pwc-archive/papers-with-abstracts huggingface dataset:
Real-Time Capable Micro-Doppler Signature Decomposition of Walking Human Limbs
Authors: Sherif Abdulatif, Fady Aziz, Bernhard Kleiner, Urs Schneider
Date: 2017-11-25 00:00:00

Unique micro-Doppler signature ($\boldsymbol{\mu}$-D) of a human body motion
can be analyzed as the superposition of different body parts
$\boldsymbol{\mu}$-D signatures. Extraction of human limbs $\boldsymbol{\mu}$-D
signatures in real-time can be used to detect, classify and track human motion
especially for safety application. In this paper, two methods are combined to
simulate $\boldsymbol{\mu}$-D signatures of a walking human. Furthermore, a
novel limbs $\mu$-D signature time independent decomposition feasibility study
is presented based on features as $\mu$-D signatures and range profiles also
known as micro-Range ($\mu$-R). Walking human body parts can be divided into
four classes (base, arms, legs, feet) and a decision tree classifier is used.
Validation is done and the classifier is able to decompose $\mu$-D signatures
of limbs from a walking human signature on real-time basis.
Learning Abduction under Partial Observability
Authors: Brendan Juba, Zongyi Li, Evan Miller
Date: 2017-11-13 00:00:00
Tasks: valid

Juba recently proposed a formulation of learning abductive reasoning from
examples, in which both the relative plausibility of various explanations, as
well as which explanations are valid, are learned directly from data. The main
shortcoming of this formulation of the task is that it assumes access to
full-information (i.e., fully specified) examples; relatedly, it offers no role
for declarative background knowledge, as such knowledge is rendered redundant
in the abduction task by complete information. In this work, we extend the
formulation to utilize such partially specified examples, along with
declarative background knowledge about the missing data. We show that it is
possible to use implicitly learned rules together with the explicitly given
declarative knowledge to support hypotheses in the course of abduction. We
observe that when a small explanation exists, it is possible to obtain a
much-improved guarantee in the challenging exception-tolerant setting. Such
small, human-understandable explanations are of particular interest for
potential applications of the task.
Disentangling Dynamics and Content for Control and Planning
Authors: Ershad Banijamali, Ahmad Khajenezhad, Ali Ghodsi, Mohammad Ghavamzadeh
Date: 2017-11-24 00:00:00

In this paper, We study the problem of learning a controllable representation
for high-dimensional observations of dynamical systems. Specifically, we
consider a situation where there are multiple sets of observations of dynamical
systems with identical underlying dynamics. Only one of these sets has
information about the effect of actions on the observation and the rest are
just some random observations of the system. Our goal is to utilize the
information in that one set and find a representation for the other sets that
can be used for planning and ling-term prediction.
STORE: Sparse Tensor Response Regression and Neuroimaging Analysis
Authors: Will Wei Sun, Lexin Li
Date: 2016-09-15 00:00:00
Tasks: Computational Efficiency, parameter estimation, regression

Motivated by applications in neuroimaging analysis, we propose a new
regression model, Sparse TensOr REsponse regression (STORE), with a tensor
response and a vector predictor. STORE embeds two key sparse structures:
element-wise sparsity and low-rankness. It can handle both a non-symmetric and
a symmetric tensor response, and thus is applicable to both structural and
functional neuroimaging data. We formulate the parameter estimation as a
non-convex optimization problem, and develop an efficient alternating updating
algorithm. We establish a non-asymptotic estimation error bound for the actual
estimator obtained from the proposed algorithm. This error bound reveals an
interesting interaction between the computational efficiency and the
statistical rate of convergence. When the distribution of the error tensor is
Gaussian, we further obtain a fast estimation error rate which allows the
tensor dimension to grow exponentially with the sample size. We illustrate the
efficacy of our model through intensive simulations and an analysis of the
Autism spectrum disorder neuroimaging data.
JADE: Joint Autoencoders for Dis-Entanglement
Authors: Ershad Banijamali, Amir-Hossein Karimi, Alexander Wong, Ali Ghodsi
Date: 2017-11-24 00:00:00
Tasks: Disentanglement, General Classification

The problem of feature disentanglement has been explored in the literature,
for the purpose of image and video processing and text analysis.
State-of-the-art methods for disentangling feature representations rely on the
presence of many labeled samples. In this work, we present a novel method for
disentangling factors of variation in data-scarce regimes. Specifically, we
explore the application of feature disentangling for the problem of supervised
classification in a setting where few labeled samples exist, and there are no
unlabeled samples for use in unsupervised training. Instead, a similar datasets
exists which shares at least one direction of variation with the
sample-constrained datasets. We train our model end-to-end using the framework
of variational autoencoders and are able to experimentally demonstrate that
using an auxiliary dataset with similar variation factors contribute positively
to classification performance, yielding competitive results with the
state-of-the-art in unsupervised learning.
An Exploration of Word Embedding Initialization in Deep-Learning Tasks
Authors: Tom Kocmi, Ondřej Bojar
Date: 2017-11-24 00:00:00
Proceeding: WS 2017 12
Tasks: Deep Learning, Word Embeddings

