pwc-archive/papers-with-abstracts huggingface dataset:
Conditional Prior Networks for Optical Flow
Authors: Yanchao Yang, Stefano Soatto
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Optical Flow Estimation

Classical computation of optical flow involves generic priors (regularizers)
that capture rudimentary statistics of images, but not long-range correlations
or semantics. On the other hand, fully supervised methods learn the regularity
in the annotated data, without explicit regularization and with the risk of
overfitting. We seek to learn richer priors on the set of possible flows that
are statistically compatible with an image. Once the prior is learned in a
supervised fashion, one can easily learn the full map to infer optical flow
directly from two or more images, without any need for (additional)
supervision. We introduce a novel architecture, called Conditional Prior
Network (CPN), and show how to train it to yield a conditional prior. When used
in conjunction with a simple optical flow architecture, the CPN beats all
variational methods and all unsupervised learning-based ones using the same
data term. It performs comparably to fully supervised ones, that however are
fine-tuned to a particular dataset. Our method, on the other hand, performs
well even when transferred between datasets.
Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset
Authors: Qi Guo, Iuri Frosio, Orazio Gallo, Todd Zickler, Jan Kautz
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9

Scene motion, multiple reflections, and sensor noise introduce artifacts in
the depth reconstruction performed by time-of-flight cameras. We propose a
two-stage, deep-learning approach to address all of these sources of artifacts
simultaneously. We also introduce FLAT, a synthetic dataset of 2000 ToF
measurements that capture all of these nonidealities, and allows to simulate
different camera hardware. Using the Kinect 2 camera as a baseline, we show
improved reconstruction errors over state-of-the-art methods, on both simulated
and real data.
Integrative Multi-View Reduced-Rank Regression: Bridging Group-Sparse and Low-Rank Models
Authors: Gen Li, Xiaokang Liu, Kun Chen
Date: 2018-07-26 00:00:00
Tasks: MULTI-VIEW LEARNING, regression

Multi-view data have been routinely collected in various fields of science
and engineering. A general problem is to study the predictive association
between multivariate responses and multi-view predictor sets, all of which can
be of high dimensionality. It is likely that only a few views are relevant to
prediction, and the predictors within each relevant view contribute to the
prediction collectively rather than sparsely. We cast this new problem under
the familiar multivariate regression framework and propose an integrative
reduced-rank regression (iRRR), where each view has its own low-rank
coefficient matrix. As such, latent features are extracted from each view in a
supervised fashion. For model estimation, we develop a convex composite nuclear
norm penalization approach, which admits an efficient algorithm via alternating
direction method of multipliers. Extensions to non-Gaussian and incomplete data
are discussed. Theoretically, we derive non-asymptotic oracle bounds of iRRR
under a restricted eigenvalue condition. Our results recover oracle bounds of
several special cases of iRRR including Lasso, group Lasso and nuclear norm
penalized regression. Therefore, iRRR seamlessly bridges group-sparse and
low-rank methods and can achieve substantially faster convergence rate under
realistic settings of multi-view learning. Simulation studies and an
application in the Longitudinal Studies of Aging further showcase the efficacy
of the proposed methods.
Message-passing neural networks for high-throughput polymer screening
Authors: Peter C. St. John, Caleb Phillips, Travis W. Kemper, A. Nolan Wilson, Michael F. Crowley, Mark R. Nimlos, Ross E. Larsen
Date: 2018-07-26 00:00:00
Tasks: 3D geometry, BIG-bench Machine Learning, Vocal Bursts Intensity Prediction

