pwc-archive/papers-with-abstracts huggingface dataset:
On The Robustness of a Neural Network
Authors: El Mahdi El Mhamdi, Rachid Guerraoui, Sebastien Rouault
Date: 2017-07-25 00:00:00

With the development of neural networks based machine learning and their
usage in mission critical applications, voices are rising against the
\textit{black box} aspect of neural networks as it becomes crucial to
understand their limits and capabilities. With the rise of neuromorphic
hardware, it is even more critical to understand how a neural network, as a
distributed system, tolerates the failures of its computing nodes, neurons, and
its communication channels, synapses. Experimentally assessing the robustness
of neural networks involves the quixotic venture of testing all the possible
failures, on all the possible inputs, which ultimately hits a combinatorial
explosion for the first, and the impossibility to gather all the possible
inputs for the second.
  In this paper, we prove an upper bound on the expected error of the output
when a subset of neurons crashes. This bound involves dependencies on the
network parameters that can be seen as being too pessimistic in the average
case. It involves a polynomial dependency on the Lipschitz coefficient of the
neurons activation function, and an exponential dependency on the depth of the
layer where a failure occurs. We back up our theoretical results with
experiments illustrating the extent to which our prediction matches the
dependencies between the network parameters and robustness. Our results show
that the robustness of neural networks to the average crash can be estimated
without the need to neither test the network on all failure configurations, nor
access the training set used to train the network, both of which are
practically impossible requirements.
Regularizing and Optimizing LSTM Language Models
Authors: Stephen Merity, Nitish Shirish Keskar, Richard Socher
Date: 2017-08-07 00:00:00
Proceeding: ICLR 2018 1
Tasks: Image Classification, Language Modeling, Language Modelling, Translation

Recurrent neural networks (RNNs), such as long short-term memory networks
(LSTMs), serve as a fundamental building block for many sequence learning
tasks, including machine translation, language modeling, and question
answering. In this paper, we consider the specific problem of word-level
language modeling and investigate strategies for regularizing and optimizing
LSTM-based models. We propose the weight-dropped LSTM which uses DropConnect on
hidden-to-hidden weights as a form of recurrent regularization. Further, we
introduce NT-ASGD, a variant of the averaged stochastic gradient method,
wherein the averaging trigger is determined using a non-monotonic condition as
opposed to being tuned by the user. Using these and other regularization
strategies, we achieve state-of-the-art word level perplexities on two data
sets: 57.3 on Penn Treebank and 65.8 on WikiText-2. In exploring the
effectiveness of a neural cache in conjunction with our proposed model, we
achieve an even lower state-of-the-art perplexity of 52.8 on Penn Treebank and
52.0 on WikiText-2.
Real Time Analytics: Algorithms and Systems
Authors: Arun Kejariwal, Sanjeev Kulkarni, Karthik Ramasamy
Date: 2017-08-07 00:00:00

Velocity is one of the 4 Vs commonly used to characterize Big Data. In this
regard, Forrester remarked the following in Q3 2014: "The high velocity,
white-water flow of data from innumerable real-time data sources such as market
data, Internet of Things, mobile, sensors, click-stream, and even transactions
remain largely unnavigated by most firms. The opportunity to leverage streaming
analytics has never been greater." Example use cases of streaming analytics
include, but not limited to: (a) visualization of business metrics in real-time
(b) facilitating highly personalized experiences (c) expediting response during
emergencies. Streaming analytics is extensively used in a wide variety of
domains such as healthcare, e-commerce, financial services, telecommunications,
energy and utilities, manufacturing, government and transportation.
  In this tutorial, we shall present an in-depth overview of streaming
analytics - applications, algorithms and platforms - landscape. We shall walk
through how the field has evolved over the last decade and then discuss the
current challenges - the impact of the other three Vs, viz., Volume, Variety
and Veracity, on Big Data streaming analytics. The tutorial is intended for
both researchers and practitioners in the industry. We shall also present
state-of-the-affairs of streaming analytics at Twitter.
Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos
Authors: Ömer Sümer, Tobias Dencker, Björn Ommer
Date: 2017-08-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: Pose Estimation, Retrieval, Self-Supervised Learning

