pwc-archive/papers-with-abstracts huggingface dataset:
Experimental Design for Cost-Aware Learning of Causal Graphs
Authors: Erik M. Lindgren, Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath
Date: 2018-10-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Experimental Design

We consider the minimum cost intervention design problem: Given the essential
graph of a causal graph and a cost to intervene on a variable, identify the set
of interventions with minimum total cost that can learn any causal graph with
the given essential graph. We first show that this problem is NP-hard. We then
prove that we can achieve a constant factor approximation to this problem with
a greedy algorithm. We then constrain the sparsity of each intervention. We
develop an algorithm that returns an intervention design that is nearly optimal
in terms of size for sparse graphs with sparse interventions and we discuss how
to use it when there are costs on the vertices.
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction
Authors: William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson
Date: 2018-10-28 00:00:00
Tasks: counterfactual, Prediction

Identifying changes in model parameters is fundamental in machine learning
and statistics. However, standard changepoint models are limited in
expressiveness, often addressing unidimensional problems and assuming
instantaneous changes. We introduce change surfaces as a multidimensional and
highly expressive generalization of changepoints. We provide a model-agnostic
formalization of change surfaces, illustrating how they can provide variable,
heterogeneous, and non-monotonic rates of change across multiple dimensions.
Additionally, we show how change surfaces can be used for counterfactual
prediction. As a concrete instantiation of the change surface framework, we
develop Gaussian Process Change Surfaces (GPCS). We demonstrate counterfactual
prediction with Bayesian posterior mean and credible sets, as well as massive
scalability by introducing novel methods for additive non-separable kernels.
Using two large spatio-temporal datasets we employ GPCS to discover and
characterize complex changes that can provide scientific and policy relevant
insights. Specifically, we analyze twentieth century measles incidence across
the United States and discover previously unknown heterogeneous changes after
the introduction of the measles vaccine. Additionally, we apply the model to
requests for lead testing kits in New York City, discovering distinct spatial
and demographic patterns.
Exploring $k$ out of Top $ρ$ Fraction of Arms in Stochastic Bandits
Authors: Wenbo Ren, Jia Liu, Ness Shroff
Date: 2018-10-28 00:00:00

This paper studies the problem of identifying any $k$ distinct arms among the top $\rho$ fraction (e.g., top 5\%) of arms from a finite or infinite set with a probably approximately correct (PAC) tolerance $\epsilon$. We consider two cases: (i) when the threshold of the top arms' expected rewards is known and (ii) when it is unknown. We prove lower bounds for the four variants (finite or infinite arms, and known or unknown threshold), and propose algorithms for each. Two of these algorithms are shown to be sample complexity optimal (up to constant factors) and the other two are optimal up to a log factor. Results in this paper provide up to $\rho n/k$ reductions compared with the "$k$-exploration" algorithms that focus on finding the (PAC) best $k$ arms out of $n$ arms. We also numerically show improvements over the state-of-the-art.
Scale Estimation of Monocular SfM for a Multi-modal Stereo Camera
Authors: Shinya Sumikura, Ken Sakurada, Nobuo Kawaguchi, Ryosuke Nakamura
Date: 2018-10-28 00:00:00

This paper proposes a novel method of estimating the absolute scale of
monocular SfM for a multi-modal stereo camera. In the fields of computer vision
and robotics, scale estimation for monocular SfM has been widely investigated
in order to simplify systems. This paper addresses the scale estimation problem
for a stereo camera system in which two cameras capture different spectral
images (e.g., RGB and FIR), whose feature points are difficult to directly
match using descriptors. Furthermore, the number of matching points between FIR
images can be comparatively small, owing to the low resolution and lack of
thermal scene texture. To cope with these difficulties, the proposed method
estimates the scale parameter using batch optimization, based on the epipolar
constraint of a small number of feature correspondences between the invisible
light images. The accuracy and numerical stability of the proposed method are
verified by synthetic and real image experiments.
LPCNet: Improving Neural Speech Synthesis Through Linear Prediction
Authors: Jean-Marc Valin, Jan Skoglund
Date: 2018-10-28 00:00:00
Tasks: Prediction, Speech Synthesis, text-to-speech, Text to Speech