Machine learning methods have shown promise in predicting molecular
properties, and given sufficient training data machine learning approaches can
enable rapid high-throughput virtual screening of large libraries of compounds.
Graph-based neural network architectures have emerged in recent years as the
most successful approach for predictions based on molecular structure, and have
consistently achieved the best performance on benchmark quantum chemical
datasets. However, these models have typically required optimized 3D structural
information for the molecule to achieve the highest accuracy. These 3D
geometries are costly to compute for high levels of theory, limiting the
applicability and practicality of machine learning methods in high-throughput
screening applications. In this study, we present a new database of candidate
molecules for organic photovoltaic applications, comprising approximately
91,000 unique chemical structures.Compared to existing datasets, this dataset
contains substantially larger molecules (up to 200 atoms) as well as
extrapolated properties for long polymer chains. We show that message-passing
neural networks trained with and without 3D structural information for these
molecules achieve similar accuracy, comparable to state-of-the-art methods on
existing benchmark datasets. These results therefore emphasize that for larger
molecules with practical applications, near-optimal prediction results can be
obtained without using optimized 3D geometry as an input. We further show that
learned molecular representations can be leveraged to reduce the training data
required to transfer predictions to a new DFT functional.
A Reinforcement Learning Approach to Target Tracking in a Camera Network
Authors: Anil Sharma, Prabhat Kumar, Saket Anand, Sanjit K. Kaul
Date: 2018-07-26 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scene Understanding

Target tracking in a camera network is an important task for surveillance and
scene understanding. The task is challenging due to disjoint views and
illumination variation in different cameras. In this direction, many
graph-based methods were proposed using appearance-based features. However, the
appearance information fades with high illumination variation in the different
camera FOVs. We, in this paper, use spatial and temporal information as the
state of the target to learn a policy that predicts the next camera given the
current state. The policy is trained using Q-learning and it does not assume
any information about the topology of the camera network. We will show that the
policy learns the camera network topology. We demonstrate the performance of
the proposed method on the NLPR MCT dataset.
A general metric for identifying adversarial images
Authors: Siddharth Krishna Kumar
Date: 2018-07-26 00:00:00

It is well known that a determined adversary can fool a neural network by
making imperceptible adversarial perturbations to an image. Recent studies have
shown that these perturbations can be detected even without information about
the neural network if the strategy taken by the adversary is known beforehand.
Unfortunately, these studies suffer from the generalization limitation -- the
detection method has to be recalibrated every time the adversary changes his
strategy. In this study, we attempt to overcome the generalization limitation
by deriving a metric which reliably identifies adversarial images even when the
approach taken by the adversary is unknown. Our metric leverages key
differences between the spectra of clean and adversarial images when an image
is treated as a matrix. Our metric is able to detect adversarial images across
different datasets and attack strategies without any additional re-calibration.
In addition, our approach provides geometric insights into several unanswered
questions about adversarial perturbations.
Selective Clustering Annotated using Modes of Projections
Authors: Evan Greene, Greg Finak, Raphael Gottardo
Date: 2018-07-26 00:00:00
Tasks: Clustering

Selective clustering annotated using modes of projections (SCAMP) is a new
clustering algorithm for data in $\mathbb{R}^p$. SCAMP is motivated from the
point of view of non-parametric mixture modeling. Rather than maximizing a
classification likelihood to determine cluster assignments, SCAMP casts
clustering as a search and selection problem. One consequence of this problem
formulation is that the number of clusters is $\textbf{not}$ a SCAMP tuning
parameter. The search phase of SCAMP consists of finding sub-collections of the
data matrix, called candidate clusters, that obey shape constraints along each
coordinate projection. An extension of the dip test of Hartigan and Hartigan
(1985) is developed to assist the search. Selection occurs by scoring each
candidate cluster with a preference function that quantifies prior belief about
the mixture composition. Clustering proceeds by selecting candidates to
maximize their total preference score. SCAMP concludes by annotating each
selected cluster with labels that describe how cluster-level statistics compare
to certain dataset-level quantities. SCAMP can be run multiple times on a
single data matrix. Comparison of annotations obtained across iterations
provides a measure of clustering uncertainty. Simulation studies and
applications to real data are considered. A C++ implementation with R interface
is $\href{https://github.com/RGLab/scamp}{available\ online}$.
Open Source Automatic Speech Recognition for German
Authors: Benjamin Milde, Arne Köhn
Date: 2018-07-26 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Diversity, speech-recognition, Speech Recognition

