveal groups with general shapes
and structure in a dataset. We demonstrate good performance on several
simulated and real datasets.
Multi-Objective Vehicle Routing Problem Applied to Large Scale Post Office Deliveries
Authors: Luis A. A. Meira, Paulo S. Martins, Mauro Menzori, Guilherme A. Zeni
Date: 2017-12-23 00:00:00

The number of optimization techniques in the combinatorial domain is large
and diversified. Nevertheless, real-world based benchmarks for testing
algorithms are few. This work creates an extensible real-world mail delivery
benchmark to the Vehicle Routing Problem (VRP) in a planar graph embedded in
the 2D Euclidean space. Such problem is multi-objective on a roadmap with up to
25 vehicles and 30,000 deliveries per day. Each instance models one generic day
of mail delivery, allowing both comparison and validation of optimization
algorithms for routing problems. The benchmark may be extended to model other
scenarios.
Texture Object Segmentation Based on Affine Invariant Texture Detection
Authors: Jianwei Zhang, Xu Chen, Xuezhong Xiao
Date: 2017-12-23 00:00:00
Tasks: Edge Detection, Object, Semantic Segmentation

To solve the issue of segmenting rich texture images, a novel detection
methods based on the affine invariable principle is proposed. Considering the
similarity between the texture areas, we first take the affine transform to get
numerous shapes, and utilize the KLT algorithm to verify the similarity. The
transforms include rotation, proportional transformation and perspective
deformation to cope with a variety of situations. Then we propose an improved
LBP method combining canny edge detection to handle the boundary in the
segmentation process. Moreover, human-computer interaction of this method which
helps splitting the matched texture area from the original images is
user-friendly.
An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection
Authors: Chao Chen, Xiao Lin, Gabriel Terejanu
Date: 2017-12-23 00:00:00
Tasks: Outlier Detection

Long Short-Term Memory networks trained with gradient descent and back-propagation have received great success in various applications. However, point estimation of the weights of the networks is prone to over-fitting problems and lacks important uncertainty information associated with the estimation. However, exact Bayesian neural network methods are intractable and non-applicable for real-world applications. In this study, we propose an approximate estimation of the weights uncertainty using Ensemble Kalman Filter, which is easily scalable to a large number of weights. Furthermore, we optimize the covariance of the noise distribution in the ensemble update step using maximum likelihood estimation. To assess the proposed algorithm, we apply it to outlier detection in five real-world events retrieved from the Twitter platform.
On Estimation of Conditional Modes Using Multiple Quantile Regressions
Authors: Hirofumi Ohta, Satoshi Hara
Date: 2017-12-23 00:00:00

We propose an estimation method for the conditional mode when the
conditioning variable is high-dimensional. In the proposed method, we first
estimate the conditional density by solving quantile regressions multiple
times. We then estimate the conditional mode by finding the maximum of the
estimated conditional density. The proposed method has two advantages in that
it is computationally stable because it has no initial parameter dependencies,
and it is statistically efficient with a fast convergence rate. Synthetic and
real-world data experiments demonstrate the better performance of the proposed
method compared to other existing ones.
Hide-and-Seek: Forcing a Network to be Meticulous for Weakly-supervised Object and Action Localization
Authors: Krishna Kumar Singh, Yong Jae Lee
Date: 2017-04-13 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Localization, Object, Object Localization, Weakly Supervised Action Localization, Weakly-Supervised Object Localization

We propose `Hide-and-Seek', a weakly-supervised framework that aims to
improve object localization in images and action localization in videos. Most
existing weakly-supervised methods localize only the most discriminative parts
of an object rather than all relevant parts, which leads to suboptimal
performance. Our key idea is to hide patches in a training image randomly,
forcing the network to seek other relevant parts when the most discriminative
part is hidden. Our approach only needs to modify the input image and can work
with any network designed for object localization. During testing, we do not
need to hide any patches. Our Hide-and-Seek approach obtains superior
performance compared to previous methods for weakly-supervised object
localization on the ILSVRC dataset. We also demonstrate that our framework can
be easily extended to weakly-supervised action localization.
Scene-Specific Pedestrian Detection Based on Parallel Vision
Authors: Wenwen Zhang, Kunfeng Wang, Hua Qu, Jihong Zhao, Fei-Yue Wang
Date: 2017-12-23 00:00:00
Tasks: object-detection, Object Detection, Pedestrian Detection