Neural speech synthesis models have recently demonstrated the ability to
synthesize high quality speech for text-to-speech and compression applications.
These new models often require powerful GPUs to achieve real-time operation, so
being able to reduce their complexity would open the way for many new
applications. We propose LPCNet, a WaveRNN variant that combines linear
prediction with recurrent neural networks to significantly improve the
efficiency of speech synthesis. We demonstrate that LPCNet can achieve
significantly higher quality than WaveRNN for the same network size and that
high quality LPCNet speech synthesis is achievable with a complexity under 3
GFLOPS. This makes it easier to deploy neural synthesis applications on
lower-power devices, such as embedded systems and mobile phones.
MaxHedge: Maximising a Maximum Online
Authors: Stephen Pasteris, Fabio Vitale, Kevin Chan, Shiqiang Wang, Mark Herbster
Date: 2018-10-28 00:00:00

We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set. Each action is associated with an energy value, a reward and a cost. The sum of the energies of the actions selected cannot exceed a given energy budget. The goal is to maximise the cumulative profit, where the profit obtained on a single trial is defined as the difference between the maximum reward among the selected actions and the sum of their costs. Action energy values and the budget are known and fixed. All rewards and costs associated with each action change over time and are revealed at each trial only after the learner's selection of actions. Our framework encompasses several online learning problems where the environment changes over time; and the solution trades-off between minimising the costs and maximising the maximum reward of the selected subset of actions, while being constrained to an action energy budget. The algorithm that we propose is efficient and general in that it may be specialised to multiple natural online combinatorial problems.
Enhanced CNN for image denoising
Authors: Chunwei Tian, Yong Xu, Lunke Fei, Junqian Wang, Jie Wen, Nan Luo
Date: 2018-10-28 00:00:00
Tasks: Denoising, Image Denoising

Owing to flexible architectures of deep convolutional neural networks (CNNs),
CNNs are successfully used for image denoising. However, they suffer from the
following drawbacks: (i) deep network architecture is very difficult to train.
(ii) Deeper networks face the challenge of performance saturation. In this
study, the authors propose a novel method called enhanced convolutional neural
denoising network (ECNDNet). Specifically, they use residual learning and batch
normalisation techniques to address the problem of training difficulties and
accelerate the convergence of the network. In addition, dilated convolutions
are used in the proposed network to enlarge the context information and reduce
the computational cost. Extensive experiments demonstrate that the ECNDNet
outperforms the state-of-the-art methods for image denoising.
VDMS: Efficient Big-Visual-Data Access for Machine Learning Workloads
Authors: Luis Remis, Vishakha Gupta-Cledat, Christina Strong, Ragaad Altarawneh
Date: 2018-10-28 00:00:00
Tasks: BIG-bench Machine Learning, Management

We introduce the Visual Data Management System (VDMS), which enables faster
access to big-visual-data and adds support to visual analytics. This is
achieved by searching for relevant visual data via metadata stored as a graph,
and enabling faster access to visual data through new machine-friendly storage
formats. VDMS differs from existing large scale photo serving, video streaming,
and textual big-data management systems due to its primary focus on supporting
machine learning and data analytics pipelines that use visual data (images,
videos, and feature vectors), treating these as first class entities. We
describe how to use VDMS via its user friendly interface and how it enables
rich and efficient vision analytics through a machine learning pipeline for
processing medical images. We show the improved performance of 2x in complex
queries over a comparable set-up.
On preserving non-discrimination when combining expert advice
Authors: Avrim Blum, Suriya Gunasekar, Thodoris Lykouris, Nathan Srebro
Date: 2018-10-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decision Making, Sequential Decision Making

