pwc-archive/papers-with-abstracts huggingface dataset:
Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features
Authors: Xiang Wang, ShaoDi You, Xi Li, Huimin Ma
Date: 2018-06-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification, Object, Segmentation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly-supervised semantic segmentation under image tags supervision is a
challenging task as it directly associates high-level semantic to low-level
appearance. To bridge this gap, in this paper, we propose an iterative
bottom-up and top-down framework which alternatively expands object regions and
optimizes segmentation network. We start from initial localization produced by
classification networks. While classification networks are only responsive to
small and coarse discriminative object regions, we argue that, these regions
contain significant common features about objects. So in the bottom-up step, we
mine common object features from the initial localization and expand object
regions with the mined features. To supplement non-discriminative regions,
saliency maps are then considered under Bayesian framework to refine the object
regions. Then in the top-down step, the refined object regions are used as
supervision to train the segmentation network and to predict object masks.
These object masks provide more accurate localization and contain more regions
of object. Further, we take these object masks as initial localization and mine
common object features from them. These processes are conducted iteratively to
progressively produce fine object masks and optimize segmentation networks.
Experimental results on Pascal VOC 2012 dataset demonstrate that the proposed
method outperforms previous state-of-the-art methods by a large margin.
FigureNet: A Deep Learning model for Question-Answering on Scientific Plots
Authors: Revanth Reddy, Rahul Ramesh, Ameet Deshpande, Mitesh M. Khapra
Date: 2018-06-12 00:00:00
Tasks: Deep Learning, Question Answering

Deep Learning has managed to push boundaries in a wide variety of tasks. One
area of interest is to tackle problems in reasoning and understanding, with an
aim to emulate human intelligence. In this work, we describe a deep learning
model that addresses the reasoning task of question-answering on categorical
plots. We introduce a novel architecture FigureNet, that learns to identify
various plot elements, quantify the represented values and determine a relative
ordering of these statistical values. We test our model on the FigureQA dataset
which provides images and accompanying questions for scientific plots like bar
graphs and pie charts, augmented with rich annotations. Our approach
outperforms the state-of-the-art Relation Networks baseline by approximately
$7\%$ on this dataset, with a training time that is over an order of magnitude
lesser.
Disentangled Sequential Autoencoder
Authors: Yingzhen Li, Stephan Mandt
Date: 2018-03-08 00:00:00
Proceeding: ICML 2018 7
Tasks: Video Compression

We present a VAE architecture for encoding and generating high dimensional
sequential data, such as video or audio. Our deep generative model learns a
latent representation of the data which is split into a static and dynamic
part, allowing us to approximately disentangle latent time-dependent features
(dynamics) from features which are preserved over time (content). This
architecture gives us partial control over generating content and dynamics by
conditioning on either one of these sets of features. In our experiments on
artificially generated cartoon video clips and voice recordings, we show that
we can convert the content of a given sequence into another one by such content
swapping. For audio, this allows us to convert a male speaker into a female
speaker and vice versa, while for video we can separately manipulate shapes and
dynamics. Furthermore, we give empirical evidence for the hypothesis that
stochastic RNNs as latent state models are more efficient at compressing and
generating long sequences than deterministic ones, which may be relevant for
applications in video compression.
Learning to Estimate Indoor Lighting from 3D Objects
Authors: Henrique Weber, Donald Prévost, Jean-François Lalonde
Date: 2018-06-11 00:00:00
Proceeding: 3DV 2018 - International Conference on 3D Vision 2018 9

In this work, we propose a step towards a more accurate prediction of the
environment light given a single picture of a known object. To achieve this, we
developed a deep learning method that is able to encode the latent space of
indoor lighting using few parameters and that is trained on a database of
environment maps. This latent space is then used to generate predictions of the
light that are both more realistic and accurate than previous methods. To
achieve this, our first contribution is a deep autoencoder which is capable of
learning the feature space that compactly models lighting. Our second
contribution is a convolutional neural network that predicts the light from a
single image of a known object. To train these networks, our third contribution
is a novel dataset that contains 21,000 HDR indoor environment maps. The
results indicate that the predictor can generate plausible lighting estimations
even from diffuse objects.
Boosting Black Box Variational Inference
Authors: Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch
Date: 2018-06-06 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Variational Inference