As a special type of object detection, pedestrian detection in generic scenes
has made a significant progress trained with large amounts of labeled training
data manually. While the models trained with generic dataset work bad when they
are directly used in specific scenes. With special viewpoints, flow light and
backgrounds, datasets from specific scenes are much different from the datasets
from generic scenes. In order to make the generic scene pedestrian detectors
work well in specific scenes, the labeled data from specific scenes are needed
to adapt the models to the specific scenes. While labeling the data manually
spends much time and money, especially for specific scenes, each time with a
new specific scene, large amounts of images must be labeled. What's more, the
labeling information is not so accurate in the pixels manually and different
people make different labeling information. In this paper, we propose an
ACP-based method, with augmented reality's help, we build the virtual world of
specific scenes, and make people walking in the virtual scenes where it is
possible for them to appear to solve this problem of lacking labeled data and
the results show that data from virtual world is helpful to adapt generic
pedestrian detectors to specific scenes.
A Fast and Accurate Vietnamese Word Segmenter
Authors: Dat Quoc Nguyen, Dai Quoc Nguyen, Thanh Vu, Mark Dras, Mark Johnson
Date: 2017-09-19 00:00:00
Proceeding: LREC 2018 5
Tasks: General Classification, Segmentation, Vietnamese Word Segmentation

We propose a novel approach to Vietnamese word segmentation. Our approach is
based on the Single Classification Ripple Down Rules methodology (Compton and
Jansen, 1990), where rules are stored in an exception structure and new rules
are only added to correct segmentation errors given by existing rules.
Experimental results on the benchmark Vietnamese treebank show that our
approach outperforms previous state-of-the-art approaches JVnSegmenter,
vnTokenizer, DongDu and UETsegmenter in terms of both accuracy and performance
speed. Our code is open-source and available at:
https://github.com/datquocnguyen/RDRsegmenter.
Online Forecasting Matrix Factorization
Authors: San Gultekin, John Paisley
Date: 2017-12-23 00:00:00
Tasks: Missing Values, Time Series, Time Series Analysis

In this paper the problem of forecasting high dimensional time series is
considered. Such time series can be modeled as matrices where each column
denotes a measurement. In addition, when missing values are present, low rank
matrix factorization approaches are suitable for predicting future values. This
paper formally defines and analyzes the forecasting problem in the online
setting, i.e. where the data arrives as a stream and only a single pass is
allowed. We present and analyze novel matrix factorization techniques which can
learn low-dimensional embeddings effectively in an online manner. Based on
these embeddings a recursive minimum mean square error estimator is derived,
which learns an autoregressive model on them. Experiments with two real
datasets with tens of millions of measurements show the benefits of the
proposed approach.
Combining Weakly and Webly Supervised Learning for Classifying Food Images
Authors: Parneet Kaur, Karan Sikka, Ajay Divakaran
Date: 2017-12-23 00:00:00
Tasks: Classification, General Classification, Weakly-supervised Learning

Food classification from images is a fine-grained classification problem.
Manual curation of food images is cost, time and scalability prohibitive. On
the other hand, web data is available freely but contains noise. In this paper,
we address the problem of classifying food images with minimal data curation.
We also tackle a key problems with food images from the web where they often
have multiple cooccuring food types but are weakly labeled with a single label.
We first demonstrate that by sequentially adding a few manually curated samples
to a larger uncurated dataset from two web sources, the top-1 classification
accuracy increases from 50.3% to 72.8%. To tackle the issue of weak labels, we
augment the deep model with Weakly Supervised learning (WSL) that results in an
increase in performance to 76.2%. Finally, we show some qualitative results to
provide insights into the performance improvements using the proposed ideas.
On the Consistency of Quick Shift
Authors: Heinrich Jiang
Date: 2017-10-29 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Clustering, regression

