pwc-archive/papers-with-abstracts huggingface dataset:
Understanding Aesthetics in Photography using Deep Convolutional Neural Networks
Authors: Maciej Suchecki, Tomasz Trzcinski
Date: 2017-07-27 00:00:00

Evaluating aesthetic value of digital photographs is a challenging task,
mainly due to numerous factors that need to be taken into account and
subjective manner of this process. In this paper, we propose to approach this
problem using deep convolutional neural networks. Using a dataset of over 1.7
million photos collected from Flickr, we train and evaluate a deep learning
model whose goal is to classify input images by analysing their aesthetic
value. The result of this work is a publicly available Web-based application
that can be used in several real-life applications, e.g. to improve the
workflow of professional photographers by pre-selecting the best photos.
Time-Space Tradeoffs for Learning from Small Test Spaces: Learning Low Degree Polynomial Functions
Authors: Paul Beame, Shayan Oveis Gharan, Xin Yang
Date: 2017-08-08 00:00:00

We develop an extension of recently developed methods for obtaining
time-space tradeoff lower bounds for problems of learning from random test
samples to handle the situation where the space of tests is signficantly
smaller than the space of inputs, a class of learning problems that is not
handled by prior work. This extension is based on a measure of how matrices
amplify the 2-norms of probability distributions that is more refined than the
2-norms of these matrices.
  As applications that follow from our new technique, we show that any
algorithm that learns $m$-variate homogeneous polynomial functions of degree at
most $d$ over $\mathbb{F}_2$ from evaluations on randomly chosen inputs either
requires space $\Omega(mn)$ or $2^{\Omega(m)}$ time where $n=m^{\Theta(d)}$ is
the dimension of the space of such functions. These bounds are asymptotically
optimal since they match the tradeoffs achieved by natural learning algorithms
for the problems.
Extractor-Based Time-Space Lower Bounds for Learning
Authors: Sumegha Garg, Ran Raz, Avishay Tal
Date: 2017-08-08 00:00:00

A matrix $M: A \times X \rightarrow \{-1,1\}$ corresponds to the following
learning problem: An unknown element $x \in X$ is chosen uniformly at random. A
learner tries to learn $x$ from a stream of samples, $(a_1, b_1), (a_2, b_2)
\ldots$, where for every $i$, $a_i \in A$ is chosen uniformly at random and
$b_i = M(a_i,x)$.
  Assume that $k,\ell, r$ are such that any submatrix of $M$ of at least
$2^{-k} \cdot |A|$ rows and at least $2^{-\ell} \cdot |X|$ columns, has a bias
of at most $2^{-r}$. We show that any learning algorithm for the learning
problem corresponding to $M$ requires either a memory of size at least
$\Omega\left(k \cdot \ell \right)$, or at least $2^{\Omega(r)}$ samples. The
result holds even if the learner has an exponentially small success probability
(of $2^{-\Omega(r)}$).
  In particular, this shows that for a large class of learning problems, any
learning algorithm requires either a memory of size at least $\Omega\left((\log
|X|) \cdot (\log |A|)\right)$ or an exponential number of samples, achieving a
tight $\Omega\left((\log |X|) \cdot (\log |A|)\right)$ lower bound on the size
of the memory, rather than a bound of $\Omega\left(\min\left\{(\log
|X|)^2,(\log |A|)^2\right\}\right)$ obtained in previous works [R17,MM17b].
  Moreover, our result implies all previous memory-samples lower bounds, as
well as a number of new applications.
  Our proof builds on [R17] that gave a general technique for proving
memory-samples lower bounds.
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
Authors: Heng-Tze Cheng, Zakaria Haque, Lichan Hong, Mustafa Ispir, Clemens Mewald, Illia Polosukhin, Georgios Roumpos, D. Sculley, Jamie Smith, David Soergel, Yuan Tang, Philipp Tucker, Martin Wicke, Cassandra Xia, Jianwei Xie
Date: 2017-08-08 00:00:00
Tasks: BIG-bench Machine Learning