Approximating a probability density in a tractable manner is a central task
in Bayesian statistics. Variational Inference (VI) is a popular technique that
achieves tractability by choosing a relatively simple variational family.
Borrowing ideas from the classic boosting framework, recent approaches attempt
to \emph{boost} VI by replacing the selection of a single density with a
greedily constructed mixture of densities. In order to guarantee convergence,
previous works impose stringent assumptions that require significant effort for
practitioners. Specifically, they require a custom implementation of the greedy
step (called the LMO) for every probabilistic model with respect to an
unnatural variational family of truncated distributions. Our work fixes these
issues with novel theoretical and algorithmic insights. On the theoretical
side, we show that boosting VI satisfies a relaxed smoothness assumption which
is sufficient for the convergence of the functional Frank-Wolfe (FW) algorithm.
Furthermore, we rephrase the LMO problem and propose to maximize the Residual
ELBO (RELBO) which replaces the standard ELBO optimization in VI. These
theoretical enhancements allow for black box implementation of the boosting
subroutine. Finally, we present a stopping criterion drawn from the duality gap
in the classic FW analyses and exhaustive experiments to illustrate the
usefulness of our theoretical and algorithmic contributions.
Adversarial Attacks on Variational Autoencoders
Authors: George Gondim-Ribeiro, Pedro Tabacof, Eduardo Valle
Date: 2018-06-12 00:00:00
Tasks: BIG-bench Machine Learning

Adversarial attacks are malicious inputs that derail machine-learning models.
We propose a scheme to attack autoencoders, as well as a quantitative
evaluation framework that correlates well with the qualitative assessment of
the attacks. We assess --- with statistically validated experiments --- the
resistance to attacks of three variational autoencoders (simple, convolutional,
and DRAW) in three datasets (MNIST, SVHN, CelebA), showing that both DRAW's
recurrence and attention mechanism lead to better resistance. As autoencoders
are proposed for compressing data --- a scenario in which their safety is
paramount --- we expect more attention will be given to adversarial attacks on
them.
Accelerating Imitation Learning with Predictive Models
Authors: Ching-An Cheng, Xinyan Yan, Evangelos A. Theodorou, Byron Boots
Date: 2018-06-12 00:00:00
Tasks: Imitation Learning, Reinforcement Learning

Sample efficiency is critical in solving real-world reinforcement learning
problems, where agent-environment interactions can be costly. Imitation
learning from expert advice has proved to be an effective strategy for reducing
the number of interactions required to train a policy. Online imitation
learning, which interleaves policy evaluation and policy optimization, is a
particularly effective technique with provable performance guarantees. In this
work, we seek to further accelerate the convergence rate of online imitation
learning, thereby making it more sample efficient. We propose two model-based
algorithms inspired by Follow-the-Leader (FTL) with prediction: MoBIL-VI based
on solving variational inequalities and MoBIL-Prox based on stochastic
first-order updates. These two methods leverage a model to predict future
gradients to speed up policy learning. When the model oracle is learned online,
these algorithms can provably accelerate the best known convergence rate up to
an order. Our algorithms can be viewed as a generalization of stochastic
Mirror-Prox (Juditsky et al., 2011), and admit a simple constructive FTL-style
analysis of performance.
Unsupervised Meta-Learning for Reinforcement Learning
Authors: Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, Sergey Levine
Date: 2018-06-12 00:00:00
Proceeding: ICLR 2020 1
Tasks: Meta-Learning, Meta Reinforcement Learning, Multi-Task Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Meta-learning algorithms use past experience to learn to quickly solve new tasks. In the context of reinforcement learning, meta-learning algorithms acquire reinforcement learning procedures to solve new problems more efficiently by utilizing experience from prior tasks. The performance of meta-learning algorithms depends on the tasks available for meta-training: in the same way that supervised learning generalizes best to test points drawn from the same distribution as the training points, meta-learning methods generalize best to tasks from the same distribution as the meta-training tasks. In effect, meta-reinforcement learning offloads the design burden from algorithm design to task design. If we can automate the process of task design as well, we can devise a meta-learning algorithm that is truly automated. In this work, we take a step in this direction, proposing a family of unsupervised meta-learning algorithms for reinforcement learning. We motivate and describe a general recipe for unsupervised meta-reinforcement learning, and present an instantiation of this approach. Our conceptual and theoretical contributions consist of formulating the unsupervised meta-reinforcement learning problem and describing how task proposals based on mutual information can be used to train optimal meta-learners. Our experimental results indicate that unsupervised meta-reinforcement learning effectively acquires accelerated reinforcement learning procedures without the need for manual task design and these procedures exceed the performance of learning from scratch.
Measures of Tractography Convergence
Authors: Daniel Moyer, Paul M. Thompson, Greg Ver Steeg
Date: 2018-06-12 00:00:00
Tasks: Diffusion  MRI