Human pose analysis is presently dominated by deep convolutional networks
trained with extensive manual annotations of joint locations and beyond. To
avoid the need for expensive labeling, we exploit spatiotemporal relations in
training videos for self-supervised learning of pose embeddings. The key idea
is to combine temporal ordering and spatial placement estimation as auxiliary
tasks for learning pose similarities in a Siamese convolutional network. Since
the self-supervised sampling of both tasks from natural videos can result in
ambiguous and incorrect training labels, our method employs a curriculum
learning idea that starts training with the most reliable data samples and
gradually increases the difficulty. To further refine the training process we
mine repetitive poses in individual videos which provide reliable labels while
removing inconsistencies. Our pose embeddings capture visual characteristics of
human pose that can boost existing supervised representations in human pose
estimation and retrieval. We report quantitative and qualitative results on
these tasks in Olympic Sports, Leeds Pose Sports and MPII Human Pose datasets.
Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources
Authors: Adrian Bulat, Georgios Tzimiropoulos
Date: 2017-03-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: Binarization, Face Alignment, Pose Estimation

Our goal is to design architectures that retain the groundbreaking
performance of CNNs for landmark localization and at the same time are
lightweight, compact and suitable for applications with limited computational
resources. To this end, we make the following contributions: (a) we are the
first to study the effect of neural network binarization on localization tasks,
namely human pose estimation and face alignment. We exhaustively evaluate
various design choices, identify performance bottlenecks, and more importantly
propose multiple orthogonal ways to boost performance. (b) Based on our
analysis, we propose a novel hierarchical, parallel and multi-scale residual
architecture that yields large performance improvement over the standard
bottleneck block while having the same number of parameters, thus bridging the
gap between the original network and its binarized counterpart. (c) We perform
a large number of ablation studies that shed light on the properties and the
performance of the proposed block. (d) We present results for experiments on
the most challenging datasets for human pose estimation and face alignment,
reporting in many cases state-of-the-art performance. Code can be downloaded
from https://www.adrianbulat.com/binary-cnn-landmarks
Regulating Highly Automated Robot Ecologies: Insights from Three User Studies
Authors: Wen Shen, Alanoud Al Khemeiri, Abdulla Almehrezi, Wael Al Enezi, Iyad Rahwan, Jacob W. Crandall
Date: 2017-08-07 00:00:00

Highly automated robot ecologies (HARE), or societies of independent
autonomous robots or agents, are rapidly becoming an important part of much of
the world's critical infrastructure. As with human societies, regulation,
wherein a governing body designs rules and processes for the society, plays an
important role in ensuring that HARE meet societal objectives. However, to
date, a careful study of interactions between a regulator and HARE is lacking.
In this paper, we report on three user studies which give insights into how to
design systems that allow people, acting as the regulatory authority, to
effectively interact with HARE. As in the study of political systems in which
governments regulate human societies, our studies analyze how interactions
between HARE and regulators are impacted by regulatory power and individual
(robot or agent) autonomy. Our results show that regulator power, decision
support, and adaptive autonomy can each diminish the social welfare of HARE,
and hint at how these seemingly desirable mechanisms can be designed so that
they become part of successful HARE.
Learning to segment on tiny datasets: a new shape model
Authors: Maxime Tremblay, André Zaccarin
Date: 2017-08-07 00:00:00
Tasks: Object, object-detection, Object Detection, Segmentation, Semantic Segmentation