We present a framework for specifying, training, evaluating, and deploying
machine learning models. Our focus is on simplifying cutting edge machine
learning for practitioners in order to bring such technologies into production.
Recognizing the fast evolution of the field of deep learning, we make no
attempt to capture the design space of all possible model architectures in a
domain- specific language (DSL) or similar configuration language. We allow
users to write code to define their models, but provide abstractions that guide
develop- ers to write models in ways conducive to productionization. We also
provide a unifying Estimator interface, making it possible to write downstream
infrastructure (e.g. distributed training, hyperparameter tuning) independent
of the model implementation. We balance the competing demands for flexibility
and simplicity by offering APIs at different levels of abstraction, making
common model architectures available out of the box, while providing a library
of utilities designed to speed up experimentation with model architectures. To
make out of the box models flexible and usable across a wide range of problems,
these canned Estimators are parameterized not only over traditional
hyperparameters, but also using feature columns, a declarative specification
describing how to interpret input data. We discuss our experience in using this
framework in re- search and production environments, and show the impact on
code health, maintainability, and development speed.
Protecting Genomic Privacy by a Sequence-Similarity Based Obfuscation Method
Authors: Shibiao Wan, Man-Wai Mak, Sun-Yuan Kung
Date: 2017-08-08 00:00:00
Tasks: Drug Discovery

In the post-genomic era, large-scale personal DNA sequences are produced and
collected for genetic medical diagnoses and new drug discovery, which, however,
simultaneously poses serious challenges to the protection of personal genomic
privacy. Existing genomic privacy-protection methods are either time-consuming
or with low accuracy. To tackle these problems, this paper proposes a sequence
similarity-based obfuscation method, namely IterMegaBLAST, for fast and
reliable protection of personal genomic privacy. Specifically, given a randomly
selected sequence from a dataset of DNA sequences, we first use MegaBLAST to
find its most similar sequence from the dataset. These two aligned sequences
form a cluster, for which an obfuscated sequence was generated via a DNA
generalization lattice scheme. These procedures are iteratively performed until
all of the sequences in the dataset are clustered and their obfuscated
sequences are generated. Experimental results on two benchmark datasets
demonstrate that under the same degree of anonymity, IterMegaBLAST
significantly outperforms existing state-of-the-art approaches in terms of both
utility accuracy and time complexity.
Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms
Authors: Pin-Yu Chen, Alfred O. Hero
Date: 2017-08-08 00:00:00
Tasks: Clustering, Graph Clustering, Spectral Graph Clustering

Multilayer graphs are commonly used for representing different relations
between entities and handling heterogeneous data processing tasks. Non-standard
multilayer graph clustering methods are needed for assigning clusters to a
common multilayer node set and for combining information from each layer. This
paper presents a multilayer spectral graph clustering (SGC) framework that
performs convex layer aggregation. Under a multilayer signal plus noise model,
we provide a phase transition analysis of clustering reliability. Moreover, we
use the phase transition criterion to propose a multilayer iterative model
order selection algorithm (MIMOSA) for multilayer SGC, which features automated
cluster assignment and layer weight adaptation, and provides statistical
clustering reliability guarantees. Numerical simulations on synthetic
multilayer graphs verify the phase transition analysis, and experiments on
real-world multilayer graphs show that MIMOSA is competitive or better than
other clustering methods.
Learning Feedforward and Recurrent Deterministic Spiking Neuron Network Feedback Controllers
Authors: Tae Seung Kang, Arunava Banerjee
Date: 2017-08-08 00:00:00

We address the problem of learning feedback control where the controller is a
network constructed solely of deterministic spiking neurons. In contrast to
previous investigations that were based on a spike rate model of the neuron,
the control signal here is determined by the precise temporal positions of
spikes generated by the output neurons of the network. We model the problem
formally as a hybrid dynamical system comprised of a closed loop between a
plant and a spiking neuron network. We derive a novel synaptic weight update
rule via which the spiking neuron network controller learns to hold process
variables at desired set points. The controller achieves its learning objective
based solely on access to the plant's process variables and their derivatives
with respect to changing control signals; in particular, it requires no
internal model of the plant. We demonstrate the efficacy of the rule by
applying it to the classical control problem of the cart-pole (inverted
pendulum) and a model of fish locomotion. Experiments show that the proposed
controller has a stability region comparable to a traditional PID controller,
its trajectories differ qualitatively from those of a PID controller, and in
many instances the controller achieves its objective using very sparse spike
train outputs.
Peak Criterion for Choosing Gaussian Kernel Bandwidth in Support Vector Data Description
Authors: Deovrat Kakde, Arin Chaudhuri, Seunghyun Kong, Maria Jahja, Hansi Jiang, Jorge Silva
Date: 2016-02-17 00:00:00
Tasks: General Classification, Outlier Detection

