pwc-archive/papers-with-abstracts huggingface dataset:
Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models
Authors: Subhabrata Majumdar, George Michailidis
Date: 2018-03-09 00:00:00
Tasks: Data Integration

The rapid development of high-throughput technologies has enabled the generation of data from biological or disease processes that span multiple layers, like genomic, proteomic or metabolomic data, and further pertain to multiple sources, like disease subtypes or experimental conditions. In this work, we propose a general statistical framework based on Gaussian graphical models for horizontal (i.e. across conditions or subtypes) and vertical (i.e. across different layers containing data on molecular compartments) integration of information in such datasets. We start with decomposing the multi-layer problem into a series of two-layer problems. For each two-layer problem, we model the outcomes at a node in the lower layer as dependent on those of other nodes in that layer, as well as all nodes in the upper layer. We use a combination of neighborhood selection and group-penalized regression to obtain sparse estimates of all model parameters. Following this, we develop a debiasing technique and asymptotic distributions of inter-layer directed edge weights that utilize already computed neighborhood selection coefficients for nodes in the upper layer. Subsequently, we establish global and simultaneous testing procedures for these edge weights. Performance of the proposed methodology is evaluated on synthetic and real data.
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Authors: Tharindu Fernando, Simon Denman, Sridha Sridharan, Clinton Fookes
Date: 2018-03-09 00:00:00
Tasks: Generative Adversarial Network, Pedestrian Trajectory Prediction, Person Re-Identification, Trajectory Prediction

Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking. In this context we first introduce a light weight sequential
Generative Adversarial Network architecture for person localisation, which
overcomes issues related to occlusions and noisy detections, typically found in
a multi person environment. In the proposed tracking framework we build upon
recent advances in pedestrian trajectory prediction approaches and propose a
novel data association scheme based on predicted trajectories. This removes the
need for computationally expensive person re-identification systems based on
appearance features and generates human like trajectories with minimal
fragmentation. The proposed method is evaluated on multiple public benchmarks
including both static and dynamic cameras and is capable of generating
outstanding performance, especially among other recently proposed deep neural
network based approaches.
Dimension-Robust MCMC in Bayesian Inverse Problems
Authors: Victor Chen, Matthew M. Dunlop, Omiros Papaspiliopoulos, Andrew M. Stuart
Date: 2018-03-09 00:00:00
Tasks: Active Learning, Efficient Exploration, Gaussian Processes, General Classification, Multi-class Classification, Uncertainty Quantification

The methodology developed in this article is motivated by a wide range of
prediction and uncertainty quantification problems that arise in Statistics,
Machine Learning and Applied Mathematics, such as non-parametric regression,
multi-class classification and inversion of partial differential equations. One
popular formulation of such problems is as Bayesian inverse problems, where a
prior distribution is used to regularize inference on a high-dimensional latent
state, typically a function or a field. It is common that such priors are
non-Gaussian, for example piecewise-constant or heavy-tailed, and/or
hierarchical, in the sense of involving a further set of low-dimensional
parameters, which, for example, control the scale or smoothness of the latent
state. In this formulation prediction and uncertainty quantification relies on
efficient exploration of the posterior distribution of latent states and
parameters. This article introduces a framework for efficient MCMC sampling in
Bayesian inverse problems that capitalizes upon two fundamental ideas in MCMC,
non-centred parameterisations of hierarchical models and dimension-robust
samplers for latent Gaussian processes. Using a range of diverse applications
we showcase that the proposed framework is dimension-robust, that is, the
efficiency of the MCMC sampling does not deteriorate as the dimension of the
latent state gets higher. We showcase the full potential of the machinery we
develop in the article in semi-supervised multi-class classification, where our
sampling algorithm is used within an active learning framework to guide the
selection of input data to manually label in order to achieve high predictive
accuracy with a minimal number of labelled data.
Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer
Authors: Horia Porav, Will Maddern, Paul Newman
Date: 2018-03-09 00:00:00
Tasks: Appearance Transfer, Visual Place Recognition