We study the interplay between sequential decision making and avoiding
discrimination against protected groups, when examples arrive online and do not
follow distributional assumptions. We consider the most basic extension of
classical online learning: "Given a class of predictors that are individually
non-discriminatory with respect to a particular metric, how can we combine them
to perform as well as the best predictor, while preserving non-discrimination?"
Surprisingly we show that this task is unachievable for the prevalent notion of
"equalized odds" that requires equal false negative rates and equal false
positive rates across groups. On the positive side, for another notion of
non-discrimination, "equalized error rates", we show that running separate
instances of the classical multiplicative weights algorithm for each group
achieves this guarantee. Interestingly, even for this notion, we show that
algorithms with stronger performance guarantees than multiplicative weights
cannot preserve non-discrimination.
Multi-Spectral Imaging via Computed Tomography (MUSIC) - Comparing Unsupervised Spectral Segmentations for Material Differentiation
Authors: Christian Kehl, Wail Mustafa, Jan Kehres, Anders Bjorholm Dahl, Ulrik Lund Olsen
Date: 2018-10-28 00:00:00
Tasks: Segmentation, Semantic Segmentation

Multi-spectral computed tomography is an emerging technology for the
non-destructive identification of object materials and the study of their
physical properties. Applications of this technology can be found in various
scientific and industrial contexts, such as luggage scanning at airports.
Material distinction and its identification is challenging, even with spectral
x-ray information, due to acquisition noise, tomographic reconstruction
artefacts and scanning setup application constraints. We present MUSIC - and
open access multi-spectral CT dataset in 2D and 3D - to promote further
research in the area of material identification. We demonstrate the value of
this dataset on the image analysis challenge of object segmentation purely
based on the spectral response of its composing materials. In this context, we
compare the segmentation accuracy of fast adaptive mean shift (FAMS) and
unconstrained graph cuts on both datasets. We further discuss the impact of
reconstruction artefacts and segmentation controls on the achievable results.
Dataset, related software packages and further documentation are made available
to the imaging community in an open-access manner to promote further
data-driven research on the subject
Object Tracking in Hyperspectral Videos with Convolutional Features and Kernelized Correlation Filter
Authors: Kun Qian, Jun Zhou, Fengchao Xiong, Huixin Zhou, Juan Du
Date: 2018-10-28 00:00:00
Tasks: Object Tracking

Target tracking in hyperspectral videos is a new research topic. In this
paper, a novel method based on convolutional network and Kernelized Correlation
Filter (KCF) framework is presented for tracking objects of interest in
hyperspectral videos. We extract a set of normalized three-dimensional cubes
from the target region as fixed convolution filters which contain spectral
information surrounding a target. The feature maps generated by convolutional
operations are combined to form a three-dimensional representation of an
object, thereby providing effective encoding of local spectral-spatial
information. We show that a simple two-layer convolutional networks is
sufficient to learn robust representations without the need of offline training
with a large dataset. In the tracking step, KCF is adopted to distinguish
targets from neighboring environment. Experimental results demonstrate that the
proposed method performs well on sample hyperspectral videos, and outperforms
several state-of-the-art methods tested on grayscale and color videos in the
same scene.
Discrimination-aware Channel Pruning for Deep Neural Networks
Authors: Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, Jinhui Zhu
Date: 2018-10-28 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: channel selection, Model Compression