Support Vector Data Description (SVDD) is a machine-learning technique used
for single class classification and outlier detection. SVDD formulation with
kernel function provides a flexible boundary around data. The value of kernel
function parameters affects the nature of the data boundary. For example, it is
observed that with a Gaussian kernel, as the value of kernel bandwidth is
lowered, the data boundary changes from spherical to wiggly. The spherical data
boundary leads to underfitting, and an extremely wiggly data boundary leads to
overfitting. In this paper, we propose empirical criterion to obtain good
values of the Gaussian kernel bandwidth parameter. This criterion provides a
smooth boundary that captures the essential geometric features of the data.
Belief Propagation, Bethe Approximation and Polynomials
Authors: Damian Straszak, Nisheeth K. Vishnoi
Date: 2017-08-08 00:00:00

Factor graphs are important models for succinctly representing probability
distributions in machine learning, coding theory, and statistical physics.
Several computational problems, such as computing marginals and partition
functions, arise naturally when working with factor graphs. Belief propagation
is a widely deployed iterative method for solving these problems. However,
despite its significant empirical success, not much is known about the
correctness and efficiency of belief propagation.
  Bethe approximation is an optimization-based framework for approximating
partition functions. While it is known that the stationary points of the Bethe
approximation coincide with the fixed points of belief propagation, in general,
the relation between the Bethe approximation and the partition function is not
well understood. It has been observed that for a few classes of factor graphs,
the Bethe approximation always gives a lower bound to the partition function,
which distinguishes them from the general case, where neither a lower bound,
nor an upper bound holds universally. This has been rigorously proved for
permanents and for attractive graphical models.
  Here we consider bipartite normal factor graphs and show that if the local
constraints satisfy a certain analytic property, the Bethe approximation is a
lower bound to the partition function. We arrive at this result by viewing
factor graphs through the lens of polynomials. In this process, we reformulate
the Bethe approximation as a polynomial optimization problem. Our sufficient
condition for the lower bound property to hold is inspired by recent
developments in the theory of real stable polynomials. We believe that this way
of viewing factor graphs and its connection to real stability might lead to a
better understanding of belief propagation and factor graphs in general.
Soft-NMS -- Improving Object Detection With One Line of Code
Authors: Navaneeth Bodla, Bharat Singh, Rama Chellappa, Larry S. Davis
Date: 2017-04-14 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, object-detection, Object Detection

Non-maximum suppression is an integral part of the object detection pipeline.
First, it sorts all detection boxes on the basis of their scores. The detection
box M with the maximum score is selected and all other detection boxes with a
significant overlap (using a pre-defined threshold) with M are suppressed. This
process is recursively applied on the remaining boxes. As per the design of the
algorithm, if an object lies within the predefined overlap threshold, it leads
to a miss. To this end, we propose Soft-NMS, an algorithm which decays the
detection scores of all other objects as a continuous function of their overlap
with M. Hence, no object is eliminated in this process. Soft-NMS obtains
consistent improvements for the coco-style mAP metric on standard datasets like
PASCAL VOC 2007 (1.7% for both R-FCN and Faster-RCNN) and MS-COCO (1.3% for
R-FCN and 1.1% for Faster-RCNN) by just changing the NMS algorithm without any
additional hyper-parameters. Using Deformable-RFCN, Soft-NMS improves
state-of-the-art in object detection from 39.8% to 40.9% with a single model.
Further, the computational complexity of Soft-NMS is the same as traditional
NMS and hence it can be efficiently implemented. Since Soft-NMS does not
require any extra training and is simple to implement, it can be easily
integrated into any object detection pipeline. Code for Soft-NMS is publicly
available on GitHub (http://bit.ly/2nJLNMu).
Multi-Task Video Captioning with Video and Entailment Generation
Authors: Ramakanth Pasunuru, Mohit Bansal
Date: 2017-04-24 00:00:00
Proceeding: ACL 2017 7
Tasks: Decoder, Multi-Task Learning, Video Captioning, Video Prediction

