pwc-archive/papers-with-abstracts huggingface dataset:
Nonnegative Tensor Factorization for Directional Blind Audio Source Separation
Authors: Noah D. Stein
Date: 2014-11-18 00:00:00
Tasks: Audio Source Separation

We augment the nonnegative matrix factorization method for audio source
separation with cues about directionality of sound propagation. This improves
separation quality greatly and removes the need for training data, with only a
twofold increase in run time. This is the first method which can exploit
directional information from microphone arrays much smaller than the wavelength
of sound, working both in simulation and in practice on millimeter-scale
microphone arrays.
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection
Authors: Mohammadreza Zolfaghari, Gabriel L. Oliveira, Nima Sedaghat, Thomas Brox
Date: 2017-04-03 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Classification, Action Localization, Action Recognition, General Classification, Skeleton Based Action Recognition, Spatio-Temporal Action Localization, Temporal Action Localization

General human action recognition requires understanding of various visual
cues. In this paper, we propose a network architecture that computes and
integrates the most important visual cues for action recognition: pose, motion,
and the raw images. For the integration, we introduce a Markov chain model
which adds cues successively. The resulting approach is efficient and
applicable to action classification as well as to spatial and temporal action
localization. The two contributions clearly improve the performance over
respective baselines. The overall approach achieves state-of-the-art action
classification performance on HMDB51, J-HMDB and NTU RGB+D datasets. Moreover,
it yields state-of-the-art spatio-temporal action localization results on
UCF101 and J-HMDB.
Unsupervised Sequence Classification using Sequential Output Statistics
Authors: Yu Liu, Jianshu Chen, Li Deng
Date: 2017-02-25 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Classification, General Classification

We consider learning a sequence classifier without labeled data by using
sequential output statistics. The problem is highly valuable since obtaining
labels in training data is often costly, while the sequential output statistics
(e.g., language models) could be obtained independently of input data and thus
with low or no cost. To address the problem, we propose an unsupervised
learning cost function and study its properties. We show that, compared to
earlier works, it is less inclined to be stuck in trivial solutions and avoids
the need for a strong generative model. Although it is harder to optimize in
its functional form, a stochastic primal-dual gradient method is developed to
effectively solve the problem. Experiment results on real-world datasets
demonstrate that the new unsupervised learning method gives drastically lower
errors than other baseline methods. Specifically, it reaches test errors about
twice of those obtained by fully supervised learning.
Community Identity and User Engagement in a Multi-Community Landscape
Authors: Justine Zhang, William L. Hamilton, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky, Jure Leskovec
Date: 2017-05-26 00:00:00

A community's identity defines and shapes its internal dynamics. Our current
understanding of this interplay is mostly limited to glimpses gathered from
isolated studies of individual communities. In this work we provide a
systematic exploration of the nature of this relation across a wide variety of
online communities. To this end we introduce a quantitative, language-based
typology reflecting two key aspects of a community's identity: how distinctive,
and how temporally dynamic it is. By mapping almost 300 Reddit communities into
the landscape induced by this typology, we reveal regularities in how patterns
of user engagement vary with the characteristics of a community.
  Our results suggest that the way new and existing users engage with a
community depends strongly and systematically on the nature of the collective
identity it fosters, in ways that are highly consequential to community
maintainers. For example, communities with distinctive and highly dynamic
identities are more likely to retain their users. However, such niche
communities also exhibit much larger acculturation gaps between existing users
and newcomers, which potentially hinder the integration of the latter.
  More generally, our methodology reveals differences in how various social
phenomena manifest across communities, and shows that structuring the
multi-community landscape can lead to a better understanding of the systematic
nature of this diversity.
Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents & Shareability of News Headlines
Authors: Terrence Szymanski, Claudia Orellana-Rodriguez, Mark T. Keane
Date: 2017-05-26 00:00:00
Tasks: regression