Current object segmentation algorithms are based on the hypothesis that one
has access to a very large amount of data. In this paper, we aim to segment
objects using only tiny datasets. To this extent, we propose a new automatic
part-based object segmentation algorithm for non-deformable and semi-deformable
objects in natural backgrounds. We have developed a novel shape descriptor
which models the local boundaries of an object's part. This shape descriptor is
used in a bag-of-words approach for object detection. Once the detection
process is performed, we use the background and foreground likelihood given by
our trained shape model, and the information from the image content, to define
a dense CRF model. We use a mean field approximation to solve it and thus
segment the object of interest. Performance evaluated on different datasets
shows that our approach can sometimes achieve results near state-of-the-art
techniques based on big data while requiring only a tiny training set.
Improved Strongly Adaptive Online Learning using Coin Betting
Authors: Kwang-Sung Jun, Francesco Orabona, Rebecca Willett, Stephen Wright
Date: 2016-10-14 00:00:00
Tasks: Metric Learning

This paper describes a new parameter-free online learning algorithm for
changing environments. In comparing against algorithms with the same time
complexity as ours, we obtain a strongly adaptive regret bound that is a factor
of at least $\sqrt{\log(T)}$ better, where $T$ is the time horizon. Empirical
results show that our algorithm outperforms state-of-the-art methods in
learning with expert advice and metric learning scenarios.
Axiomatic Characterization of Data-Driven Influence Measures for Classification
Authors: Jakub Sliwinski, Martin Strobel, Yair Zick
Date: 2017-08-07 00:00:00
Tasks: Classification, General Classification

We study the following problem: given a labeled dataset and a specific
datapoint x, how did the i-th feature influence the classification for x? We
identify a family of numerical influence measures - functions that, given a
datapoint x, assign a numeric value phi_i(x) to every feature i, corresponding
to how altering i's value would influence the outcome for x. This family, which
we term monotone influence measures (MIM), is uniquely derived from a set of
desirable properties, or axioms. The MIM family constitutes a provably sound
methodology for measuring feature influence in classification domains; the
values generated by MIM are based on the dataset alone, and do not make any
queries to the classifier. While this requirement naturally limits the scope of
our framework, we demonstrate its effectiveness on data.
STARDATA: A StarCraft AI Research Dataset
Authors: Zeming Lin, Jonas Gehring, Vasil Khalidov, Gabriel Synnaeve
Date: 2017-08-07 00:00:00
Tasks: Diversity, Imitation Learning, Real-Time Strategy Games, Reinforcement Learning, Starcraft, valid

We release a dataset of 65646 StarCraft replays that contains 1535 million
frames and 496 million player actions. We provide full game state data along
with the original replays that can be viewed in StarCraft. The game state data
was recorded every 3 frames which ensures suitability for a wide variety of
machine learning tasks such as strategy classification, inverse reinforcement
learning, imitation learning, forward modeling, partial information extraction,
and others. We use TorchCraft to extract and store the data, which standardizes
the data format for both reading from replays and reading directly from the
game. Furthermore, the data can be used on different operating systems and
platforms. The dataset contains valid, non-corrupted replays only and its
quality and diversity was ensured by a number of heuristics. We illustrate the
diversity of the data with various statistics and provide examples of tasks
that benefit from the dataset. We make the dataset available at
https://github.com/TorchCraft/StarData . En Taro Adun!
Weakly Supervised Object Localization Using Things and Stuff Transfer
Authors: Miaojing Shi, Holger Caesar, Vittorio Ferrari
Date: 2017-03-23 00:00:00
Proceeding: ICCV 2017 10
Tasks: Multiple Instance Learning, Object, Object Localization, Segmentation, Weakly-Supervised Object Localization

We propose to help weakly supervised object localization for classes where
location annotations are not available, by transferring things and stuff
knowledge from a source set with available annotations. The source and target
classes might share similar appearance (e.g. bear fur is similar to cat fur) or
appear against similar background (e.g. horse and sheep appear against grass).
To exploit this, we acquire three types of knowledge from the source set: a
segmentation model trained on both thing and stuff classes; similarity
relations between target and source classes; and co-occurrence relations
between thing and stuff classes in the source. The segmentation model is used
to generate thing and stuff segmentation maps on a target image, while the
class similarity and co-occurrence knowledge help refining them. We then
incorporate these maps as new cues into a multiple instance learning framework
(MIL), propagating the transferred knowledge from the pixel level to the object
proposal level. In extensive experiments, we conduct our transfer from the
PASCAL Context dataset (source) to the ILSVRC, COCO and PASCAL VOC 2007
datasets (targets). We evaluate our transfer across widely different thing
classes, including some that are not similar in appearance, but appear against
similar background. The results demonstrate significant improvement over
standard MIL, and we outperform the state-of-the-art in the transfer setting.
Multimodal Classification for Analysing Social Media
Authors: Chi Thang Duong, Remi Lebret, Karl Aberer
Date: 2017-08-07 00:00:00
Tasks: Auxiliary Learning, Classification, Emotion Classification, General Classification