Channel pruning is one of the predominant approaches for deep model
compression. Existing pruning methods either train from scratch with sparsity
constraints on channels, or minimize the reconstruction error between the
pre-trained feature maps and the compressed ones. Both strategies suffer from
some limitations: the former kind is computationally expensive and difficult to
converge, whilst the latter kind optimizes the reconstruction error but ignores
the discriminative power of channels. To overcome these drawbacks, we
investigate a simple-yet-effective method, called discrimination-aware channel
pruning, to choose those channels that really contribute to discriminative
power. To this end, we introduce additional losses into the network to increase
the discriminative power of intermediate layers and then select the most
discriminative channels for each layer by considering the additional loss and
the reconstruction error. Last, we propose a greedy algorithm to conduct
channel selection and parameter optimization in an iterative way. Extensive
experiments demonstrate the effectiveness of our method. For example, on
ILSVRC-12, our pruned ResNet-50 with 30% reduction of channels even outperforms
the original model by 0.39% in top-1 accuracy.
Robots Learning to Say `No': Prohibition and Rejective Mechanisms in Acquisition of Linguistic Negation
Authors: Frank Förster, Joe Saunders, Hagen Lehmann, Chrystopher L. Nehaniv
Date: 2018-10-28 00:00:00
Tasks: Gesture Generation, Language Acquisition, Negation

`No' belongs to the first ten words used by children and embodies the first
active form of linguistic negation. Despite its early occurrence the details of
its acquisition process remain largely unknown. The circumstance that `no'
cannot be construed as a label for perceptible objects or events puts it
outside of the scope of most modern accounts of language acquisition. Moreover,
most symbol grounding architectures will struggle to ground the word due to its
non-referential character. In an experimental study involving the child-like
humanoid robot iCub that was designed to illuminate the acquisition process of
negation words, the robot is deployed in several rounds of speech-wise
unconstrained interaction with na\"ive participants acting as its language
teachers. The results corroborate the hypothesis that affect or volition plays
a pivotal role in the socially distributed acquisition process. Negation words
are prosodically salient within prohibitive utterances and negative intent
interpretations such that they can be easily isolated from the teacher's speech
signal. These words subsequently may be grounded in negative affective states.
However, observations of the nature of prohibitive acts and the temporal
relationships between its linguistic and extra-linguistic components raise
serious questions over the suitability of Hebbian-type algorithms for language
grounding.
Cascaded Pyramid Mining Network for Weakly Supervised Temporal Action Localization
Authors: Haisheng Su, Xu Zhao, Tianwei Lin
Date: 2018-10-28 00:00:00
Tasks: Action Localization, General Classification, Temporal Action Localization, Video Classification, Weakly-supervised Temporal Action Localization

Weakly supervised temporal action localization, which aims at temporally
locating action instances in untrimmed videos using only video-level class
labels during training, is an important yet challenging problem in video
analysis. Many current methods adopt the "localization by classification"
framework: first do video classification, then locate temporal area
contributing to the results most. However, this framework fails to locate the
entire action instances and gives little consideration to the local context. In
this paper, we present a novel architecture called Cascaded Pyramid Mining
Network (CPMN) to address these issues using two effective modules. First, to
discover the entire temporal interval of specific action, we design a two-stage
cascaded module with proposed Online Adversarial Erasing (OAE) mechanism, where
new and complementary regions are mined through feeding the erased feature maps
of discovered regions back to the system. Second, to exploit hierarchical
contextual information in videos and reduce missing detections, we design a
pyramid module which produces a scale-invariant attention map through combining
the feature maps from different levels. Final, we aggregate the results of two
modules to perform action localization via locating high score areas in
temporal Class Activation Sequence (CAS). Extensive experiments conducted on
THUMOS14 and ActivityNet-1.3 datasets demonstrate the effectiveness of our
method.
Robust Audio Adversarial Example for a Physical Attack
Authors: Hiromu Yakura, Jun Sakuma
Date: 2018-10-28 00:00:00
Tasks: speech-recognition, Speech Recognition

We propose a method to generate audio adversarial examples that can attack a state-of-the-art speech recognition model in the physical world. Previous work assumes that generated adversarial examples are directly fed to the recognition model, and is not able to perform such a physical attack because of reverberation and noise from playback environments. In contrast, our method obtains robust adversarial examples by simulating transformations caused by playback or recording in the physical world and incorporating the transformations into the generation process. Evaluation and a listening experiment demonstrated that our adversarial examples are able to attack without being noticed by humans. This result suggests that audio adversarial examples generated by the proposed method may become a real threat.
A Hitchhiker's Guide On Distributed Training of Deep Neural Networks
Authors: Karanbir Chahal, Manraj Singh Grover, Kuntal Dey
Date: 2018-10-28 00:00:00
Tasks: Deep Learning, GPU, Playing the Game of 2048

