pwc-archive/papers-with-abstracts huggingface dataset:
Fast k-Nearest Neighbour Search via Prioritized DCI
Authors: Ke Li, Jitendra Malik
Date: 2017-03-01 00:00:00
Proceeding: ICML 2017 8

Most exact methods for k-nearest neighbour search suffer from the curse of
dimensionality; that is, their query times exhibit exponential dependence on
either the ambient or the intrinsic dimensionality. Dynamic Continuous Indexing
(DCI) offers a promising way of circumventing the curse and successfully
reduces the dependence of query time on intrinsic dimensionality from
exponential to sublinear. In this paper, we propose a variant of DCI, which we
call Prioritized DCI, and show a remarkable improvement in the dependence of
query time on intrinsic dimensionality. In particular, a linear increase in
intrinsic dimensionality, or equivalently, an exponential increase in the
number of points near a query, can be mostly counteracted with just a linear
increase in space. We also demonstrate empirically that Prioritized DCI
significantly outperforms prior methods. In particular, relative to
Locality-Sensitive Hashing (LSH), Prioritized DCI reduces the number of
distance evaluations by a factor of 14 to 116 and the memory consumption by a
factor of 21.
Decoupled classifiers for fair and efficient machine learning
Authors: Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, Max Leiserson
Date: 2017-07-20 00:00:00
Tasks: Attribute, BIG-bench Machine Learning, Fairness, Transfer Learning

When it is ethical and legal to use a sensitive attribute (such as gender or
race) in machine learning systems, the question remains how to do so. We show
that the naive application of machine learning algorithms using sensitive
features leads to an inherent tradeoff in accuracy between groups. We provide a
simple and efficient decoupling technique, that can be added on top of any
black-box machine learning algorithm, to learn different classifiers for
different groups. Transfer learning is used to mitigate the problem of having
too little data on any one group.
  The method can apply to a range of fairness criteria. In particular, we
require the application designer to specify as joint loss function that makes
explicit the trade-off between fairness and accuracy. Our reduction is shown to
efficiently find the minimum loss as long as the objective has a certain
natural monotonicity property which may be of independent interest in the study
of fairness in algorithms.
Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments
Authors: Anton Andreychuk, Konstantin Yakovlev
Date: 2017-07-20 00:00:00

The paper considers the problem of planning a set of non-conflict
trajectories for the coalition of intelligent agents (mobile robots). Two
divergent approaches, e.g. centralized and decentralized, are surveyed and
analyzed. Decentralized planner - MAPP is described and applied to the task of
finding trajectories for dozens UAVs performing nap-of-the-earth flight in
urban environments. Results of the experimental studies provide an opportunity
to claim that MAPP is a highly efficient planner for solving considered types
of tasks.
Nonlinear network-based quantitative trait prediction from transcriptomic data
Authors: Emilie Devijver, Mélina Gallopin, Emeline Perthame
Date: 2017-01-26 00:00:00

Quantitatively predicting phenotype variables by the expression changes in a
set of candidate genes is of great interest in molecular biology but it is also
a challenging task for several reasons. First, the collected biological
observations might be heterogeneous and correspond to different biological
mechanisms. Secondly, the gene expression variables used to predict the
phenotype are potentially highly correlated since genes interact though unknown
regulatory networks. In this paper, we present a novel approach designed to
predict quantitative trait from transcriptomic data, taking into account the
heterogeneity in biological samples and the hidden gene regulatory networks
underlying different biological mechanisms. The proposed model performs well on
prediction but it is also fully parametric, which facilitates the downstream
biological interpretation. The model provides clusters of individuals based on
the relation between gene expression data and the phenotype, and also leads to
infer a gene regulatory network specific for each cluster of individuals. We
perform numerical simulations to demonstrate that our model is competitive with
other prediction models, and we demonstrate the predictive performance and the
interpretability of our model to predict alcohol sensitivity from
transcriptomic data on real data from Drosophila Melanogaster Genetic Reference
Panel (DGRP).
Structural Learning and Integrative Decomposition of Multi-View Data
Authors: Irina Gaynanova, Gen Li
Date: 2017-07-20 00:00:00
Tasks: Clustering, Dimensionality Reduction

The increased availability of the multi-view data (data on the same samples
from multiple sources) has led to strong interest in models based on low-rank
matrix factorizations. These models represent each data view via shared and
individual components, and have been successfully applied for exploratory
dimension reduction, association analysis between the views, and further
learning tasks such as consensus clustering. Despite these advances, there
remain significant challenges in modeling partially-shared components, and
identifying the number of components of each type
(shared/partially-shared/individual). In this work, we formulate a novel linked
component model that directly incorporates partially-shared structures. We call
this model SLIDE for Structural Learning and Integrative DEcomposition of
multi-view data. We prove the existence of SLIDE decomposition and explicitly
characterize the identifiability conditions. The proposed model fitting and
selection techniques allow for joint identification of the number of components
of each type, in contrast to existing sequential approaches. In our empirical
studies, SLIDE demonstrates excellent performance in both signal estimation and
component selection. We further illustrate the methodology on the breast cancer
data from The Cancer Genome Atlas repository.
Pictures of Combinatorial Cubes
Authors: André Wagner
Date: 2017-07-20 00:00:00