In the present work, we use information theory to understand the empirical
convergence rate of tractography, a widely-used approach to reconstruct
anatomical fiber pathways in the living brain. Based on diffusion MRI data,
tractography is the starting point for many methods to study brain
connectivity. Of the available methods to perform tractography, most
reconstruct a finite set of streamlines, or 3D curves, representing probable
connections between anatomical regions, yet relatively little is known about
how the sampling of this set of streamlines affects downstream results, and how
exhaustive the sampling should be. Here we provide a method to measure the
information theoretic surprise (self-cross entropy) for tract sampling schema.
We then empirically assess four streamline methods. We demonstrate that the
relative information gain is very low after a moderate number of streamlines
have been generated for each tested method. The results give rise to several
guidelines for optimal sampling in brain connectivity analyses.
Organizing Experience: A Deeper Look at Replay Mechanisms for Sample-based Planning in Continuous State Domains
Authors: Yangchen Pan, Muhammad Zaheer, Adam White, Andrew Patterson, Martha White
Date: 2018-06-12 00:00:00

Model-based strategies for control are critical to obtain sample efficient
learning. Dyna is a planning paradigm that naturally interleaves learning and
planning, by simulating one-step experience to update the action-value
function. This elegant planning strategy has been mostly explored in the
tabular setting. The aim of this paper is to revisit sample-based planning, in
stochastic and continuous domains with learned models. We first highlight the
flexibility afforded by a model over Experience Replay (ER). Replay-based
methods can be seen as stochastic planning methods that repeatedly sample from
a buffer of recent agent-environment interactions and perform updates to
improve data efficiency. We show that a model, as opposed to a replay buffer,
is particularly useful for specifying which states to sample from during
planning, such as predecessor states that propagate information in reverse from
a state more quickly. We introduce a semi-parametric model learning approach,
called Reweighted Experience Models (REMs), that makes it simple to sample next
states or predecessors. We demonstrate that REM-Dyna exhibits similar
advantages over replay-based methods in learning in continuous state problems,
and that the performance gap grows when moving to stochastic domains, of
increasing size.
Fast forwarding Egocentric Videos by Listening and Watching
Authors: Vinicius S. Furlan, Ruzena Bajcsy, Erickson R. Nascimento
Date: 2018-06-12 00:00:00