We present a method of improving visual place recognition and metric
localisation under very strong appear- ance change. We learn an invertable
generator that can trans- form the conditions of images, e.g. from day to
night, summer to winter etc. This image transforming filter is explicitly
designed to aid and abet feature-matching using a new loss based on SURF
detector and dense descriptor maps. A network is trained to output synthetic
images optimised for feature matching given only an input RGB image, and these
generated images are used to localize the robot against a previously built map
using traditional sparse matching approaches. We benchmark our results using
multiple traversals of the Oxford RobotCar Dataset over a year-long period,
using one traversal as a map and the other to localise. We show that this
method significantly improves place recognition and localisation under changing
and adverse conditions, while reducing the number of mapping runs needed to
successfully achieve reliable localisation.
Linear-Time Sequence Classification using Restricted Boltzmann Machines
Authors: Son N. Tran, Srikanth Cherla, Artur Garcez, Tillman Weyde
Date: 2017-10-06 00:00:00
Tasks: Chunking, Classification, General Classification, Optical Character Recognition, Optical Character Recognition (OCR), Part-Of-Speech Tagging, Representation Learning

Classification of sequence data is the topic of interest for dynamic Bayesian
models and Recurrent Neural Networks (RNNs). While the former can explicitly
model the temporal dependencies between class variables, the latter have a
capability of learning representations. Several attempts have been made to
improve performance by combining these two approaches or increasing the
processing capability of the hidden units in RNNs. This often results in
complex models with a large number of learning parameters. In this paper, a
compact model is proposed which offers both representation learning and
temporal inference of class variables by rolling Restricted Boltzmann Machines
(RBMs) and class variables over time. We address the key issue of
intractability in this variant of RBMs by optimising a conditional
distribution, instead of a joint distribution. Experiments reported in the
paper on melody modelling and optical character recognition show that the
proposed model can outperform the state-of-the-art. Also, the experimental
results on optical character recognition, part-of-speech tagging and text
chunking demonstrate that our model is comparable to recurrent neural networks
with complex memory gates while requiring far fewer parameters.
Motion deblurring of faces
Authors: Grigorios G. Chrysos, Paolo Favaro, Stefanos Zafeiriou
Date: 2018-03-08 00:00:00
Tasks: Deblurring, Face Verification

Face analysis is a core part of computer vision, in which remarkable progress
has been observed in the past decades. Current methods achieve recognition and
tracking with invariance to fundamental modes of variation such as
illumination, 3D pose, expressions. Notwithstanding, a much less standing mode
of variation is motion deblurring, which however presents substantial
challenges in face analysis. Recent approaches either make oversimplifying
assumptions, e.g. in cases of joint optimization with other tasks, or fail to
preserve the highly structured shape/identity information. Therefore, we
propose a data-driven method that encourages identity preservation. The
proposed model includes two parallel streams (sub-networks): the first deblurs
the image, the second implicitly extracts and projects the identity of both the
sharp and the blurred image in similar subspaces. We devise a method for
creating realistic motion blur by averaging a variable number of frames to
train our model. The averaged images originate from a 2MF2 dataset with 10
million facial frames, which we introduce for the task. Considering deblurring
as an intermediate step, we utilize the deblurred outputs to conduct a thorough
experimentation on high-level face analysis tasks, i.e. landmark localization
and face verification. The experimental evaluation demonstrates the superiority
of our method.
Learning Deep Generative Models of Graphs
Authors: Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia
Date: 2018-03-08 00:00:00
Proceeding: ICLR 2018 1
Tasks: Graph Generation, Knowledge Graphs

Graphs are fundamental data structures which concisely capture the relational
structure in many important real-world domains, such as knowledge graphs,
physical and social interactions, language, and chemistry. Here we introduce a
powerful new approach for learning generative models over graphs, which can
capture both their structure and attributes. Our approach uses graph neural
networks to express probabilistic dependencies among a graph's nodes and edges,
and can, in principle, learn distributions over any arbitrary graph. In a
series of experiments our results show that once trained, our models can
generate good quality samples of both synthetic graphs as well as real
molecular graphs, both unconditionally and conditioned on data. Compared to
baselines that do not use graph-structured representations, our models often
perform far better. We also explore key challenges of learning generative
models of graphs, such as how to handle symmetries and ordering of elements
during the graph generation process, and offer possible solutions. Our work is
the first and most general approach for learning generative models over
arbitrary graphs, and opens new directions for moving away from restrictions of
vector- and sequence-like knowledge representations, toward more expressive and
flexible relational data structures.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Authors: Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam
Date: 2018-02-07 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decoder, Image Classification, Image Segmentation, Lesion Segmentation, Semantic Segmentation

