pwc-archive/papers-with-abstracts huggingface dataset:
A Feature Clustering Approach Based on Histogram of Oriented Optical Flow and Superpixels
Authors: A. M. R. R. Bandara, L. Ranathunga, N. A. Abdullah
Date: 2018-02-28 00:00:00
Tasks: Clustering, Optical Flow Estimation, Superpixels

Visual feature clustering is one of the cost-effective approaches to segment
objects in videos. However, the assumptions made for developing the existing
algorithms prevent them from being used in situations like segmenting an
unknown number of static and moving objects under heavy camera movements. This
paper addresses the problem by introducing a clustering approach based on
superpixels and short-term Histogram of Oriented Optical Flow (HOOF). Salient
Dither Pattern Feature (SDPF) is used as the visual feature to track the flow
and Simple Linear Iterative Clustering (SLIC) is used for obtaining the
superpixels. This new clustering approach is based on merging superpixels by
comparing short term local HOOF and a color cue to form high-level semantic
segments. The new approach was compared with one of the latest feature
clustering approaches based on K-Means in eight-dimensional space and the
results revealed that the new approach is better by means of consistency,
completeness, and spatial accuracy. Further, the new approach completely solved
the problem of not knowing the number of objects in a scene.
INSPECTRE: Privately Estimating the Unseen
Authors: Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang
Date: 2018-02-28 00:00:00
Proceeding: ICML 2018 7
Tasks: Sensitivity

We develop differentially private methods for estimating various
distributional properties. Given a sample from a discrete distribution $p$,
some functional $f$, and accuracy and privacy parameters $\alpha$ and
$\varepsilon$, the goal is to estimate $f(p)$ up to accuracy $\alpha$, while
maintaining $\varepsilon$-differential privacy of the sample.
  We prove almost-tight bounds on the sample size required for this problem for
several functionals of interest, including support size, support coverage, and
entropy. We show that the cost of privacy is negligible in a variety of
settings, both theoretically and experimentally. Our methods are based on a
sensitivity analysis of several state-of-the-art methods for estimating these
properties with sublinear sample complexities.
Model-Ensemble Trust-Region Policy Optimization
Authors: Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel
Date: 2018-02-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: continuous-control, Continuous Control, model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-free reinforcement learning (RL) methods are succeeding in a growing
number of tasks, aided by recent advances in deep learning. However, they tend
to suffer from high sample complexity, which hinders their use in real-world
domains. Alternatively, model-based reinforcement learning promises to reduce
sample complexity, but tends to require careful tuning and to date have
succeeded mainly in restrictive domains where simple models are sufficient for
learning. In this paper, we analyze the behavior of vanilla model-based
reinforcement learning methods when deep neural networks are used to learn both
the model and the policy, and show that the learned policy tends to exploit
regions where insufficient data is available for the model to be learned,
causing instability in training. To overcome this issue, we propose to use an
ensemble of models to maintain the model uncertainty and regularize the
learning process. We further show that the use of likelihood ratio derivatives
yields much more stable learning than backpropagation through time. Altogether,
our approach Model-Ensemble Trust-Region Policy Optimization (ME-TRPO)
significantly reduces the sample complexity compared to model-free deep RL
methods on challenging continuous control benchmark tasks.
A Riemannian gossip approach to subspace learning on Grassmann manifold
Authors: Bamdev Mishra, Hiroyuki Kasai, Pratik Jawanpuria, Atul Saroop
Date: 2017-05-01 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, regression

In this paper, we focus on subspace learning problems on the Grassmann
manifold. Interesting applications in this setting include low-rank matrix
completion and low-dimensional multivariate regression, among others. Motivated
by privacy concerns, we aim to solve such problems in a decentralized setting
where multiple agents have access to (and solve) only a part of the whole
optimization problem. The agents communicate with each other to arrive at a
consensus, i.e., agree on a common quantity, via the gossip protocol.
  We propose a novel cost function for subspace learning on the Grassmann
manifold, which is a weighted sum of several sub-problems (each solved by an
agent) and the communication cost among the agents. The cost function has a
finite sum structure. In the proposed modeling approach, different agents learn
individual local subspace but they achieve asymptotic consensus on the global
learned subspace. The approach is scalable and parallelizable. Numerical
experiments show the efficacy of the proposed decentralized algorithms on
various matrix completion and multivariate regression benchmarks.
Modeling Activity Tracker Data Using Deep Boltzmann Machines
Authors: Martin Treppner, Stefan Lenz, Harald Binder, Daniela Zöller
Date: 2018-02-28 00:00:00