High quality Automatic Speech Recognition (ASR) is a prerequisite for
speech-based applications and research. While state-of-the-art ASR software is
freely available, the language dependent acoustic models are lacking for
languages other than English, due to the limited amount of freely available
training data. We train acoustic models for German with Kaldi on two datasets,
which are both distributed under a Creative Commons license. The resulting
model is freely redistributable, lowering the cost of entry for German ASR. The
models are trained on a total of 412 hours of German read speech data and we
achieve a relative word error reduction of 26% by adding data from the Spoken
Wikipedia Corpus to the previously best freely available German acoustic model
recipe and dataset. Our best model achieves a word error rate of 14.38 on the
Tuda-De test set. Due to the large amount of speakers and the diversity of
topics included in the training data, our model is robust against speaker
variation and topic shift.
Semantically Meaningful View Selection
Authors: Joris Guérin, Olivier Gibaru, Eric Nyiri, Stéphane Thiery, Byron Boots
Date: 2018-07-26 00:00:00
Tasks: Clustering, Object, Object Recognition

An understanding of the nature of objects could help robots to solve both
high-level abstract tasks and improve performance at lower-level concrete
tasks. Although deep learning has facilitated progress in image understanding,
a robot's performance in problems like object recognition often depends on the
angle from which the object is observed. Traditionally, robot sorting tasks
rely on a fixed top-down view of an object. By changing its viewing angle, a
robot can select a more semantically informative view leading to better
performance for object recognition. In this paper, we introduce the problem of
semantic view selection, which seeks to find good camera poses to gain semantic
knowledge about an observed object. We propose a conceptual formulation of the
problem, together with a solvable relaxation based on clustering. We then
present a new image dataset consisting of around 10k images representing
various views of 144 objects under different poses. Finally we use this dataset
to propose a first solution to the problem by training a neural network to
predict a "semantic score" from a top view image and camera pose. The views
predicted to have higher scores are then shown to provide better clustering
results than fixed top-down views.
Discovering physical concepts with neural networks
Authors: Raban Iten, Tony Metger, Henrik Wilming, Lidia del Rio, Renato Renner
Date: 2018-07-26 00:00:00
Tasks: Representation Learning, scientific discovery, Time Series Analysis

Despite the success of neural networks at solving concrete physics problems, their use as a general-purpose tool for scientific discovery is still in its infancy. Here, we approach this problem by modelling a neural network architecture after the human physical reasoning process, which has similarities to representation learning. This allows us to make progress towards the long-term goal of machine-assisted scientific discovery from experimental data without making prior assumptions about the system. We apply this method to toy examples and show that the network finds the physically relevant parameters, exploits conservation laws to make predictions, and can help to gain conceptual insights, e.g. Copernicus' conclusion that the solar system is heliocentric.
Variational Option Discovery Algorithms
Authors: Joshua Achiam, Harrison Edwards, Dario Amodei, Pieter Abbeel
Date: 2018-07-26 00:00:00
Tasks: Decoder, Variational Inference

We explore methods for option discovery based on variational inference and
make two algorithmic contributions. First: we highlight a tight connection
between variational option discovery methods and variational autoencoders, and
introduce Variational Autoencoding Learning of Options by Reinforcement
(VALOR), a new method derived from the connection. In VALOR, the policy encodes
contexts from a noise distribution into trajectories, and the decoder recovers
the contexts from the complete trajectories. Second: we propose a curriculum
learning approach where the number of contexts seen by the agent increases
whenever the agent's performance is strong enough (as measured by the decoder)
on the current set of contexts. We show that this simple trick stabilizes
training for VALOR and prior variational option discovery methods, allowing a
single agent to learn many more modes of behavior than it could with a fixed
context distribution. Finally, we investigate other topics related to
variational option discovery, including fundamental limitations of the general
approach and the applicability of learned options to downstream tasks.
Learning low dimensional word based linear classifiers using Data Shared Adaptive Bootstrap Aggregated Lasso with application to IMDb data
Authors: Ashutosh K. Maurya
Date: 2018-07-26 00:00:00
Tasks: Dimensionality Reduction, Ensemble Learning, Sentence, Sentiment Analysis