Spatial pyramid pooling module or encode-decoder structure are used in deep
neural networks for semantic segmentation task. The former networks are able to
encode multi-scale contextual information by probing the incoming features with
filters or pooling operations at multiple rates and multiple effective
fields-of-view, while the latter networks can capture sharper object boundaries
by gradually recovering the spatial information. In this work, we propose to
combine the advantages from both methods. Specifically, our proposed model,
DeepLabv3+, extends DeepLabv3 by adding a simple yet effective decoder module
to refine the segmentation results especially along object boundaries. We
further explore the Xception model and apply the depthwise separable
convolution to both Atrous Spatial Pyramid Pooling and decoder modules,
resulting in a faster and stronger encoder-decoder network. We demonstrate the
effectiveness of the proposed model on PASCAL VOC 2012 and Cityscapes datasets,
achieving the test set performance of 89.0\% and 82.1\% without any
post-processing. Our paper is accompanied with a publicly available reference
implementation of the proposed models in Tensorflow at
\url{https://github.com/tensorflow/models/tree/master/research/deeplab}.
Efficient Loss-Based Decoding on Graphs For Extreme Classification
Authors: Itay Evron, Edward Moroshko, Koby Crammer
Date: 2018-03-08 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Classification, General Classification

In extreme classification problems, learning algorithms are required to map
instances to labels from an extremely large label set. We build on a recent
extreme classification framework with logarithmic time and space, and on a
general approach for error correcting output coding (ECOC) with loss-based
decoding, and introduce a flexible and efficient approach accompanied by
theoretical bounds. Our framework employs output codes induced by graphs, for
which we show how to perform efficient loss-based decoding to potentially
improve accuracy. In addition, our framework offers a tradeoff between
accuracy, model size and prediction time. We show how to find the sweet spot of
this tradeoff using only the training data. Our experimental study demonstrates
the validity of our assumptions and claims, and shows that our method is
competitive with state-of-the-art algorithms.
Precision and Recall for Time Series
Authors: Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich
Date: 2018-03-08 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Anomaly Detection, General Classification, Time Series, Time Series Analysis, Time Series Classification

Classical anomaly detection is principally concerned with point-based
anomalies, those anomalies that occur at a single point in time. Yet, many
real-world anomalies are range-based, meaning they occur over a period of time.
Motivated by this observation, we present a new mathematical model to evaluate
the accuracy of time series classification algorithms. Our model expands the
well-known Precision and Recall metrics to measure ranges, while simultaneously
enabling customization support for domain-specific preferences.
Visualizing Neural Network Developing Perturbation Theory
Authors: Yadong Wu, Pengfei Zhang, Huitao Shen, Hui Zhai
Date: 2018-02-12 00:00:00

In this letter, motivated by the question that whether the empirical fitting
of data by neural network can yield the same structure of physical laws, we
apply the neural network to a simple quantum mechanical two-body scattering
problem with short-range potentials, which by itself also plays an important
role in many branches of physics. We train a neural network to accurately
predict $ s $-wave scattering length, which governs the low-energy scattering
physics, directly from the scattering potential without solving Schr\"odinger
equation or obtaining the wavefunction. After analyzing the neural network, it
is shown that the neural network develops perturbation theory order by order
when the potential increases. This provides an important benchmark to the
machine-assisted physics research or even automated machine learning physics
laws.
Generalization in Metric Learning: Should the Embedding Layer be the Embedding Layer?
Authors: Nam Vo, James Hays
Date: 2018-03-08 00:00:00
Tasks: Image Retrieval, Metric Learning, Retrieval

This work studies deep metric learning under small to medium scale data as we
believe that better generalization could be a contributing factor to the
improvement of previous fine-grained image retrieval methods; it should be
considered when designing future techniques. In particular, we investigate
using other layers in a deep metric learning system (besides the embedding
layer) for feature extraction and analyze how well they perform on training
data and generalize to testing data. From this study, we suggest a new
regularization practice where one can add or choose a more optimal layer for
feature extraction. State-of-the-art performance is demonstrated on 3
fine-grained image retrieval benchmarks: Cars-196, CUB-200-2011, and Stanford
Online Product.
Generalization Properties of Doubly Stochastic Learning Algorithms
Authors: Junhong Lin, Lorenzo Rosasco
Date: 2017-07-03 00:00:00