Commercial activity trackers are set to become an essential tool in health
research, due to increasing availability in the general population. The
corresponding vast amounts of mostly unlabeled data pose a challenge to
statistical modeling approaches. To investigate the feasibility of deep
learning approaches for unsupervised learning with such data, we examine weekly
usage patterns of Fitbit activity trackers with deep Boltzmann machines (DBMs).
This method is particularly suitable for modeling complex joint distributions
via latent variables. We also chose this specific procedure because it is a
generative approach, i.e., artificial samples can be generated to explore the
learned structure. We describe how the data can be preprocessed to be
compatible with binary DBMs. The results reveal two distinct usage patterns in
which one group frequently uses trackers on Mondays and Tuesdays, whereas the
other uses trackers during the entire week. This exemplary result shows that
DBMs are feasible and can be useful for modeling activity tracker data.
Near-Optimal Sample Complexity Bounds for Maximum Likelihood Estimation of Multivariate Log-concave Densities
Authors: Timothy Carpenter, Ilias Diakonikolas, Anastasios Sidiropoulos, Alistair Stewart
Date: 2018-02-28 00:00:00

We study the problem of learning multivariate log-concave densities with
respect to a global loss function. We obtain the first upper bound on the
sample complexity of the maximum likelihood estimator (MLE) for a log-concave
density on $\mathbb{R}^d$, for all $d \geq 4$. Prior to this work, no finite
sample upper bound was known for this estimator in more than $3$ dimensions.
  In more detail, we prove that for any $d \geq 1$ and $\epsilon>0$, given
$\tilde{O}_d((1/\epsilon)^{(d+3)/2})$ samples drawn from an unknown log-concave
density $f_0$ on $\mathbb{R}^d$, the MLE outputs a hypothesis $h$ that with
high probability is $\epsilon$-close to $f_0$, in squared Hellinger loss. A
sample complexity lower bound of $\Omega_d((1/\epsilon)^{(d+1)/2})$ was
previously known for any learning algorithm that achieves this guarantee. We
thus establish that the sample complexity of the log-concave MLE is
near-optimal, up to an $\tilde{O}(1/\epsilon)$ factor.
Statistical shape analysis in a Bayesian framework for shapes in two and three dimensions
Authors: Thomai Tsiftsi
Date: 2018-02-28 00:00:00
Tasks: Classification, General Classification

In this paper, we describe a novel shape classification method which is
embedded in the Bayesian paradigm. We discuss the modelling and the resulting
shape classification algorithm for two and three dimensional data shapes. We
conclude by evaluating the efficiency and efficacy of the proposed algorithm on
the Kimia shape database for the two dimensional case.
Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction
Authors: Patrick Verga, Emma Strubell, Andrew McCallum
Date: 2018-02-28 00:00:00
Proceeding: NAACL 2018 6
Tasks: All, Relation, Relation Extraction, Sentence