Classification of social media data is an important approach in understanding
user behavior on the Web. Although information on social media can be of
different modalities such as texts, images, audio or videos, traditional
approaches in classification usually leverage only one prominent modality.
Techniques that are able to leverage multiple modalities are often complex and
susceptible to the absence of some modalities. In this paper, we present simple
models that combine information from different modalities to classify social
media content and are able to handle the above problems with existing
techniques. Our models combine information from different modalities using a
pooling layer and an auxiliary learning task is used to learn a common feature
space. We demonstrate the performance of our models and their robustness to the
missing of some modalities in the emotion classification domain. Our
approaches, although being simple, can not only achieve significantly higher
accuracies than traditional fusion approaches but also have comparable results
when only one modality is available.
Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
Authors: Raghavendra Selvan, Jens Petersen, Jesper H. Pedersen, Marleen de Bruijne
Date: 2017-08-07 00:00:00
Tasks: Medical Image Analysis, State Space Models

Segmenting tree structures is common in several image processing
applications. In medical image analysis, reliable segmentations of airways,
vessels, neurons and other tree structures can enable important clinical
applications. We present a framework for tracking tree structures comprising of
elongated branches using probabilistic state-space models and Bayesian
smoothing. Unlike most existing methods that proceed with sequential tracking
of branches, we present an exploratory method, that is less sensitive to local
anomalies in the data due to acquisition noise and/or interfering structures.
The evolution of individual branches is modelled using a process model and the
observed data is incorporated into the update step of the Bayesian smoother
using a measurement model that is based on a multi-scale blob detector.
Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother,
which provides Gaussian density estimates of branch states at each tracking
step. We select likely branch seed points automatically based on the response
of the blob detection and track from all such seed points using the RTS
smoother. We use covariance of the marginal posterior density estimated for
each branch to discriminate false positive and true positive branches. The
method is evaluated on 3D chest CT scans to track airways. We show that the
presented method results in additional branches compared to a baseline method
based on region growing on probability images.
Learning for Active 3D Mapping
Authors: Karel Zimmermann, Tomas Petricek, Vojtech Salansky, Tomas Svoboda
Date: 2017-08-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: POS

We propose an active 3D mapping method for depth sensors, which allow
individual control of depth-measuring rays, such as the newly emerging
solid-state lidars. The method simultaneously (i) learns to reconstruct a dense
3D occupancy map from sparse depth measurements, and (ii) optimizes the
reactive control of depth-measuring rays. To make the first step towards the
online control optimization, we propose a fast prioritized greedy algorithm,
which needs to update its cost function in only a small fraction of pos- sible
rays. The approximation ratio of the greedy algorithm is derived. An
experimental evaluation on the subset of the KITTI dataset demonstrates
significant improve- ment in the 3D map accuracy when learning-to-reconstruct
from sparse measurements is coupled with the optimization of depth-measuring
rays.
Structured Attentions for Visual Question Answering
Authors: Chen Zhu, Yanpeng Zhao, Shuaiyi Huang, Kewei Tu, Yi Ma
Date: 2017-08-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: Visual Question Answering, Visual Question Answering (VQA)