Quick Shift is a popular mode-seeking and clustering algorithm. We present
finite sample statistical consistency guarantees for Quick Shift on mode and
cluster recovery under mild distributional assumptions. We then apply our
results to construct a consistent modal regression algorithm.
Towards Structured Analysis of Broadcast Badminton Videos
Authors: Anurag Ghosh, Suriya Singh, C. V. Jawahar
Date: 2017-12-23 00:00:00

Sports video data is recorded for nearly every major tournament but remains
archived and inaccessible to large scale data mining and analytics. It can only
be viewed sequentially or manually tagged with higher-level labels which is
time consuming and prone to errors. In this work, we propose an end-to-end
framework for automatic attributes tagging and analysis of sport videos. We use
commonly available broadcast videos of matches and, unlike previous approaches,
does not rely on special camera setups or additional sensors.
  Our focus is on Badminton as the sport of interest. We propose a method to
analyze a large corpus of badminton broadcast videos by segmenting the points
played, tracking and recognizing the players in each point and annotating their
respective badminton strokes. We evaluate the performance on 10 Olympic matches
with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player
detection score (mAP@0.5), 97.98% player identification accuracy, and stroke
segmentation edit scores of 80.48%. We further show that the automatically
annotated videos alone could enable the gameplay analysis and inference by
computing understandable metrics such as player's reaction time, speed, and
footwork around the court, etc.
Query-limited Black-box Attacks to Classifiers
Authors: Fnu Suya, Yuan Tian, David Evans, Paolo Papotti
Date: 2017-12-23 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning

We study black-box attacks on machine learning classifiers where each query
to the model incurs some cost or risk of detection to the adversary. We focus
explicitly on minimizing the number of queries as a major objective.
Specifically, we consider the problem of attacking machine learning classifiers
subject to a budget of feature modification cost while minimizing the number of
queries, where each query returns only a class and confidence score. We
describe an approach that uses Bayesian optimization to minimize the number of
queries, and find that the number of queries can be reduced to approximately
one tenth of the number needed through a random strategy for scenarios where
the feature modification cost budget is low.
On the Universality of Memcomputing Machines
Authors: Yan Ru Pei, Fabio L. Traversa, Massimiliano Di Ventra
Date: 2017-12-23 00:00:00

Universal memcomputing machines (UMMs) [IEEE Trans. Neural Netw. Learn. Syst. 26, 2702 (2015)] represent a novel computational model in which memory (time non-locality) accomplishes both tasks of storing and processing of information. UMMs have been shown to be Turing-complete, namely they can simulate any Turing machine. In this paper, using set theory and cardinality arguments, we compare them with liquid-state machines (or "reservoir computing") and quantum machines ("quantum computing"). We show that UMMs can simulate both types of machines, hence they are both "liquid-" or "reservoir-complete" and "quantum-complete". Of course, these statements pertain only to the type of problems these machines can solve, and not to the amount of resources required for such simulations. Nonetheless, the method presented here provides a general framework in which to describe the relation between UMMs and any other type of computational model.
Linearly convergent stochastic heavy ball method for minimizing generalization error
Authors: Nicolas Loizou, Peter Richtárik
Date: 2017-10-30 00:00:00

In this work we establish the first linear convergence result for the
stochastic heavy ball method. The method performs SGD steps with a fixed
stepsize, amended by a heavy ball momentum term. In the analysis, we focus on
minimizing the expected loss and not on finite-sum minimization, which is
typically a much harder problem. While in the analysis we constrain ourselves
to quadratic loss, the overall objective is not necessarily strongly convex.