The remarkable technological advance in well-equipped wearable devices is
pushing an increasing production of long first-person videos. However, since
most of these videos have long and tedious parts, they are forgotten or never
seen. Despite a large number of techniques proposed to fast-forward these
videos by highlighting relevant moments, most of them are image based only.
Most of these techniques disregard other relevant sensors present in the
current devices such as high-definition microphones. In this work, we propose a
new approach to fast-forward videos using psychoacoustic metrics extracted from
the soundtrack. These metrics can be used to estimate the annoyance of a
segment allowing our method to emphasize moments of sound pleasantness. The
efficiency of our method is demonstrated through qualitative results and
quantitative results as far as of speed-up and instability are concerned.
Imperfect Segmentation Labels: How Much Do They Matter?
Authors: Nicholas Heller, Joshua Dean, Nikolaos Papanikolopoulos
Date: 2018-06-12 00:00:00
Tasks: Liver Segmentation, Segmentation, Semantic Segmentation

Labeled datasets for semantic segmentation are imperfect, especially in
medical imaging where borders are often subtle or ill-defined. Little work has
been done to analyze the effect that label errors have on the performance of
segmentation methodologies. Here we present a large-scale study of model
performance in the presence of varying types and degrees of error in training
data. We trained U-Net, SegNet, and FCN32 several times for liver segmentation
with 10 different modes of ground-truth perturbation. Our results show that for
each architecture, performance steadily declines with boundary-localized
errors, however, U-Net was significantly more robust to jagged boundary errors
than the other architectures. We also found that each architecture was very
robust to non-boundary-localized errors, suggesting that boundary-localized
errors are fundamentally different and more challenging problem than random
label errors in a classification setting.
Deep Learning to Detect Redundant Method Comments
Authors: Annie Louis, Santanu Kumar Dash, Earl T. Barr, Charles Sutton
Date: 2018-06-12 00:00:00
Tasks: Deep Learning

Comments in software are critical for maintenance and reuse. But apart from
prescriptive advice, there is little practical support or quantitative
understanding of what makes a comment useful. In this paper, we introduce the
task of identifying comments which are uninformative about the code they are
meant to document. To address this problem, we introduce the notion of comment
entailment from code, high entailment indicating that a comment's natural
language semantics can be inferred directly from the code. Although not all
entailed comments are low quality, comments that are too easily inferred, for
example, comments that restate the code, are widely discouraged by authorities
on software style. Based on this, we develop a tool called CRAIC which scores
method-level comments for redundancy. Highly redundant comments can then be
expanded or alternately removed by the developer. CRAIC uses deep language
models to exploit large software corpora without requiring expensive manual
annotations of entailment. We show that CRAIC can perform the comment
entailment task with good agreement with human judgements. Our findings also
have implications for documentation tools. For example, we find that common
tags in Javadoc are at least two times more predictable from code than
non-Javadoc sentences, suggesting that Javadoc tags are less informative than
more free-form comments
Improving Regression Performance with Distributional Losses
Authors: Ehsan Imani, Martha White
Date: 2018-06-12 00:00:00
Proceeding: ICML 2018 7
Tasks: regression, Reinforcement Learning

There is growing evidence that converting targets to soft targets in
supervised learning can provide considerable gains in performance. Much of this
work has considered classification, converting hard zero-one values to soft
labels---such as by adding label noise, incorporating label ambiguity or using
distillation. In parallel, there is some evidence from a regression setting in
reinforcement learning that learning distributions can improve performance. In
this work, we investigate the reasons for this improvement, in a regression
setting. We introduce a novel distributional regression loss, and similarly
find it significantly improves prediction accuracy. We investigate several
common hypotheses, around reducing overfitting and improved representations. We
instead find evidence for an alternative hypothesis: this loss is easier to
optimize, with better behaved gradients, resulting in improved generalization.
We provide theoretical support for this alternative hypothesis, by
characterizing the norm of the gradients of this loss.
A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values
Authors: Ruifei Cui, Ioan Gabriel Bucur, Perry Groot, Tom Heskes
Date: 2018-06-12 00:00:00
Tasks: Missing Values

