pwc-archive/papers-with-abstracts huggingface dataset:
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning
Authors: Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
Date: 2017-04-04 00:00:00
Tasks: BIG-bench Machine Learning

The AMIDST Toolbox is a software for scalable probabilistic machine learning
with a spe- cial focus on (massive) streaming data. The toolbox supports a
flexible modeling language based on probabilistic graphical models with latent
variables and temporal dependencies. The specified models can be learnt from
large data sets using parallel or distributed implementa- tions of Bayesian
learning algorithms for either streaming or batch data. These algorithms are
based on a flexible variational message passing scheme, which supports discrete
and continu- ous variables from a wide range of probability distributions.
AMIDST also leverages existing functionality and algorithms by interfacing to
software tools such as Flink, Spark, MOA, Weka, R and HUGIN. AMIDST is an open
source toolbox written in Java and available at http://www.amidsttoolbox.com
under the Apache Software License version 2.0.
Cyclical Learning Rates for Training Neural Networks
Authors: Leslie N. Smith
Date: 2015-06-03 00:00:00

It is known that the learning rate is the most important hyper-parameter to
tune for training deep neural networks. This paper describes a new method for
setting the learning rate, named cyclical learning rates, which practically
eliminates the need to experimentally find the best values and schedule for the
global learning rates. Instead of monotonically decreasing the learning rate,
this method lets the learning rate cyclically vary between reasonable boundary
values. Training with cyclical learning rates instead of fixed values achieves
improved classification accuracy without a need to tune and often in fewer
iterations. This paper also describes a simple way to estimate "reasonable
bounds" -- linearly increasing the learning rate of the network for a few
epochs. In addition, cyclical learning rates are demonstrated on the CIFAR-10
and CIFAR-100 datasets with ResNets, Stochastic Depth networks, and DenseNets,
and the ImageNet dataset with the AlexNet and GoogLeNet architectures. These
are practical tools for everyone who trains neural networks.
Adaptive Motion Gaming AI for Health Promotion
Authors: Pujana Paliyawan, Takahiro Kusano, Yuto Nakagawa, Tomohiro Harada, Ruck Thawonmas
Date: 2017-04-04 00:00:00

This paper presents a design of a non-player character (AI) for promoting
balancedness in use of body segments when engaging in full-body motion gaming.
In our experiment, we settle a battle between the proposed AI and a player by
using FightingICE, a fighting game platform for AI development. A middleware
called UKI is used to allow the player to control the game by using body motion
instead of the keyboard and mouse. During gameplay, the proposed AI analyze
health states of the player; it determines its next action by predicting how
each candidate action, recommended by a Monte-Carlo tree search algorithm, will
induce the player to move, and how the player's health tends to be affected.
Our result demonstrates successful improvement in balancedness in use of body
segments on 4 out of 5 subjects.
A Transition-Based Directed Acyclic Graph Parser for UCCA
Authors: Daniel Hershcovich, Omri Abend, Ari Rappoport
Date: 2017-04-03 00:00:00
Proceeding: ACL 2017 7
Tasks: Semantic Parsing, UCCA Parsing

We present the first parser for UCCA, a cross-linguistically applicable
framework for semantic representation, which builds on extensive typological
work and supports rapid annotation. UCCA poses a challenge for existing parsing
techniques, as it exhibits reentrancy (resulting in DAG structures),
discontinuous structures and non-terminal nodes corresponding to complex
semantic units. To our knowledge, the conjunction of these formal properties is
not supported by any existing parser. Our transition-based parser, which uses a
novel transition set and features based on bidirectional LSTMs, has value not
just for UCCA parsing: its ability to handle more general graph structures can
inform the development of parsers for other semantic DAG structures, and in
languages that frequently use discontinuous structures.
Super-resolving multiresolution images with band-independant geometry of multispectral pixels
Authors: Nicolas Brodu
Date: 2016-09-26 00:00:00