We present a software tool that employs state-of-the-art natural language
processing (NLP) and machine learning techniques to help newspaper editors
compose effective headlines for online publication. The system identifies the
most salient keywords in a news article and ranks them based on both their
overall popularity and their direct relevance to the article. The system also
uses a supervised regression model to identify headlines that are likely to be
widely shared on social media. The user interface is designed to simplify and
speed the editor's decision process on the composition of the headline. As
such, the tool provides an efficient way to combine the benefits of automated
predictors of engagement and search-engine optimization (SEO) with human
judgments of overall headline quality.
Poincaré Embeddings for Learning Hierarchical Representations
Authors: Maximilian Nickel, Douwe Kiela
Date: 2017-05-22 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Graph Embedding, Representation Learning, Riemannian optimization

Representation learning has become an invaluable approach for learning from
symbolic data such as text and graphs. However, while complex symbolic datasets
often exhibit a latent hierarchical structure, state-of-the-art methods
typically learn embeddings in Euclidean vector spaces, which do not account for
this property. For this purpose, we introduce a new approach for learning
hierarchical representations of symbolic data by embedding them into hyperbolic
space -- or more precisely into an n-dimensional Poincar\'e ball. Due to the
underlying hyperbolic geometry, this allows us to learn parsimonious
representations of symbolic data by simultaneously capturing hierarchy and
similarity. We introduce an efficient algorithm to learn the embeddings based
on Riemannian optimization and show experimentally that Poincar\'e embeddings
outperform Euclidean embeddings significantly on data with latent hierarchies,
both in terms of representation capacity and in terms of generalization
ability.
Two-dimensional nonseparable discrete linear canonical transform based on CM-CC-CM-CC decomposition
Authors: Soo-Chang Pei, Shih-Gu Huang
Date: 2017-05-26 00:00:00

As a generalization of the two-dimensional Fourier transform (2D FT) and 2D
fractional Fourier transform, the 2D nonseparable linear canonical transform
(2D NsLCT) is useful in optics, signal and image processing. To reduce the
digital implementation complexity of the 2D NsLCT, some previous works
decomposed the 2D NsLCT into several low-complexity operations, including 2D
FT, 2D chirp multiplication (2D CM) and 2D affine transformations. However, 2D
affine transformations will introduce interpolation error. In this paper, we
propose a new decomposition called CM-CC-CM-CC decomposition, which decomposes
the 2D NsLCT into two 2D CMs and two 2D chirp convolutions (2D CCs). No 2D
affine transforms are involved. Simulation results show that the proposed
methods have higher accuracy, lower computational complexity and smaller error
in the additivity property compared with the previous works. Plus, the proposed
methods have perfect reversibility property that one can reconstruct the input
signal/image losslessly from the output.
Formalizing Neurath's Ship: Approximate Algorithms for Online Causal Learning
Authors: Neil R. Bramley, Peter Dayan, Thomas L. Griffiths, David A. Lagnado
Date: 2016-09-14 00:00:00
Tasks: Bayesian Inference, Philosophy

Higher-level cognition depends on the ability to learn models of the world.
We can characterize this at the computational level as a structure-learning
problem with the goal of best identifying the prevailing causal relationships
among a set of relata. However, the computational cost of performing exact
Bayesian inference over causal models grows rapidly as the number of relata
increases. This implies that the cognitive processes underlying causal learning
must be substantially approximate. A powerful class of approximations that
focuses on the sequential absorption of successive inputs is captured by the
Neurath's ship metaphor in philosophy of science, where theory change is cast
as a stochastic and gradual process shaped as much by people's limited
willingness to abandon their current theory when considering alternatives as by
the ground truth they hope to approach. Inspired by this metaphor and by
algorithms for approximating Bayesian inference in machine learning, we propose
an algorithmic-level model of causal structure learning under which learners
represent only a single global hypothesis that they update locally as they
gather evidence. We propose a related scheme for understanding how, under these
limitations, learners choose informative interventions that manipulate the
causal system to help elucidate its workings. We find support for our approach
in the analysis of four experiments.
Design and Implementation of Modified Fuzzy based CPU Scheduling Algorithm
Authors: Rajani Kumari, Vivek Kumar Sharma, Sandeep Kumar
Date: 2017-05-26 00:00:00
Tasks: CPU, Scheduling