We prove that the 8-point algorithm always fails to reconstruct a unique
fundamental matrix $F$ independent on the camera positions, when its input are
image point configurations that are perspective projections of the vertices of
a combinatorial cube in $\mathbb{R}^3$. We give an algorithm that improves the
7- and 8-point algorithm in such a pathological situation. Additionally we
analyze the regions of focal point positions where a reconstruction of $F$ is
possible at all, when the world points are the vertices of a combinatorial cube
in $\mathbb{R}^3$.
From Task Classification Towards Similarity Measures for Recommendation in Crowdsourcing Systems
Authors: Steffen Schnitzer, Svenja Neitzel, Christoph Rensing
Date: 2017-07-20 00:00:00
Tasks: General Classification, Recommendation Systems

Task selection in micro-task markets can be supported by recommender systems
to help individuals to find appropriate tasks. Previous work showed that for
the selection process of a micro-task the semantic aspects, such as the
required action and the comprehensibility, are rated more important than
factual aspects, such as the payment or the required completion time. This work
gives a foundation to create such similarity measures. Therefore, we show that
an automatic classification based on task descriptions is possible.
Additionally, we propose similarity measures to cluster micro-tasks according
to semantic aspects.
leave a trace - A People Tracking System Meets Anomaly Detection
Authors: Dominik Rueß, Konstantinos Amplianitis, Niklas Deckers, Michele Adduci, Kristian Manthey, Ralf Reulke
Date: 2017-07-20 00:00:00
Tasks: Anomaly Detection

Video surveillance always had a negative connotation, among others because of
the loss of privacy and because it may not automatically increase public
safety. If it was able to detect atypical (i.e. dangerous) situations in real
time, autonomously and anonymously, this could change. A prerequisite for this
is a reliable automatic detection of possibly dangerous situations from video
data. This is done classically by object extraction and tracking. From the
derived trajectories, we then want to determine dangerous situations by
detecting atypical trajectories. However, due to ethical considerations it is
better to develop such a system on data without people being threatened or even
harmed, plus with having them know that there is such a tracking system
installed. Another important point is that these situations do not occur very
often in real, public CCTV areas and may be captured properly even less. In the
artistic project leave a trace the tracked objects, people in an atrium of a
institutional building, become actor and thus part of the installation.
Visualisation in real-time allows interaction by these actors, which in turn
creates many atypical interaction situations on which we can develop our
situation detection. The data set has evolved over three years and hence, is
huge. In this article we describe the tracking system and several approaches
for the detection of atypical trajectories.
High-risk learning: acquiring new word vectors from tiny data
Authors: Aurelie Herbelot, Marco Baroni
Date: 2017-07-20 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Language Modeling, Language Modelling, Small Data Image Classification, Vocal Bursts Intensity Prediction

Distributional semantics models are known to struggle with small data. It is
generally accepted that in order to learn 'a good vector' for a word, a model
must have sufficient examples of its usage. This contradicts the fact that
humans can guess the meaning of a word from a few occurrences only. In this
paper, we show that a neural language model such as Word2Vec only necessitates
minor modifications to its standard architecture to learn new terms from tiny
data, using background knowledge from a previously learnt semantic space. We
test our model on word definitions and on a nonce task involving 2-6 sentences'
worth of context, showing a large increase in performance over state-of-the-art
models on the definitional task.
Field-Programmable Crossbar Array (FPCA) for Reconfigurable Computing
Authors: Mohammed A. Zidan, YeonJoo Jeong, Jong Hong Shin, Chao Du, Zhengya Zhang, Wei D. Lu
Date: 2016-12-09 00:00:00

For decades, advances in electronics were directly driven by the scaling of
CMOS transistors according to Moore's law. However, both the CMOS scaling and
the classical computer architecture are approaching fundamental and practical
limits, and new computing architectures based on emerging devices, such as
resistive random-access memory (RRAM) devices, are expected to sustain the
exponential growth of computing capability. Here we propose a novel
memory-centric, reconfigurable, general purpose computing platform that is
capable of handling the explosive amount of data in a fast and energy-efficient
manner. The proposed computing architecture is based on a uniform, physical,
resistive, memory-centric fabric that can be optimally reconfigured and
utilized to perform different computing and data storage tasks in a massively
parallel approach. The system can be tailored to achieve maximal energy
efficiency based on the data flow by dynamically allocating the basic computing
fabric for storage, arithmetic, and analog computing including neuromorphic
computing tasks.
Connecting the dots across time: Reconstruction of single cell signaling trajectories using time-stamped data
Authors: Sayak Mukherjee, David Stewart, William Stewart, Lewis L. Lanier, Jayajit Das
Date: 2016-09-26 00:00:00