In this article we propose a new supervised ensemble learning method called
Data Shared Adaptive Bootstrap Aggregated (AdaBag) Lasso for capturing low
dimensional useful features for word based sentiment analysis and mining
problems. The literature on ensemble methods is very rich in both statistics
and machine learning. The algorithm is a substantial upgrade of the Data Shared
Lasso uplift algorithm. The most significant conceptual addition to the
existing literature lies in the final selection of bag of predictors through a
special bootstrap aggregation scheme. We apply the algorithm to one simulated
data and perform dimension reduction in grouped IMDb data (drama, comedy and
horror) to extract reduced set of word features for predicting sentiment
ratings of movie reviews demonstrating different aspects. We also compare the
performance of the present method with the classical Principal Components with
associated Linear Discrimination (PCA-LD) as baseline. There are few
limitations in the algorithm. Firstly, the algorithm workflow does not
incorporate online sequential data acquisition and it does not use sentence
based models which are common in ANN algorithms . Our results produce slightly
higher error rate compare to the reported state-of-the-art as a consequence.
Resource-Size matters: Improving Neural Named Entity Recognition with Optimized Large Corpora
Authors: Sajawel Ahmed, Alexander Mehler
Date: 2018-07-26 00:00:00
Tasks: Lemmatization, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Word Embeddings

This study improves the performance of neural named entity recognition by a
margin of up to 11% in F-score on the example of a low-resource language like
German, thereby outperforming existing baselines and establishing a new
state-of-the-art on each single open-source dataset. Rather than designing
deeper and wider hybrid neural architectures, we gather all available resources
and perform a detailed optimization and grammar-dependent morphological
processing consisting of lemmatization and part-of-speech tagging prior to
exposing the raw data to any training process. We test our approach in a
threefold monolingual experimental setup of a) single, b) joint, and c)
optimized training and shed light on the dependency of downstream-tasks on the
size of corpora used to compute word embeddings.
Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series
Authors: Bastian Seifert, Katharina Korn, Steffen Hartmann, Christian Uhl
Date: 2018-07-26 00:00:00
Tasks: Dimensionality Reduction, EEG, Electroencephalogram (EEG), General Classification, Time Series, Time Series Analysis

Multivariate signal processing is often based on dimensionality reduction
techniques. We propose a new method, Dynamical Component Analysis (DyCA),
leading to a classification of the underlying dynamics and - for a certain type
of dynamics - to a signal subspace representing the dynamics of the data. In
this paper the algorithm is derived leading to a generalized eigenvalue problem
of correlation matrices. The application of the DyCA on high-dimensional
chaotic signals is presented both for simulated data as well as real EEG data
of epileptic seizures.
Contributions to the development of the CRO-SL algorithm: Engineering applications problems
Authors: Carlos Camacho-Gómez
Date: 2018-07-26 00:00:00
Tasks: Scheduling