CPU Scheduling is the base of multiprogramming. Scheduling is a process which decides order of task from a set of multiple tasks that are ready to execute. There are number of CPU scheduling algorithms available, but it is very difficult task to decide which one is better. This paper discusses the design and implementation of modified fuzzy based CPU scheduling algorithm. This paper present a new set of fuzzy rules. It demonstrates that scheduling done with new priority improves average waiting time and average turnaround time.
Learning Causal Structures Using Regression Invariance
Authors: AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang
Date: 2017-05-26 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Causal Inference, regression

We study causal inference in a multi-environment setting, in which the
functional relations for producing the variables from their direct causes
remain the same across environments, while the distribution of exogenous noises
may vary. We introduce the idea of using the invariance of the functional
relations of the variables to their causes across a set of environments. We
define a notion of completeness for a causal inference algorithm in this
setting and prove the existence of such algorithm by proposing the baseline
algorithm. Additionally, we present an alternate algorithm that has
significantly improved computational and sample complexity compared to the
baseline algorithm. The experiment results show that the proposed algorithm
outperforms the other existing algorithms.
Adaptive Training of Random Mapping for Data Quantization
Authors: Miao Cheng, Ah Chung Tsoi
Date: 2016-06-28 00:00:00
Tasks: Quantization

Data quantization learns encoding results of data with certain requirements,
and provides a broad perspective of many real-world applications to data
handling. Nevertheless, the results of encoder is usually limited to
multivariate inputs with the random mapping, and side information of binary
codes are hardly to mostly depict the original data patterns as possible. In
the literature, cosine based random quantization has attracted much attentions
due to its intrinsic bounded results. Nevertheless, it usually suffers from the
uncertain outputs, and information of original data fails to be fully preserved
in the reduced codes. In this work, a novel binary embedding method, termed
adaptive training quantization (ATQ), is proposed to learn the ideal transform
of random encoder, where the limitation of cosine random mapping is tackled. As
an adaptive learning idea, the reduced mapping is adaptively calculated with
idea of data group, while the bias of random transform is to be improved to
hold most matching information. Experimental results show that the proposed
method is able to obtain outstanding performance compared with other random
quantization methods.
Computational Eco-Systems for Handwritten Digits Recognition
Authors: Antonio Loquercio, Francesca Della Torre, Massimo Buscema
Date: 2017-03-06 00:00:00
Tasks: BIG-bench Machine Learning, Diversity, General Classification

Inspired by the importance of diversity in biological system, we built an
heterogeneous system that could achieve this goal. Our architecture could be
summarized in two basic steps. First, we generate a diverse set of
classification hypothesis using both Convolutional Neural Networks, currently
the state-of-the-art technique for this task, among with other traditional and
innovative machine learning techniques. Then, we optimally combine them through
Meta-Nets, a family of recently developed and performing ensemble methods.
Combinatorial Multi-Armed Bandits with Filtered Feedback
Authors: James A. Grant, David S. Leslie, Kevin Glazebrook, Roberto Szechtman
Date: 2017-05-26 00:00:00
Tasks: Multi-Armed Bandits

Motivated by problems in search and detection we present a solution to a
Combinatorial Multi-Armed Bandit (CMAB) problem with both heavy-tailed reward
distributions and a new class of feedback, filtered semibandit feedback. In a
CMAB problem an agent pulls a combination of arms from a set $\{1,...,k\}$ in
each round, generating random outcomes from probability distributions
associated with these arms and receiving an overall reward. Under semibandit
feedback it is assumed that the random outcomes generated are all observed.
Filtered semibandit feedback allows the outcomes that are observed to be
sampled from a second distribution conditioned on the initial random outcomes.
This feedback mechanism is valuable as it allows CMAB methods to be applied to
sequential search and detection problems where combinatorial actions are made,
but the true rewards (number of objects of interest appearing in the round) are
not observed, rather a filtered reward (the number of objects the searcher
successfully finds, which must by definition be less than the number that
appear). We present an upper confidence bound type algorithm, Robust-F-CUCB,
and associated regret bound of order $\mathcal{O}(\ln(n))$ to balance
exploration and exploitation in the face of both filtering of reward and heavy
tailed reward distributions.
Learning a Robust Society of Tracking Parts
Authors: Elena Burceanu, Marius Leordeanu
Date: 2017-05-26 00:00:00
Tasks: Object Tracking