A new resolution enhancement method is presented for multispectral and
multi-resolution images, such as these provided by the Sentinel-2 satellites.
Starting from the highest resolution bands, band-dependent information
(reflectance) is separated from information that is common to all bands
(geometry of scene elements). This model is then applied to unmix
low-resolution bands, preserving their reflectance, while propagating
band-independent information to preserve the sub-pixel details. A reference
implementation is provided, with an application example for super-resolving
Sentinel-2 data.
Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines
Authors: Youness Mansar, Lorenzo Gatti, Sira Ferradans, Marco Guerini, Jacopo Staiano
Date: 2017-04-04 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Word Embeddings

In this paper, we describe a methodology to infer Bullish or Bearish
sentiment towards companies/brands. More specifically, our approach leverages
affective lexica and word embeddings in combination with convolutional neural
networks to infer the sentiment of financial news headlines towards a target
company. Such architecture was used and evaluated in the context of the SemEval
2017 challenge (task 5, subtask 2), in which it obtained the best performance.
Japanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention
Authors: Ryosuke Miyazaki, Mamoru Komachi
Date: 2017-04-04 00:00:00
Proceeding: PACLIC 2018 12
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

Previous approaches to training syntax-based sentiment classification models
required phrase-level annotated corpora, which are not readily available in
many languages other than English. Thus, we propose the use of tree-structured
Long Short-Term Memory with an attention mechanism that pays attention to each
subtree of the parse tree. Experimental results indicate that our model
achieves the state-of-the-art performance in a Japanese sentiment
classification task.
A New Spectral Method for Latent Variable Models
Authors: Matteo Ruffini, Marta Casanellas, Ricard Gavaldà
Date: 2016-12-11 00:00:00

This paper presents an algorithm for the unsupervised learning of latent
variable models from unlabeled sets of data. We base our technique on spectral
decomposition, providing a technique that proves to be robust both in theory
and in practice. We also describe how to use this algorithm to learn the
parameters of two well known text mining models: single topic model and Latent
Dirichlet Allocation, providing in both cases an efficient technique to
retrieve the parameters to feed the algorithm. We compare the results of our
algorithm with those of existing algorithms on synthetic data, and we provide
examples of applications to real world text corpora for both single topic model
and LDA, obtaining meaningful results.
A Unified Multi-Faceted Video Summarization System
Authors: Anurag Sahoo, Vishal Kaushal, Khoshrav Doctor, Suyash Shetty, Rishabh Iyer, Ganesh Ramakrishnan
Date: 2017-04-04 00:00:00
Tasks: Diversity, Extractive Summarization, Query-focused Summarization, Video Summarization

This paper addresses automatic summarization and search in visual data
comprising of videos, live streams and image collections in a unified manner.
In particular, we propose a framework for multi-faceted summarization which
extracts key-frames (image summaries), skims (video summaries) and entity
summaries (summarization at the level of entities like objects, scenes, humans
and faces in the video). The user can either view these as extractive
summarization, or query focused summarization. Our approach first pre-processes
the video or image collection once, to extract all important visual features,
following which we provide an interactive mechanism to the user to summarize
the video based on their choice. We investigate several diversity, coverage and
representation models for all these problems, and argue the utility of these
different mod- els depending on the application. While most of the prior work
on submodular summarization approaches has focused on combining several models
and learning weighted mixtures, we focus on the explain-ability of different
the diversity, coverage and representation models and their scalability. Most
importantly, we also show that we can summarize hours of video data in a few
seconds, and our system allows the user to generate summaries of various
lengths and types interactively on the fly.
A Branch-and-Bound Algorithm for Checkerboard Extraction in Camera-Laser Calibration
Authors: Alireza Khosravian, Tat-Jun Chin, Ian Reid
Date: 2017-04-04 00:00:00
Tasks: Combinatorial Optimization