This Ph.D. thesis discusses advanced design issues of the evolutionary-based
algorithm \textit{"Coral Reef Optimization"}, in its Substrate-Layer (CRO-SL)
version, for optimization problems in Engineering Applications. The problems
that can be tackled with meta-heuristic approaches is very wide and varied, and
it is not exclusive of engineering. However we focus the Thesis on it area, one
of the most prominent in our time. One of the proposed application is battery
scheduling problem in Micro-Grids (MGs). Specifically, we consider an MG that
includes renewable distributed generation and different loads, defined by its
power profiles, and is equipped with an energy storage device (battery) to
address its programming (duration of loading / discharging and occurrence) in a
real scenario with variable electricity prices. Also, we discuss a problem of
vibration cancellation over structures of two and four floors, using Tuned Mass
Dampers (TMD's). The optimization algorithm will try to find the best solution
by obtaining three physical parameters and the TMD location. As another related
application, CRO-SL is used to design Multi-Input-Multi-Output Active Vibration
Control (MIMO-AVC) via inertial-mass actuators, for structures subjected to
human induced vibration. In this problem, we will optimize the location of each
actuator and tune control gains. Finally, we tackle the optimization of a
textile modified meander-line Inverted-F Antenna (IFA) with variable width and
spacing meander, for RFID systems. Specifically, the CRO-SL is used to obtain
an optimal antenna design, with a good bandwidth and radiation pattern, ideal
for RFID readers. Radio Frequency Identification (RFID) has become one of the
most numerous manufactured devices worldwide due to a reliable and inexpensive
means of locating people. They are used in access and money cards and product
labels and many other applications.
Reverse Attention for Salient Object Detection
Authors: Shuhan Chen, Xiuli Tan, Ben Wang, Xuelong Hu
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Prediction, Salient Object Detection

Benefit from the quick development of deep learning techniques, salient
object detection has achieved remarkable progresses recently. However, there
still exists following two major challenges that hinder its application in
embedded devices, low resolution output and heavy model weight. To this end,
this paper presents an accurate yet compact deep network for efficient salient
object detection. More specifically, given a coarse saliency prediction in the
deepest layer, we first employ residual learning to learn side-output residual
features for saliency refinement, which can be achieved with very limited
convolutional parameters while keep accuracy. Secondly, we further propose
reverse attention to guide such side-output residual learning in a top-down
manner. By erasing the current predicted salient regions from side-output
features, the network can eventually explore the missing object parts and
details which results in high resolution and accuracy. Experiments on six
benchmark datasets demonstrate that the proposed approach compares favorably
against state-of-the-art methods, and with advantages in terms of simplicity,
efficiency (45 FPS) and model size (81 MB).
HiDDeN: Hiding Data With Deep Networks
Authors: Jiren Zhu, Russell Kaplan, Justin Johnson, Li Fei-Fei
Date: 2018-07-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Decoder

Recent work has shown that deep neural networks are highly sensitive to tiny
perturbations of input images, giving rise to adversarial examples. Though this
property is usually considered a weakness of learned models, we explore whether
it can be beneficial. We find that neural networks can learn to use invisible
perturbations to encode a rich amount of useful information. In fact, one can
exploit this capability for the task of data hiding. We jointly train encoder
and decoder networks, where given an input message and cover image, the encoder
produces a visually indistinguishable encoded image, from which the decoder can
recover the original message. We show that these encodings are competitive with
existing data hiding algorithms, and further that they can be made robust to
noise: our models learn to reconstruct hidden information in an encoded image
despite the presence of Gaussian blurring, pixel-wise dropout, cropping, and
JPEG compression. Even though JPEG is non-differentiable, we show that a robust
model can be trained using differentiable approximations. Finally, we
demonstrate that adversarial training improves the visual quality of encoded
images.
Multi-Agent Generative Adversarial Imitation Learning
Authors: Jiaming Song, Hongyu Ren, Dorsa Sadigh, Stefano Ermon
Date: 2018-07-26 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Imitation learning algorithms can be used to learn a policy from expert
demonstrations without access to a reward signal. However, most existing
approaches are not applicable in multi-agent settings due to the existence of
multiple (Nash) equilibria and non-stationary environments. We propose a new
framework for multi-agent imitation learning for general Markov games, where we
build upon a generalized notion of inverse reinforcement learning. We further
introduce a practical multi-agent actor-critic algorithm with good empirical
performance. Our method can be used to imitate complex behaviors in
high-dimensional environments with multiple cooperative or competing agents.