Object tracking is an essential task in computer vision that has been studied
since the early days of the field. Being able to follow objects that undergo
different transformations in the video sequence, including changes in scale,
illumination, shape and occlusions, makes the problem extremely difficult. One
of the real challenges is to keep track of the changes in objects appearance
and not drift towards the background clutter. Different from previous
approaches, we obtain robustness against background with a tracker model that
is composed of many different parts. They are classifiers that respond at
different scales and locations. The tracker system functions as a society of
parts, each having its own role and level of credibility. Reliable classifiers
decide the tracker's next move, while newcomers are first monitored before
gaining the necessary level of reliability to participate in the decision
process. Some parts that loose their consistency are rejected, while others
that show consistency for a sufficiently long time are promoted to permanent
roles. The tracker system, as a whole, could also go through different phases,
from the usual, normal functioning to states of weak agreement and even crisis.
The tracker system has different governing rules in each state. What truly
distinguishes our work from others is not necessarily the strength of
individual tracking parts, but the way in which they work together and build a
strong and robust organization. We also propose an efficient way to learn
simultaneously many tracking parts, with a single closed-form formulation. We
obtain a fast and robust tracker with state of the art performance on the
challenging OTB50 dataset.
Density Estimation in Infinite Dimensional Exponential Families
Authors: Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar
Date: 2013-12-12 00:00:00
Tasks: Density Estimation

In this paper, we consider an infinite dimensional exponential family,
$\mathcal{P}$ of probability densities, which are parametrized by functions in
a reproducing kernel Hilbert space, $H$ and show it to be quite rich in the
sense that a broad class of densities on $\mathbb{R}^d$ can be approximated
arbitrarily well in Kullback-Leibler (KL) divergence by elements in
$\mathcal{P}$. The main goal of the paper is to estimate an unknown density,
$p_0$ through an element in $\mathcal{P}$. Standard techniques like maximum
likelihood estimation (MLE) or pseudo MLE (based on the method of sieves),
which are based on minimizing the KL divergence between $p_0$ and
$\mathcal{P}$, do not yield practically useful estimators because of their
inability to efficiently handle the log-partition function. Instead, we propose
an estimator, $\hat{p}_n$ based on minimizing the \emph{Fisher divergence},
$J(p_0\Vert p)$ between $p_0$ and $p\in \mathcal{P}$, which involves solving a
simple finite-dimensional linear system. When $p_0\in\mathcal{P}$, we show that
the proposed estimator is consistent, and provide a convergence rate of
$n^{-\min\left\{\frac{2}{3},\frac{2\beta+1}{2\beta+2}\right\}}$ in Fisher
divergence under the smoothness assumption that $\log
p_0\in\mathcal{R}(C^\beta)$ for some $\beta\ge 0$, where $C$ is a certain
Hilbert-Schmidt operator on $H$ and $\mathcal{R}(C^\beta)$ denotes the image of
$C^\beta$. We also investigate the misspecified case of $p_0\notin\mathcal{P}$
and show that $J(p_0\Vert\hat{p}_n)\rightarrow \inf_{p\in\mathcal{P}}J(p_0\Vert
p)$ as $n\rightarrow\infty$, and provide a rate for this convergence under a
similar smoothness condition as above. Through numerical simulations we
demonstrate that the proposed estimator outperforms the non-parametric kernel
density estimator, and that the advantage with the proposed estimator grows as
$d$ increases.
Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks
Authors: Russell Bates, Benjamin Irving, Bostjan Markelc, Jakob Kaeppler, Ruth Muschel, Vicente Grau, Julia A. Schnabel
Date: 2017-05-26 00:00:00