We address the problem of camera-to-laser-scanner calibration using a
checkerboard and multiple image-laser scan pairs. Distinguishing which laser
points measure the checkerboard and which lie on the background is essential to
any such system. We formulate the checkerboard extraction as a combinatorial
optimization problem with a clear cut objective function. We propose a
branch-and-bound technique that deterministically and globally optimizes the
objective. Unlike what is available in the literature, the proposed method is
not heuristic and does not require assumptions such as constraints on the
background or relying on discontinuity of the range measurements to partition
the data into line segments. The proposed approach is generic and can be
applied to both 3D or 2D laser scanners as well as the cases where multiple
checkerboards are present. We demonstrate the effectiveness of the proposed
approach by providing numerical simulations as well as experimental results.
Scaling the Scattering Transform: Deep Hybrid Networks
Authors: Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko
Date: 2017-03-27 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Classification

We use the scattering network as a generic and fixed ini-tialization of the
first layers of a supervised hybrid deep network. We show that early layers do
not necessarily need to be learned, providing the best results to-date with
pre-defined representations while being competitive with Deep CNNs. Using a
shallow cascade of 1 x 1 convolutions, which encodes scattering coefficients
that correspond to spatial windows of very small sizes, permits to obtain
AlexNet accuracy on the imagenet ILSVRC2012. We demonstrate that this local
encoding explicitly learns invariance w.r.t. rotations. Combining scattering
networks with a modern ResNet, we achieve a single-crop top 5 error of 11.4% on
imagenet ILSVRC2012, comparable to the Resnet-18 architecture, while utilizing
only 10 layers. We also find that hybrid architectures can yield excellent
performance in the small sample regime, exceeding their end-to-end
counterparts, through their ability to incorporate geometrical priors. We
demonstrate this on subsets of the CIFAR-10 dataset and on the STL-10 dataset.
Simultaneous Feature Aggregating and Hashing for Large-scale Image Search
Authors: Thanh-Toan Do, Dang-Khoa Le Tan, Trung T. Pham, Ngai-Man Cheung
Date: 2017-04-04 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Retrieval, Retrieval

In most state-of-the-art hashing-based visual search systems, local image
descriptors of an image are first aggregated as a single feature vector. This
feature vector is then subjected to a hashing function that produces a binary
hash code. In previous work, the aggregating and the hashing processes are
designed independently. In this paper, we propose a novel framework where
feature aggregating and hashing are designed simultaneously and optimized
jointly. Specifically, our joint optimization produces aggregated
representations that can be better reconstructed by some binary codes. This
leads to more discriminative binary hash codes and improved retrieval accuracy.
In addition, we also propose a fast version of the recently-proposed Binary
Autoencoder to be used in our proposed framework. We perform extensive
retrieval experiments on several benchmark datasets with both SIFT and
convolutional features. Our results suggest that the proposed framework
achieves significant improvements over the state of the art.
Embedding based on function approximation for large scale image search
Authors: Thanh-Toan Do, Ngai-Man Cheung
Date: 2016-05-23 00:00:00
Tasks: Image Retrieval, Retrieval

The objective of this paper is to design an embedding method that maps local
features describing an image (e.g. SIFT) to a higher dimensional representation
useful for the image retrieval problem. First, motivated by the relationship
between the linear approximation of a nonlinear function in high dimensional
space and the stateof-the-art feature representation used in image retrieval,
i.e., VLAD, we propose a new approach for the approximation. The embedded
vectors resulted by the function approximation process are then aggregated to
form a single representation for image retrieval. Second, in order to make the
proposed embedding method applicable to large scale problem, we further derive
its fast version in which the embedded vectors can be efficiently computed,
i.e., in the closed-form. We compare the proposed embedding methods with the
state of the art in the context of image search under various settings: when
the images are represented by medium length vectors, short vectors, or binary
vectors. The experimental results show that the proposed embedding methods
outperform existing the state of the art on the standard public image retrieval
benchmarks.
Learning a collaborative multiscale dictionary based on robust empirical mode decomposition
Authors: Rui Chen, Huizhu Jia, Xiaodong Xie, Wen Gao
Date: 2017-04-04 00:00:00
Tasks: Clustering, Denoising, Dictionary Learning, Image Denoising