Visual attention, which assigns weights to image regions according to their
relevance to a question, is considered as an indispensable part by most Visual
Question Answering models. Although the questions may involve complex relations
among multiple regions, few attention models can effectively encode such
cross-region relations. In this paper, we demonstrate the importance of
encoding such relations by showing the limited effective receptive field of
ResNet on two datasets, and propose to model the visual attention as a
multivariate distribution over a grid-structured Conditional Random Field on
image regions. We demonstrate how to convert the iterative inference
algorithms, Mean Field and Loopy Belief Propagation, as recurrent layers of an
end-to-end neural network. We empirically evaluated our model on 3 datasets, in
which it surpasses the best baseline model of the newly released CLEVR dataset
by 9.5%, and the best published model on the VQA dataset by 1.25%. Source code
is available at https: //github.com/zhuchen03/vqa-sva.
Efficient Noisy Optimisation with the Sliding Window Compact Genetic Algorithm
Authors: Simon M. Lucas, Jialin Liu, Diego Pérez-Liébana
Date: 2017-08-07 00:00:00

The compact genetic algorithm is an Estimation of Distribution Algorithm for
binary optimisation problems. Unlike the standard Genetic Algorithm, no
cross-over or mutation is involved. Instead, the compact Genetic Algorithm uses
a virtual population represented as a probability distribution over the set of
binary strings. At each optimisation iteration, exactly two individuals are
generated by sampling from the distribution, and compared exactly once to
determine a winner and a loser. The probability distribution is then adjusted
to increase the likelihood of generating individuals similar to the winner.
  This paper introduces two straightforward variations of the compact Genetic
Algorithm, each of which lead to a significant improvement in performance. The
main idea is to make better use of each fitness evaluation, by ensuring that
each evaluated individual is used in multiple win/loss comparisons. The first
variation is to sample $n>2$ individuals at each iteration to make $n(n-1)/2$
comparisons. The second variation only samples one individual at each iteration
but keeps a sliding history window of previous individuals to compare with. We
evaluate methods on two noisy test problems and show that in each case they
significantly outperform the compact Genetic Algorithm, while maintaining the
simplicity of the algorithm.
Superpixel-based Semantic Segmentation Trained by Statistical Process Control
Authors: Hyojin Park, Jisoo Jeong, Youngjoon Yoo, Nojun Kwak
Date: 2017-06-30 00:00:00
Tasks: Semantic Segmentation

Semantic segmentation, like other fields of computer vision, has seen a
remarkable performance advance by the use of deep convolution neural networks.
However, considering that neighboring pixels are heavily dependent on each
other, both learning and testing of these methods have a lot of redundant
operations. To resolve this problem, the proposed network is trained and tested
with only 0.37% of total pixels by superpixel-based sampling and largely
reduced the complexity of upsampling calculation. The hypercolumn feature maps
are constructed by pyramid module in combination with the convolution layers of
the base network. Since the proposed method uses a very small number of sampled
pixels, the end-to-end learning of the entire network is difficult with a
common learning rate for all the layers. In order to resolve this problem, the
learning rate after sampling is controlled by statistical process control (SPC)
of gradients in each layer. The proposed method performs better than or equal
to the conventional methods that use much more samples on Pascal Context,
SUN-RGBD dataset.
Nonconvex Sparse Logistic Regression with Weakly Convex Regularization
Authors: Xinyue Shen, Yuantao Gu
Date: 2017-08-07 00:00:00
Tasks: regression

In this work we propose to fit a sparse logistic regression model by a weakly
convex regularized nonconvex optimization problem. The idea is based on the
finding that a weakly convex function as an approximation of the $\ell_0$
pseudo norm is able to better induce sparsity than the commonly used $\ell_1$
norm. For a class of weakly convex sparsity inducing functions, we prove the
nonconvexity of the corresponding sparse logistic regression problem, and study
its local optimality conditions and the choice of the regularization parameter
to exclude trivial solutions. Despite the nonconvexity, a method based on
proximal gradient descent is used to solve the general weakly convex sparse
logistic regression, and its convergence behavior is studied theoretically.
Then the general framework is applied to a specific weakly convex function, and
a necessary and sufficient local optimality condition is provided. The solution
method is instantiated in this case as an iterative firm-shrinkage algorithm,
and its effectiveness is demonstrated in numerical experiments by both randomly
generated and real datasets.