Single cell responses are shaped by the geometry of signaling kinetic
trajectories carved in a multidimensional space spanned by signaling protein
abundances. It is however challenging to assay large number (>3) of signaling
species in live-cell imaging which makes it difficult to probe single cell
signaling kinetic trajectories in large dimensions. Flow and mass cytometry
techniques can measure a large number (4 - >40) of signaling species but are
unable to track single cells. Thus cytometry experiments provide detailed time
stamped snapshots of single cell signaling kinetics. Is it possible to use the
time stamped cytometry data to reconstruct single cell signaling trajectories?
Borrowing concepts of conserved and slow variables from non-equilibrium
statistical physics we develop an approach to reconstruct signaling
trajectories using snapshot data by creating new variables that remain
invariant or vary slowly during the signaling kinetics. We apply this approach
to reconstruct trajectories using snapshot data obtained from in silico
simulations and live-cell imaging measurements. The use of invariants and slow
variables to reconstruct trajectories provides a radically different way to
track object using snapshot data. The approach is likely to have implications
for solving matching problems in a wide range of disciplines.
Video Object Segmentation using Tracked Object Proposals
Authors: Gilad Sharir, Eddie Smolyansky, Itamar Friedman
Date: 2017-07-20 00:00:00
Tasks: Object, object-detection, Object Detection, Object Tracking, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

We present an approach to semi-supervised video object segmentation, in the
context of the DAVIS 2017 challenge. Our approach combines category-based
object detection, category-independent object appearance segmentation and
temporal object tracking. We are motivated by the fact that the objects
semantic category tends not to change throughout the video while its appearance
and location can vary considerably. In order to capture the specific object
appearance independent of its category, for each video we train a fully
convolutional network using augmentations of the given annotated frame. We
refine the appearance segmentation mask with the bounding boxes provided either
by a semantic object detection network, when applicable, or by a previous frame
prediction. By introducing a temporal continuity constraint on the detected
boxes, we are able to improve the object segmentation mask of the appearance
network and achieve competitive results on the DAVIS datasets.
An All-in-One Network for Dehazing and Beyond
Authors: Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, Dan Feng
Date: 2017-07-20 00:00:00
Tasks: All, Image Dehazing, object-detection, Object Detection, SSIM

This paper proposes an image dehazing model built with a convolutional neural
network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed
based on a re-formulated atmospheric scattering model. Instead of estimating
the transmission matrix and the atmospheric light separately as most previous
models did, AOD-Net directly generates the clean image through a light-weight
CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other
deep models, e.g., Faster R-CNN, for improving high-level task performance on
hazy images. Experimental results on both synthesized and natural hazy image
datasets demonstrate our superior performance than the state-of-the-art in
terms of PSNR, SSIM and the subjective visual quality. Furthermore, when
concatenating AOD-Net with Faster R-CNN and training the joint pipeline from
end to end, we witness a large improvement of the object detection performance
on hazy images.
Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data
Authors: Gil Keren, Björn Schuller
Date: 2016-02-18 00:00:00
Tasks: Audio Classification

Traditional convolutional layers extract features from patches of data by
applying a non-linearity on an affine function of the input. We propose a model
that enhances this feature extraction process for the case of sequential data,
by feeding patches of the data into a recurrent neural network and using the
outputs or hidden states of the recurrent units to compute the extracted
features. By doing so, we exploit the fact that a window containing a few
frames of the sequential data is a sequence itself and this additional
structure might encapsulate valuable information. In addition, we allow for
more steps of computation in the feature extraction process, which is
potentially beneficial as an affine function followed by a non-linearity can
result in too simple features. Using our convolutional recurrent layers we
obtain an improvement in performance in two audio classification tasks,
compared to traditional convolutional layers. Tensorflow code for the
convolutional recurrent layers is publicly available in
https://github.com/cruvadom/Convolutional-RNN.
Generalized orderless pooling performs implicit salient matching
Authors: Marcel Simon, Yang Gao, Trevor Darrell, Joachim Denzler, Erik Rodner
Date: 2017-05-01 00:00:00
Proceeding: ICCV 2017 10