We consider the problem of learning parameters of latent variable models from
mixed (continuous and ordinal) data with missing values. We propose a novel
Bayesian Gaussian copula factor (BGCF) approach that is consistent under
certain conditions and that is quite robust to the violations of these
conditions. In simulations, BGCF substantially outperforms two state-of-the-art
alternative approaches. An illustration on the `Holzinger & Swineford 1939'
dataset indicates that BGCF is favorable over the so-called robust maximum
likelihood (MLR) even if the data match the assumptions of MLR.
Streaming PCA and Subspace Tracking: The Missing Data Case
Authors: Laura Balzano, Yuejie Chi, Yue M. Lu
Date: 2018-06-12 00:00:00
Tasks: Decision Making

For many modern applications in science and engineering, data are collected
in a streaming fashion carrying time-varying information, and practitioners
need to process them with a limited amount of memory and computational
resources in a timely manner for decision making. This often is coupled with
the missing data problem, such that only a small fraction of data attributes
are observed. These complications impose significant, and unconventional,
constraints on the problem of streaming Principal Component Analysis (PCA) and
subspace tracking, which is an essential building block for many inference
tasks in signal processing and machine learning. This survey article reviews a
variety of classical and recent algorithms for solving this problem with low
computational and memory complexities, particularly those applicable in the big
data regime with missing data. We illustrate that streaming PCA and subspace
tracking algorithms can be understood through algebraic and geometric
perspectives, and they need to be adjusted carefully to handle missing data.
Both asymptotic and non-asymptotic convergence guarantees are reviewed.
Finally, we benchmark the performance of several competitive algorithms in the
presence of missing data for both well-conditioned and ill-conditioned systems.
Knowledge Distillation by On-the-Fly Native Ensemble
Authors: Xu Lan, Xiatian Zhu, Shaogang Gong
Date: 2018-06-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Computational Efficiency, image-classification, Image Classification, Knowledge Distillation

Knowledge distillation is effective to train small and generalisable network
models for meeting the low-memory and fast running requirements. Existing
offline distillation methods rely on a strong pre-trained teacher, which
enables favourable knowledge discovery and transfer but requires a complex
two-phase training procedure. Online counterparts address this limitation at
the price of lacking a highcapacity teacher. In this work, we present an
On-the-fly Native Ensemble (ONE) strategy for one-stage online distillation.
Specifically, ONE trains only a single multi-branch network while
simultaneously establishing a strong teacher on-the- fly to enhance the
learning of target network. Extensive evaluations show that ONE improves the
generalisation performance a variety of deep neural networks more significantly
than alternative methods on four image classification dataset: CIFAR10,
CIFAR100, SVHN, and ImageNet, whilst having the computational efficiency
advantages.
Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation
Authors: Jun Chen, Guang Yang, Zhifan Gao, Hao Ni, Elsa Angelini, Raad Mohiaddin, Tom Wong, Yanping Zhang, Xiuquan Du, Heye Zhang, Jennifer Keegan, David Firmin
Date: 2018-06-12 00:00:00
Tasks: Anatomy, Segmentation

Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in
atrial fibrillation (AF) patients has recently emerged as a promising technique
to stratify patients, guide ablation therapy and predict treatment success.
Visualisation and quantification of scar tissues require a segmentation of both
the left atrium (LA) and the high intensity scar regions from LGE-CMRI images.
These two segmentation tasks are challenging due to the cancelling of healthy
tissue signal, low signal-to-noise ratio and often limited image quality in
these patients. Most approaches require manual supervision and/or a second
bright-blood MRI acquisition for anatomical segmentation. Segmenting both the
LA anatomy and the scar tissues automatically from a single LGE-CMRI
acquisition is highly in demand. In this study, we proposed a novel fully
automated multiview two-task (MVTT) recursive attention model working directly
on LGE-CMRI images that combines a sequential learning and a dilated residual
learning to segment the LA (including attached pulmonary veins) and delineate
the atrial scars simultaneously via an innovative attention model. Compared to
other state-of-the-art methods, the proposed MVTT achieves compelling
improvement, enabling to generate a patient-specific anatomical and atrial scar
assessment model.