Video captioning, the task of describing the content of a video, has seen
some promising improvements in recent years with sequence-to-sequence models,
but accurately learning the temporal and logical dynamics involved in the task
still remains a challenge, especially given the lack of sufficient annotated
data. We improve video captioning by sharing knowledge with two related
directed-generation tasks: a temporally-directed unsupervised video prediction
task to learn richer context-aware video encoder representations, and a
logically-directed language entailment generation task to learn better
video-entailed caption decoder representations. For this, we present a
many-to-many multi-task learning model that shares parameters across the
encoders and decoders of the three tasks. We achieve significant improvements
and the new state-of-the-art on several standard video captioning datasets
using diverse automatic and human evaluations. We also show mutual multi-task
improvements on the entailment generation task.
Neural-based Context Representation Learning for Dialog Act Classification
Authors: Daniel Ortega, Ngoc Thang Vu
Date: 2017-08-08 00:00:00
Proceeding: WS 2017 8
Tasks: Classification, Dialog Act Classification, General Classification, Representation Learning

We explore context representation learning methods in neural-based models for
dialog act classification. We propose and compare extensively different methods
which combine recurrent neural network architectures and attention mechanisms
(AMs) at different context levels. Our experimental results on two benchmark
datasets show consistent improvements compared to the models without contextual
information and reveal that the most suitable AM in the architecture depends on
the nature of the dataset.
Semantic Instance Segmentation with a Discriminative Loss Function
Authors: Bert De Brabandere, Davy Neven, Luc van Gool
Date: 2017-08-08 00:00:00
Tasks: Instance Segmentation, Lane Detection, Metric Learning, Multi-Human Parsing, Segmentation, Semantic Segmentation

Semantic instance segmentation remains a challenging task. In this work we
propose to tackle the problem with a discriminative loss function, operating at
the pixel level, that encourages a convolutional network to produce a
representation of the image that can easily be clustered into instances with a
simple post-processing step. The loss function encourages the network to map
each pixel to a point in feature space so that pixels belonging to the same
instance lie close together while different instances are separated by a wide
margin. Our approach of combining an off-the-shelf network with a principled
loss function inspired by a metric learning objective is conceptually simple
and distinct from recent efforts in instance segmentation. In contrast to
previous works, our method does not rely on object proposals or recurrent
mechanisms. A key contribution of our work is to demonstrate that such a simple
setup without bells and whistles is effective and can perform on par with more
complex methods. Moreover, we show that it does not suffer from some of the
limitations of the popular detect-and-segment approaches. We achieve
competitive performance on the Cityscapes and CVPPP leaf segmentation
benchmarks.
Fast Scene Understanding for Autonomous Driving
Authors: Davy Neven, Bert de Brabandere, Stamatios Georgoulis, Marc Proesmans, Luc van Gool
Date: 2017-08-08 00:00:00
Tasks: Autonomous Driving, Decoder, Depth Estimation, Instance Segmentation, Monocular Depth Estimation, Scene Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

Most approaches for instance-aware semantic labeling traditionally focus on
accuracy. Other aspects like runtime and memory footprint are arguably as
important for real-time applications such as autonomous driving. Motivated by
this observation and inspired by recent works that tackle multiple tasks with a
single integrated architecture, in this paper we present a real-time efficient
implementation based on ENet that solves three autonomous driving related tasks
at once: semantic scene segmentation, instance segmentation and monocular depth
estimation. Our approach builds upon a branched ENet architecture with a shared
encoder but different decoder branches for each of the three tasks. The
presented method can run at 21 fps at a resolution of 1024x512 on the
Cityscapes dataset without sacrificing accuracy compared to running each task
separately.
Demixing Structured Superposition Signals from Periodic and Aperiodic Nonlinear Observations
Authors: Mohammadreza Soltani, Chinmay Hegde
Date: 2017-08-08 00:00:00