Dictionary learning is a challenge topic in many image processing areas. The
basic goal is to learn a sparse representation from an overcomplete basis set.
Due to combining the advantages of generic multiscale representations with
learning based adaptivity, multiscale dictionary representation approaches have
the power in capturing structural characteristics of natural images. However,
existing multiscale learning approaches still suffer from three main
weaknesses: inadaptability to diverse scales of image data, sensitivity to
noise and outliers, difficulty to determine optimal dictionary structure. In
this paper, we present a novel multiscale dictionary learning paradigm for
sparse image representations based on an improved empirical mode decomposition.
This powerful data-driven analysis tool for multi-dimensional signal can fully
adaptively decompose the image into multiscale oscillating components according
to intrinsic modes of data self. This treatment can obtain a robust and
effective sparse representation, and meanwhile generates a raw base dictionary
at multiple geometric scales and spatial frequency bands. This dictionary is
refined by selecting optimal oscillating atoms based on frequency clustering.
In order to further enhance sparsity and generalization, a tolerance dictionary
is learned using a coherence regularized model. A fast proximal scheme is
developed to optimize this model. The multiscale dictionary is considered as
the product of oscillating dictionary and tolerance dictionary. Experimental
results demonstrate that the proposed learning approach has the superior
performance in sparse image representations as compared with several competing
methods. We also show the promising results in image denoising application.
A History of Metaheuristics
Authors: Kenneth Sorensen, Marc Sevaux, Fred Glover
Date: 2017-04-04 00:00:00

This chapter describes the history of metaheuristics in five distinct
periods, starting long before the first use of the term and ending a long time
in the future.
Statistical Inference using the Morse-Smale Complex
Authors: Yen-Chi Chen, Christopher R. Genovese, Larry Wasserman
Date: 2015-06-29 00:00:00
Tasks: Clustering, Density Estimation, regression

The Morse-Smale complex of a function $f$ decomposes the sample space into
cells where $f$ is increasing or decreasing. When applied to nonparametric
density estimation and regression, it provides a way to represent, visualize,
and compare multivariate functions. In this paper, we present some statistical
results on estimating Morse-Smale complexes. This allows us to derive new
results for two existing methods: mode clustering and Morse-Smale regression.
We also develop two new methods based on the Morse-Smale complex: a
visualization technique for multivariate functions and a two-sample,
multivariate hypothesis test.
Opinion Mining on Non-English Short Text
Authors: Esra Akbas
Date: 2017-03-31 00:00:00
Tasks: Opinion Mining, Sentiment Analysis

As the type and the number of such venues increase, automated analysis of
sentiment on textual resources has become an essential data mining task. In
this paper, we investigate the problem of mining opinions on the collection of
informal short texts. Both positive and negative sentiment strength of texts
are detected. We focus on a non-English language that has few resources for
text mining. This approach would help enhance the sentiment analysis in
languages where a list of opinionated words does not exist. We propose a new
method projects the text into dense and low dimensional feature vectors
according to the sentiment strength of the words. We detect the mixture of
positive and negative sentiments on a multi-variant scale. Empirical evaluation
of the proposed framework on Turkish tweets shows that our approach gets good
results for opinion mining.
Guided Proofreading of Automatic Segmentations for Connectomics
Authors: Daniel Haehn, Verena Kaynig, James Tompkin, Jeff W. Lichtman, Hanspeter Pfister
Date: 2017-04-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Automatic cell image segmentation methods in connectomics produce merge and
split errors, which require correction through proofreading. Previous research
has identified the visual search for these errors as the bottleneck in
interactive proofreading. To aid error correction, we develop two classifiers
that automatically recommend candidate merges and splits to the user. These
classifiers use a convolutional neural network (CNN) that has been trained with
errors in automatic segmentations against expert-labeled ground truth. Our
classifiers detect potentially-erroneous regions by considering a large context
region around a segmentation boundary. Corrections can then be performed by a
user with yes/no decisions, which reduces variation of information 7.5x faster
than previous proofreading methods. We also present a fully-automatic mode that
uses a probability threshold to make merge/split decisions. Extensive
experiments using the automatic approach and comparing performance of novice
and expert users demonstrate that our method performs favorably against
state-of-the-art proofreading methods on different connectomics datasets.
4-DoF Tracking for Robot Fine Manipulation Tasks
Authors: Mennatullah Siam, Abhineet Singh, Camilo Perez, Martin Jagersand
Date: 2017-03-06 00:00:00