Most work in relation extraction forms a prediction by looking at a short
span of text within a single sentence containing a single entity pair mention.
This approach often does not consider interactions across mentions, requires
redundant computation for each mention pair, and ignores relationships
expressed across sentence boundaries. These problems are exacerbated by the
document- (rather than sentence-) level annotation common in biological text.
In response, we propose a model which simultaneously predicts relationships
between all mention pairs in a document. We form pairwise predictions over
entire paper abstracts using an efficient self-attention encoder. All-pairs
mention scores allow us to perform multi-instance learning by aggregating over
mentions to form entity pair representations. We further adapt to settings
without mention-level annotation by jointly training to predict named entities
and adding a corpus of weakly labeled data. In experiments on two Biocreative
benchmark datasets, we achieve state of the art performance on the Biocreative
V Chemical Disease Relation dataset for models without external KB resources.
We also introduce a new dataset an order of magnitude larger than existing
human-annotated biological information extraction datasets and more accurate
than distantly supervised alternatives.
Learning by Playing - Solving Sparse Reward Tasks from Scratch
Authors: Martin Riedmiller, Roland Hafner, Thomas Lampe, Michael Neunert, Jonas Degrave, Tom Van de Wiele, Volodymyr Mnih, Nicolas Heess, Jost Tobias Springenberg
Date: 2018-02-28 00:00:00
Proceeding: ICML 2018 7
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in
the context of Reinforcement Learning (RL). SAC-X enables learning of complex
behaviors - from scratch - in the presence of multiple sparse reward signals.
To this end, the agent is equipped with a set of general auxiliary tasks, that
it attempts to learn simultaneously via off-policy RL. The key idea behind our
method is that active (learned) scheduling and execution of auxiliary policies
allows the agent to efficiently explore its environment - enabling it to excel
at sparse reward RL. Our experiments in several challenging robotic
manipulation settings demonstrate the power of our approach.
Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl
Authors: Alexander Panchenko, Eugen Ruppert, Stefano Faralli, Simone Paolo Ponzetto, Chris Biemann
Date: 2017-10-04 00:00:00
Proceeding: LREC 2018 5
Tasks: Open Information Extraction, Question Answering, Word Embeddings

We present DepCC, the largest-to-date linguistically analyzed corpus in
English including 365 million documents, composed of 252 billion tokens and 7.5
billion of named entity occurrences in 14.3 billion sentences from a web-scale
crawl of the \textsc{Common Crawl} project. The sentences are processed with a
dependency parser and with a named entity tagger and contain provenance
information, enabling various applications ranging from training syntax-based
word embeddings to open information extraction and question answering. We built
an index of all sentences and their linguistic meta-data enabling quick search
across the corpus. We demonstrate the utility of this corpus on the verb
similarity task by showing that a distributional model trained on our corpus
yields better results than models trained on smaller corpora, like Wikipedia.
This distributional model outperforms the state of art models of verb
similarity trained on smaller corpora on the SimVerb3500 dataset.
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Authors: Scott Reed, Yutian Chen, Thomas Paine, Aäron van den Oord, S. M. Ali Eslami, Danilo Rezende, Oriol Vinyals, Nando de Freitas
Date: 2017-10-27 00:00:00
Proceeding: ICLR 2018 1
Tasks: Density Estimation, Image Generation, Meta-Learning

Deep autoregressive models have shown state-of-the-art performance in density
estimation for natural images on large-scale datasets such as ImageNet.
However, such models require many thousands of gradient-based weight updates
and unique image examples for training. Ideally, the models would rapidly learn
visual concepts from only a handful of examples, similar to the manner in which
humans learns across many vision tasks. In this paper, we show how 1) neural
attention and 2) meta learning techniques can be used in combination with
autoregressive models to enable effective few-shot density estimation. Our
proposed modifications to PixelCNN result in state-of-the art few-shot density
estimation on the Omniglot dataset. Furthermore, we visualize the learned
attention policy and find that it learns intuitive algorithms for simple tasks
such as image mirroring on ImageNet and handwriting on Omniglot without
supervision. Finally, we extend the model to natural images and demonstrate
few-shot image generation on the Stanford Online Products dataset.
Stronger generalization bounds for deep nets via a compression approach
Authors: Sanjeev Arora, Rong Ge, Behnam Neyshabur, Yi Zhang
Date: 2018-02-14 00:00:00
Proceeding: ICML 2018 7
Tasks: Generalization Bounds