We consider the demixing problem of two (or more) structured high-dimensional
vectors from a limited number of nonlinear observations where this nonlinearity
is due to either a periodic or an aperiodic function. We study certain families
of structured superposition models, and propose a method which provably
recovers the components given (nearly) $m = \mathcal{O}(s)$ samples where $s$
denotes the sparsity level of the underlying components. This strictly improves
upon previous nonlinear demixing techniques and asymptotically matches the best
possible sample complexity. We also provide a range of simulations to
illustrate the performance of the proposed algorithms.
A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence
Authors: Sudeepa Roy, Babak Salimi
Date: 2017-08-08 00:00:00
Tasks: Causal Inference

The study of causality or causal inference - how much a given treatment
causally affects a given outcome in a population - goes way beyond correlation
or association analysis of variables, and is critical in making sound data
driven decisions and policies in a multitude of applications. The gold standard
in causal inference is performing "controlled experiments", which often is not
possible due to logistical or ethical reasons. As an alternative, inferring
causality on "observational data" based on the "Neyman-Rubin potential outcome
model" has been extensively used in statistics, economics, and social sciences
over several decades. In this paper, we present a formal framework for sound
causal analysis on observational datasets that are given as multiple relations
and where the population under study is obtained by joining these base
relations. We study a crucial condition for inferring causality from
observational data, called the "strong ignorability assumption" (the treatment
and outcome variables should be independent in the joined relation given the
observed covariates), using known conditional independences that hold in the
base relations. We also discuss how the structure of the conditional
independences in base relations given as graphical models help infer new
conditional independences in the joined relation. The proposed framework
combines concepts from databases, statistics, and graphical models, and aims to
initiate new research directions spanning these fields to facilitate powerful
data-driven decisions in today's big data world.
Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual Cross Retrieval
Authors: Yuming Shen, Li Liu, Ling Shao, Jingkuan Song
Date: 2017-08-08 00:00:00
Proceeding: ICCV 2017 10
Tasks: Cross-Modal Retrieval, Descriptive, Retrieval

Cross-modal hashing is usually regarded as an effective technique for
large-scale textual-visual cross retrieval, where data from different
modalities are mapped into a shared Hamming space for matching. Most of the
traditional textual-visual binary encoding methods only consider holistic image
representations and fail to model descriptive sentences. This renders existing
methods inappropriate to handle the rich semantics of informative cross-modal
data for quality textual-visual search tasks. To address the problem of hashing
cross-modal data with semantic-rich cues, in this paper, a novel integrated
deep architecture is developed to effectively encode the detailed semantics of
informative images and long descriptive sentences, named as Textual-Visual Deep
Binaries (TVDB). In particular, region-based convolutional networks with long
short-term memory units are introduced to fully explore image regional details
while semantic cues of sentences are modeled by a text convolutional network.
Additionally, we propose a stochastic batch-wise training routine, where
high-quality binary codes and deep encoding functions are efficiently optimized
in an alternating manner. Experiments are conducted on three multimedia
datasets, i.e. Microsoft COCO, IAPR TC-12, and INRIA Web Queries, where the
proposed TVDB model significantly outperforms state-of-the-art binary coding
methods in the task of cross-modal retrieval.
Learning Joint Multilingual Sentence Representations with Neural Machine Translation
Authors: Holger Schwenk, Matthijs Douze
Date: 2017-04-13 00:00:00
Proceeding: WS 2017 8
Tasks: Joint Multilingual Sentence Representations, Machine Translation, Sentence, Translation

In this paper, we use the framework of neural machine translation to learn
joint sentence representations across six very different languages. Our aim is
that a representation which is independent of the language, is likely to
capture the underlying semantics. We define a new cross-lingual similarity
measure, compare up to 1.4M sentence representations and study the
characteristics of close sentences. We provide experimental evidence that
sentences that are close in embedding space are indeed semantically highly
related, but often have quite different structure and syntax. These relations
also hold when comparing sentences in different languages.