Most recent CNN architectures use average pooling as a final feature encoding
step. In the field of fine-grained recognition, however, recent global
representations like bilinear pooling offer improved performance. In this
paper, we generalize average and bilinear pooling to "alpha-pooling", allowing
for learning the pooling strategy during training. In addition, we present a
novel way to visualize decisions made by these approaches. We identify parts of
training images having the highest influence on the prediction of a given test
image. It allows for justifying decisions to users and also for analyzing the
influence of semantic parts. For example, we can show that the higher capacity
VGG16 model focuses much more on the bird's head than, e.g., the lower-capacity
VGG-M model when recognizing fine-grained bird categories. Both contributions
allow us to analyze the difference when moving between average and bilinear
pooling. In addition, experiments show that our generalized approach can
outperform both across a variety of standard datasets.
Robust Semi-Supervised Graph Classifier Learning with Negative Edge Weights
Authors: Gene Cheung, Weng-Tai Su, Yu Mao, Chia-Wen Lin
Date: 2016-11-15 00:00:00

In a semi-supervised learning scenario, (possibly noisy) partially observed
labels are used as input to train a classifier, in order to assign labels to
unclassified samples. In this paper, we study this classifier learning problem
from a graph signal processing (GSP) perspective. Specifically, by viewing a
binary classifier as a piecewise constant graph-signal in a high-dimensional
feature space, we cast classifier learning as a signal restoration problem via
a classical maximum a posteriori (MAP) formulation. Unlike previous
graph-signal restoration works, we consider in addition edges with negative
weights that signify anti-correlation between samples. One unfortunate
consequence is that the graph Laplacian matrix $\mathbf{L}$ can be indefinite,
and previously proposed graph-signal smoothness prior $\mathbf{x}^T \mathbf{L}
\mathbf{x}$ for candidate signal $\mathbf{x}$ can lead to pathological
solutions. In response, we derive an optimal perturbation matrix
$\boldsymbol{\Delta}$ - based on a fast lower-bound computation of the minimum
eigenvalue of $\mathbf{L}$ via a novel application of the Haynsworth inertia
additivity formula---so that $\mathbf{L} + \boldsymbol{\Delta}$ is positive
semi-definite, resulting in a stable signal prior. Further, instead of forcing
a hard binary decision for each sample, we define the notion of generalized
smoothness on graph that promotes ambiguity in the classifier signal. Finally,
we propose an algorithm based on iterative reweighted least squares (IRLS) that
solves the posed MAP problem efficiently. Extensive simulation results show
that our proposed algorithm outperforms both SVM variants and graph-based
classifiers using positive-edge graphs noticeably.
Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones
Authors: Zhenisbek Assylbekov, Rustem Takhanov, Bagdat Myrzakhmetov, Jonathan N. Washington
Date: 2017-07-20 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Language Modeling, Language Modelling, Segmentation

Syllabification does not seem to improve word-level RNN language modeling
quality when compared to character-based segmentation. However, our best
syllable-aware language model, achieving performance comparable to the
competitive character-aware model, has 18%-33% fewer parameters and is trained
1.2-2.2 times faster.
The Inflation Technique Completely Solves the Causal Compatibility Problem
Authors: Miguel Navascues, Elie Wolfe
Date: 2017-07-20 00:00:00
Tasks: Causal Inference

The causal compatibility question asks whether a given causal structure graph -- possibly involving latent variables -- constitutes a genuinely plausible causal explanation for a given probability distribution over the graph's observed variables. Algorithms predicated on merely necessary constraints for causal compatibility typically suffer from false negatives, i.e. they admit incompatible distributions as apparently compatible with the given graph. In [arXiv:1609.00672], one of us introduced the inflation technique for formulating useful relaxations of the causal compatibility problem in terms of linear programming. In this work, we develop a formal hierarchy of such causal compatibility relaxations. We prove that inflation is asymptotically tight, i.e., that the hierarchy converges to a zero-error test for causal compatibility. In this sense, the inflation technique fulfills a longstanding desideratum in the field of causal inference. We quantify the rate of convergence by showing that any distribution which passes the $n^{th}$-order inflation test must be $O\left(n^{-1/2}\right)$-close in Euclidean norm to some distribution genuinely compatible with the given causal structure. Furthermore, we show that for many causal structures, the (unrelaxed) causal compatibility problem is faithfully formulated already by either the first or second order inflation test.
Revisiting Selectional Preferences for Coreference Resolution
Authors: Benjamin Heinzerling, Nafise Sadat Moosavi, Michael Strube
Date: 2017-07-20 00:00:00
Proceeding: EMNLP 2017 9
Tasks: coreference-resolution, Coreference Resolution

Selectional preferences have long been claimed to be essential for
coreference resolution. However, they are mainly modeled only implicitly by
current coreference resolvers. We propose a dependency-based embedding model of
selectional preferences which allows fine-grained compatibility judgments with
high coverage. We show that the incorporation of our model improves coreference
resolution performance on the CoNLL dataset, matching the state-of-the-art
results of a more complex system. However, it comes with a cost that makes it
debatable how worthwhile such improvements are.