Deep learning has led to tremendous advancements in the field of Artificial
Intelligence. One caveat however is the substantial amount of compute needed to
train these deep learning models. Training a benchmark dataset like ImageNet on
a single machine with a modern GPU can take upto a week, distributing training
on multiple machines has been observed to drastically bring this time down.
Recent work has brought down ImageNet training time to a time as low as 4
minutes by using a cluster of 2048 GPUs. This paper surveys the various
algorithms and techniques used to distribute training and presents the current
state of the art for a modern distributed training framework. More
specifically, we explore the synchronous and asynchronous variants of
distributed Stochastic Gradient Descent, various All Reduce gradient
aggregation strategies and best practices for obtaining higher throughout and
lower latency over a cluster such as mixed precision training, large batch
training and gradient compression.
RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications
Authors: Huan Zhang, Pengchuan Zhang, Cho-Jui Hsieh
Date: 2018-10-28 00:00:00

The Jacobian matrix (or the gradient for single-output networks) is directly
related to many important properties of neural networks, such as the function
landscape, stationary points, (local) Lipschitz constants and robustness to
adversarial attacks. In this paper, we propose a recursive algorithm, RecurJac,
to compute both upper and lower bounds for each element in the Jacobian matrix
of a neural network with respect to network's input, and the network can
contain a wide range of activation functions. As a byproduct, we can
efficiently obtain a (local) Lipschitz constant, which plays a crucial role in
neural network robustness verification, as well as the training stability of
GANs. Experiments show that (local) Lipschitz constants produced by our method
is of better quality than previous approaches, thus providing better robustness
verification results. Our algorithm has polynomial time complexity, and its
computation time is reasonable even for relatively large networks.
Additionally, we use our bounds of Jacobian matrix to characterize the
landscape of the neural network, for example, to determine whether there exist
stationary points in a local neighborhood. Source code available at
\url{http://github.com/huanzhang12/RecurJac-Jacobian-bounds}.
Deep Affinity Network for Multiple Object Tracking
Authors: Shi-Jie Sun, Naveed Akhtar, HuanSheng Song, Ajmal Mian, Mubarak Shah
Date: 2018-10-28 00:00:00
Tasks: Benchmarking, Multiple Object Tracking, Object, object-detection, Object Detection, Object Tracking

Multiple Object Tracking (MOT) plays an important role in solving many fundamental problems in video analysis in computer vision. Most MOT methods employ two steps: Object Detection and Data Association. The first step detects objects of interest in every frame of a video, and the second establishes correspondence between the detected objects in different frames to obtain their tracks. Object detection has made tremendous progress in the last few years due to deep learning. However, data association for tracking still relies on hand crafted constraints such as appearance, motion, spatial proximity, grouping etc. to compute affinities between the objects in different frames. In this paper, we harness the power of deep learning for data association in tracking by jointly modelling object appearances and their affinities between different frames in an end-to-end fashion. The proposed Deep Affinity Network (DAN) learns compact; yet comprehensive features of pre-detected objects at several levels of abstraction, and performs exhaustive pairing permutations of those features in any two frames to infer object affinities. DAN also accounts for multiple objects appearing and disappearing between video frames. We exploit the resulting efficient affinity computations to associate objects in the current frame deep into the previous frames for reliable on-line tracking. Our technique is evaluated on popular multiple object tracking challenges MOT15, MOT17 and UA-DETRAC. Comprehensive benchmarking under twelve evaluation metrics demonstrates that our approach is among the best performing techniques on the leader board for these challenges. The open source implementation of our work is available at https://github.com/shijieS/SST.git.