Vasculature is known to be of key biological significance, especially in the
study of cancer. As such, considerable effort has been focused on the automated
measurement and analysis of vasculature in medical and pre-clinical images. In
tumors in particular, the vascular networks may be extremely irregular and the
appearance of the individual vessels may not conform to classical descriptions
of vascular appearance. Typically, vessels are extracted by either a
segmentation and thinning pipeline, or by direct tracking. Neither of these
methods are well suited to microscopy images of tumor vasculature. In order to
address this we propose a method to directly extract a medial representation of
the vessels using Convolutional Neural Networks. We then show that these
two-dimensional centerlines can be meaningfully extended into 3D in anisotropic
and complex microscopy images using the recently popularized Convolutional Long
Short-Term Memory units (ConvLSTM). We demonstrate the effectiveness of this
hybrid convolutional-recurrent architecture over both 2D and 3D convolutional
comparators.
DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels
Authors: Paul Guerrero, Holger Winnemöller, Wilmot Li, Niloy J. Mitra
Date: 2017-05-22 00:00:00
Tasks: Scene Understanding, Segmentation

In the context of scene understanding, a variety of methods exists to
estimate different information channels from mono or stereo images, including
disparity, depth, and normals. Although several advances have been reported in
the recent years for these tasks, the estimated information is often imprecise
particularly near depth discontinuities or creases. Studies have however shown
that precisely such depth edges carry critical cues for the perception of
shape, and play important roles in tasks like depth-based segmentation or
foreground selection. Unfortunately, the currently extracted channels often
carry conflicting signals, making it difficult for subsequent applications to
effectively use them. In this paper, we focus on the problem of obtaining
high-precision depth edges (i.e., depth contours and creases) by jointly
analyzing such unreliable information channels. We propose DepthCut, a
data-driven fusion of the channels using a convolutional neural network trained
on a large dataset with known depth. The resulting depth edges can be used for
segmentation, decomposing a scene into depth layers with relatively flat depth,
or improving the accuracy of the depth estimate near depth edges by
constraining its gradients to agree with these edges. Quantitatively, we
compare against 15 variants of baselines and demonstrate that our depth edges
result in an improved segmentation performance and an improved depth estimate
near depth edges compared to data-agnostic channel fusion. Qualitatively, we
demonstrate that the depth edges result in superior segmentation and depth
orderings.
Enhancement of SSD by concatenating feature maps for object detection
Authors: Jisoo Jeong, Hyojin Park, Nojun Kwak
Date: 2017-05-26 00:00:00
Tasks: GPU, object-detection, Object Detection

We propose an object detection method that improves the accuracy of the
conventional SSD (Single Shot Multibox Detector), which is one of the top
object detection algorithms in both aspects of accuracy and speed. The
performance of a deep network is known to be improved as the number of feature
maps increases. However, it is difficult to improve the performance by simply
raising the number of feature maps. In this paper, we propose and analyze how
to use feature maps effectively to improve the performance of the conventional
SSD. The enhanced performance was obtained by changing the structure close to
the classifier network, rather than growing layers close to the input data,
e.g., by replacing VGGNet with ResNet. The proposed network is suitable for
sharing the weights in the classifier networks, by which property, the training
can be faster with better generalization power. For the Pascal VOC 2007 test
set trained with VOC 2007 and VOC 2012 training sets, the proposed network with
the input size of 300 x 300 achieved 78.5% mAP (mean average precision) at the
speed of 35.0 FPS (frame per second), while the network with a 512 x 512 sized
input achieved 80.8% mAP at 16.6 FPS using Nvidia Titan X GPU. The proposed
network shows state-of-the-art mAP, which is better than those of the
conventional SSD, YOLO, Faster-RCNN and RFCN. Also, it is faster than
Faster-RCNN and RFCN.