This paper presents two visual trackers from the different paradigms of
learning and registration based tracking and evaluates their application in
image based visual servoing. They can track object motion with four degrees of
freedom (DoF) which, as we will show here, is sufficient for many fine
manipulation tasks. One of these trackers is a newly developed learning based
tracker that relies on learning discriminative correlation filters while the
other is a refinement of a recent 8 DoF RANSAC based tracker adapted with a new
appearance model for tracking 4 DoF motion.
  Both trackers are shown to provide superior performance to several state of
the art trackers on an existing dataset for manipulation tasks. Further, a new
dataset with challenging sequences for fine manipulation tasks captured from
robot mounted eye-in-hand (EIH) cameras is also presented. These sequences have
a variety of challenges encountered during real tasks including jittery camera
movement, motion blur, drastic scale changes and partial occlusions.
Quantitative and qualitative results on these sequences are used to show that
these two trackers are robust to failures while providing high precision that
makes them suitable for such fine manipulation tasks.
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth using Stochastic Grammars
Authors: Chenfanfu Jiang, Siyuan Qi, Yixin Zhu, Siyuan Huang, Jenny Lin, Lap-Fai Yu, Demetri Terzopoulos, Song-Chun Zhu
Date: 2017-04-01 00:00:00
Tasks: Benchmarking, Object, Scene Understanding, Semantic Segmentation

We propose a systematic learning-based approach to the generation of massive
quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D
images thereof, with associated ground truth information, for the purposes of
training, benchmarking, and diagnosing learning-based computer vision and
robotics algorithms. In particular, we devise a learning-based pipeline of
algorithms capable of automatically generating and rendering a potentially
infinite variety of indoor scenes by using a stochastic grammar, represented as
an attributed Spatial And-Or Graph, in conjunction with state-of-the-art
physics-based rendering. Our pipeline is capable of synthesizing scene layouts
with high diversity, and it is configurable inasmuch as it enables the precise
customization and control of important attributes of the generated scenes. It
renders photorealistic RGB images of the generated scenes while automatically
synthesizing detailed, per-pixel ground truth data, including visible surface
depth and normal, object identity, and material information (detailed to object
parts), as well as environments (e.g., illuminations and camera viewpoints). We
demonstrate the value of our synthesized dataset, by improving performance in
certain machine-learning-based scene understanding tasks--depth and surface
normal prediction, semantic segmentation, reconstruction, etc.--and by
providing benchmarks for and diagnostics of trained models by modifying object
attributes and scene properties in a controllable manner.
Cascaded Segmentation-Detection Networks for Word-Level Text Spotting
Authors: Siyang Qin, Roberto Manduchi
Date: 2017-04-03 00:00:00
Tasks: GPU, Text Spotting

We introduce an algorithm for word-level text spotting that is able to
accurately and reliably determine the bounding regions of individual words of
text "in the wild". Our system is formed by the cascade of two convolutional
neural networks. The first network is fully convolutional and is in charge of
detecting areas containing text. This results in a very reliable but possibly
inaccurate segmentation of the input image. The second network (inspired by the
popular YOLO architecture) analyzes each segment produced in the first stage,
and predicts oriented rectangular regions containing individual words. No
post-processing (e.g. text line grouping) is necessary. With execution time of
450 ms for a 1000-by-560 image on a Titan X GPU, our system achieves the
highest score to date among published algorithms on the ICDAR 2015 Incidental
Scene Text dataset benchmark.