Deep nets generalize well despite having more parameters than the number of
training samples. Recent works try to give an explanation using PAC-Bayes and
Margin-based analyses, but do not as yet result in sample complexity bounds
better than naive parameter counting. The current paper shows generalization
bounds that're orders of magnitude better in practice. These rely upon new
succinct reparametrizations of the trained net --- a compression that is
explicit and efficient. These yield generalization bounds via a simple
compression-based framework introduced here. Our results also provide some
theoretical justification for widespread empirical success in compressing deep
nets. Analysis of correctness of our compression relies upon some newly
identified \textquotedblleft noise stability\textquotedblright properties of
trained deep nets, which are also experimentally verified. The study of these
properties and resulting generalization bounds are also extended to
convolutional nets, which had eluded earlier attempts on proving
generalization.
A theory of sequence indexing and working memory in recurrent neural networks
Authors: E. Paxon Frady, Denis Kleyko, Friedrich T. Sommer
Date: 2018-02-28 00:00:00
Tasks: Retrieval

To accommodate structured approaches of neural computation, we propose a
class of recurrent neural networks for indexing and storing sequences of
symbols or analog data vectors. These networks with randomized input weights
and orthogonal recurrent weights implement coding principles previously
described in vector symbolic architectures (VSA), and leverage properties of
reservoir computing. In general, the storage in reservoir computing is lossy
and crosstalk noise limits the retrieval accuracy and information capacity. A
novel theory to optimize memory performance in such networks is presented and
compared with simulation experiments. The theory describes linear readout of
analog data, and readout with winner-take-all error correction of symbolic data
as proposed in VSA models. We find that diverse VSA models from the literature
have universal performance properties, which are superior to what previous
analyses predicted. Further, we propose novel VSA models with the statistically
optimal Wiener filter in the readout that exhibit much higher information
capacity, in particular for storing analog data.
  The presented theory also applies to memory buffers, networks with gradual
forgetting, which can operate on infinite data streams without memory overflow.
Interestingly, we find that different forgetting mechanisms, such as
attenuating recurrent weights or neural nonlinearities, produce very similar
behavior if the forgetting time constants are aligned. Such models exhibit
extensive capacity when their forgetting time constant is optimized for given
noise conditions and network size. These results enable the design of new types
of VSA models for the online processing of data streams.
Novelty Detection with GAN
Authors: Mark Kliger, Shachar Fleishman
Date: 2018-02-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, Novelty Detection

The ability of a classifier to recognize unknown inputs is important for many
classification-based systems. We discuss the problem of simultaneous
classification and novelty detection, i.e. determining whether an input is from
the known set of classes and from which specific class, or from an unknown
domain and does not belong to any of the known classes. We propose a method
based on the Generative Adversarial Networks (GAN) framework. We show that a
multi-class discriminator trained with a generator that generates samples from
a mixture of nominal and novel data distributions is the optimal novelty
detector. We approximate that generator with a mixture generator trained with
the Feature Matching loss and empirically show that the proposed method
outperforms conventional methods for novelty detection. Our findings
demonstrate a simple, yet powerful new application of the GAN framework for the
task of novelty detection.
Exactly Robust Kernel Principal Component Analysis
Authors: Jicong Fan, Tommy W. S. Chow
Date: 2018-02-28 00:00:00
Tasks: Clustering

Robust principal component analysis (RPCA) can recover low-rank matrices when
they are corrupted by sparse noises. In practice, many matrices are, however,
of high-rank and hence cannot be recovered by RPCA. We propose a novel method
called robust kernel principal component analysis (RKPCA) to decompose a
partially corrupted matrix as a sparse matrix plus a high or full-rank matrix
with low latent dimensionality. RKPCA can be applied to many problems such as
noise removal and subspace clustering and is still the only unsupervised
nonlinear method robust to sparse noises. Our theoretical analysis shows that,
with high probability, RKPCA can provide high recovery accuracy. The
optimization of RKPCA involves nonconvex and indifferentiable problems. We
propose two nonconvex optimization algorithms for RKPCA. They are alternating
direction method of multipliers with backtracking line search and proximal
linearized minimization with adaptive step size. Comparative studies in noise
removal and robust subspace clustering corroborate the effectiveness and
superiority of RKPCA.
Learning Multi-Level Hierarchies with Hindsight
Authors: Andrew Levy, George Konidaris, Robert Platt, Kate Saenko
Date: 2017-12-04 00:00:00
Tasks: Decision Making, Hierarchical Reinforcement Learning, Reinforcement Learning, Sequential Decision Making

Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions. In order to realize this potential of faster learning, hierarchical agents need to be able to learn their multiple levels of policies in parallel so these simpler subproblems can be solved simultaneously. Yet, learning multiple levels of policies in parallel is hard because it is inherently unstable: changes in a policy at one level of the hierarchy may cause changes in the transition and reward functions at higher levels in the hierarchy, making it difficult to jointly learn multiple levels of policies. In this paper, we introduce a new Hierarchical Reinforcement Learning (HRL) framework, Hierarchical Actor-Critic (HAC), that can overcome the instability issues that arise when agents try to jointly learn multiple levels of policies. The main idea behind HAC is to train each level of the hierarchy independently of the lower levels by training each level as if the lower level policies are already optimal. We demonstrate experimentally in both grid world and simulated robotics domains that our approach can significantly accelerate learning relative to other non-hierarchical and hierarchical methods. Indeed, our framework is the first to successfully learn 3-level hierarchies in parallel in tasks with continuous state and action spaces.
Retrieval and Registration of Long-Range Overlapping Frames for Scalable Mosaicking of In Vivo Fetoscopy
Authors: Loïc Peter, Marcel Tella-Amo, Dzhoshkun Ismail Shakir, George Attilakos, Ruwan Wimalasundera, Jan Deprest, Sébastien Ourselin, Tom Vercauteren
Date: 2018-02-28 00:00:00
Tasks: Retrieval

Purpose: The standard clinical treatment of Twin-to-Twin Transfusion Syndrome
consists in the photo-coagulation of undesired anastomoses located on the
placenta which are responsible to a blood transfer between the two twins. While
being the standard of care procedure, fetoscopy suffers from a limited
field-of-view of the placenta resulting in missed anastomoses. To facilitate
the task of the clinician, building a global map of the placenta providing a
larger overview of the vascular network is highly desired. Methods: To overcome
the challenging visual conditions inherent to in vivo sequences (low contrast,
obstructions or presence of artifacts, among others), we propose the following
contributions: (i) robust pairwise registration is achieved by aligning the
orientation of the image gradients, and (ii) difficulties regarding long-range
consistency (e.g. due to the presence of outliers) is tackled via a bag-of-word
strategy, which identifies overlapping frames of the sequence to be registered
regardless of their respective location in time. Results: In addition to visual
difficulties, in vivo sequences are characterised by the intrinsic absence of
gold standard. We present mosaics motivating qualitatively our methodological
choices and demonstrating their promising aspect. We also demonstrate
semi-quantitatively, via visual inspection of registration results, the
efficacy of our registration approach in comparison to two standard baselines.
Conclusion: This paper proposes the first approach for the construction of
mosaics of placenta in in vivo fetoscopy sequences. Robustness to visual
challenges during registration and long-range temporal consistency are
proposed, offering first positive results on in vivo data for which standard
mosaicking techniques are not applicable.
Automatic topography of high-dimensional data sets by non-parametric Density Peak clustering
Authors: Maria d'Errico, Elena Facco, Alessandro Laio, Alex Rodriguez
Date: 2018-02-28 00:00:00
Tasks: Clustering

Data analysis in high-dimensional spaces aims at obtaining a synthetic description of a data set, revealing its main structure and its salient features. We here introduce an approach providing this description in the form of a topography of the data, namely a human-readable chart of the probability density from which the data are harvested. The approach is based on an unsupervised extension of Density Peak clustering and a non-parametric density estimator that measures the probability density in the manifold containing the data. This allows finding automatically the number and the height of the peaks of the probability density, and the depth of the "valleys" separating them. Importantly, the density estimator provides a measure of the error, which allows distinguishing genuine density peaks from density fluctuations due to finite sampling. The approach thus provides robust and visual information about the density peaks' height, their statistical reliability, and their hierarchical organization, offering a conceptually powerful extension of the standard clustering partitions. We show that this framework is particularly useful in the analysis of complex data sets.