Doubly stochastic learning algorithms are scalable kernel methods that
perform very well in practice. However, their generalization properties are not
well understood and their analysis is challenging since the corresponding
learning sequence may not be in the hypothesis space induced by the kernel. In
this paper, we provide an in-depth theoretical analysis for different variants
of doubly stochastic learning algorithms within the setting of nonparametric
regression in a reproducing kernel Hilbert space and considering the square
loss. Particularly, we derive convergence results on the generalization error
for the studied algorithms either with or without an explicit penalty term. To
the best of our knowledge, the derived results for the unregularized variants
are the first of this kind, while the results for the regularized variants
improve those in the literature. The novelties in our proof are a sample error
bound that requires controlling the trace norm of a cumulative operator, and a
refined analysis of bounding initial error.
A model of reward-modulated motor learning with parallelcortical and basal ganglia pathways
Authors: Ryan Pyle, Robert Rosenbaum
Date: 2018-03-08 00:00:00

Many recent studies of the motor system are divided into two distinct
approaches: Those that investigate how motor responses are encoded in cortical
neurons' firing rate dynamics and those that study the learning rules by which
mammals and songbirds develop reliable motor responses. Computationally, the
first approach is encapsulated by reservoir computing models, which can learn
intricate motor tasks and produce internal dynamics strikingly similar to those
of motor cortical neurons, but rely on biologically unrealistic learning rules.
The more realistic learning rules developed by the second approach are often
derived for simplified, discrete tasks in contrast to the intricate dynamics
that characterize real motor responses. We bridge these two approaches to
develop a biologically realistic learning rule for reservoir computing. Our
algorithm learns simulated motor tasks on which previous reservoir computing
algorithms fail, and reproduces experimental findings including those that
relate motor learning to Parkinson's disease and its treatment.
Totally Looks Like - How Humans Compare, Compared to Machines
Authors: Amir Rosenfeld, Markus D. Solbach, John K. Tsotsos
Date: 2018-03-05 00:00:00

Perceptual judgment of image similarity by humans relies on rich internal
representations ranging from low-level features to high-level concepts, scene
properties and even cultural associations. However, existing methods and
datasets attempting to explain perceived similarity use stimuli which arguably
do not cover the full breadth of factors that affect human similarity
judgments, even those geared toward this goal. We introduce a new dataset
dubbed Totally-Looks-Like (TLL) after a popular entertainment website, which
contains images paired by humans as being visually similar. The dataset
contains 6016 image-pairs from the wild, shedding light upon a rich and diverse
set of criteria employed by human beings. We conduct experiments to try to
reproduce the pairings via features extracted from state-of-the-art deep
convolutional neural networks, as well as additional human experiments to
verify the consistency of the collected data. Though we create conditions to
artificially make the matching task increasingly easier, we show that
machine-extracted representations perform very poorly in terms of reproducing
the matching selected by humans. We discuss and analyze these results,
suggesting future directions for improvement of learned image representations.
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling
Authors: Sayed Hadi Hashemi, Sangeetha Abdu Jyothi, Roy H. Campbell
Date: 2018-03-08 00:00:00
Tasks: Deep Learning, Scheduling

State-of-the-art deep learning systems rely on iterative distributed training
to tackle the increasing complexity of models and input data. The iteration
time in these communication-heavy systems depends on the computation time,
communication time and the extent of overlap of computation and communication.
  In this work, we identify a shortcoming in systems with graph representation
for computation, such as TensorFlow and PyTorch, that result in high variance
in iteration time --- random order of received parameters across workers. We
develop a system, TicTac, to improve the iteration time by fixing this issue in
distributed deep learning with Parameter Servers while guaranteeing
near-optimal overlap of communication and computation. TicTac identifies and
enforces an order of network transfers which improves the iteration time using
prioritization. Our system is implemented over TensorFlow and requires no
changes to the model or developer inputs. TicTac improves the throughput by up
to $37.7\%$ in inference and $19.2\%$ in training, while also reducing
straggler effect by up to $2.3\times$. Our code is publicly available.
ParlAI: A Dialog Research Software Platform
Authors: Alexander H. Miller, Will Feng, Adam Fisch, Jiasen Lu, Dhruv Batra, Antoine Bordes, Devi Parikh, Jason Weston
Date: 2017-05-18 00:00:00
Proceeding: EMNLP 2017 9
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Visual Question Answering (VQA)

We introduce ParlAI (pronounced "par-lay"), an open-source software platform
for dialog research implemented in Python, available at http://parl.ai. Its
goal is to provide a unified framework for sharing, training and testing of
dialog models, integration of Amazon Mechanical Turk for data collection, human
evaluation, and online/reinforcement learning; and a repository of machine
learning models for comparing with others' models, and improving upon existing
architectures. Over 20 tasks are supported in the first release, including
popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, QACNN, QADailyMail,
CBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA. Several models are integrated,
including neural models such as memory networks, seq2seq and attentive LSTMs.