Word embeddings are the interface between the world of discrete units of text
processing and the continuous, differentiable world of neural networks. In this
work, we examine various random and pretrained initialization methods for
embeddings used in deep networks and their effect on the performance on four
NLP tasks with both recurrent and convolutional architectures. We confirm that
pretrained embeddings are a little better than random initialization,
especially considering the speed of learning. On the other hand, we do not see
any significant difference between various methods of random initialization, as
long as the variance is kept reasonably low. High-variance initialization
prevents the network to use the space of embeddings and forces it to use other
free parameters to accomplish the task. We support this hypothesis by observing
the performance in learning lexical relations and by the fact that the network
can learn to perform reasonably in its task even with fixed random embeddings.
Quantifying the Effects of Enforcing Disentanglement on Variational Autoencoders
Authors: Momchil Peychev, Petar Veličković, Pietro Liò
Date: 2017-11-24 00:00:00
Tasks: Disentanglement

The notion of disentangled autoencoders was proposed as an extension to the
variational autoencoder by introducing a disentanglement parameter $\beta$,
controlling the learning pressure put on the possible underlying latent
representations. For certain values of $\beta$ this kind of autoencoders is
capable of encoding independent input generative factors in separate elements
of the code, leading to a more interpretable and predictable model behaviour.
In this paper we quantify the effects of the parameter $\beta$ on the model
performance and disentanglement. After training multiple models with the same
value of $\beta$, we establish the existence of consistent variance in one of
the disentanglement measures, proposed in literature. The negative consequences
of the disentanglement to the autoencoder's discriminative ability are also
asserted while varying the amount of examples available during training.
Persistent homology machine learning for fingerprint classification
Authors: Noah Giansiracusa, Robert Giansiracusa, Chul Moon
Date: 2017-11-24 00:00:00
Tasks: BIG-bench Machine Learning, Classification, feature selection, General Classification, Topological Data Analysis

The fingerprint classification problem is to sort fingerprints into
pre-determined groups, such as arch, loop, and whorl. It was asserted in the
literature that minutiae points, which are commonly used for fingerprint
matching, are not useful for classification. We show that, to the contrary,
near state-of-the-art classification accuracy rates can be achieved when
applying topological data analysis (TDA) to 3-dimensional point clouds of
oriented minutiae points. We also apply TDA to fingerprint ink-roll images,
which yields a lower accuracy rate but still shows promise, particularly since
the only preprocessing is cropping; moreover, combining the two approaches
outperforms each one individually. These methods use supervised learning
applied to persistent homology and allow us to explore feature selection on
barcodes, an important topic at the interface between TDA and machine learning.
We test our classification algorithms on the NIST fingerprint database SD-27.
Warped-Linear Models for Time Series Classification
Authors: Brijnesh J. Jain
Date: 2017-11-24 00:00:00
Tasks: Classification, Dynamic Time Warping, General Classification, Quantization, Time Series, Time Series Analysis, Time Series Averaging, Time Series Classification

This article proposes and studies warped-linear models for time series
classification. The proposed models are time-warp invariant analogues of linear
models. Their construction is in line with time series averaging and extensions
of k-means and learning vector quantization to dynamic time warping (DTW)
spaces. The main theoretical result is that warped-linear models correspond to
polyhedral classifiers in Euclidean spaces. This result simplifies the analysis
of time-warp invariant models by reducing to max-linear functions. We exploit
this relationship and derive solutions to the label-dependency problem and the
problem of learning warped-linear models. Empirical results on time series
classification suggest that warped-linear functions better trade solution
quality against computation time than nearest-neighbor and prototype-based
methods.
Convolutional Image Captioning
Authors: Jyoti Aneja, Aditya Deshpande, Alexander Schwing
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Captioning, Text Generation, Translation

Image captioning is an important but challenging task, applicable to virtual
assistants, editing tools, image indexing, and support of the disabled. Its
challenges are due to the variability and ambiguity of possible image
descriptions. In recent years significant progress has been made in image
captioning, using Recurrent Neural Networks powered by long-short-term-memory
(LSTM) units. Despite mitigating the vanishing gradient problem, and despite
their compelling ability to memorize dependencies, LSTM units are complex and
inherently sequential across time. To address this issue, recent work has shown
benefits of convolutional networks for machine translation and conditional
image generation. Inspired by their success, in this paper, we develop a
convolutional image captioning technique. We demonstrate its efficacy on the
challenging MSCOCO dataset and demonstrate performance on par with the
baseline, while having a faster training time per number of parameters. We also
perform a detailed analysis, providing compelling reasons in favor of
convolutional language generation approaches.
Cascade Attribute Learning Network
Authors: Zhuo Xu, Haonan Chang, Masayoshi Tomizuka
Date: 2017-11-24 00:00:00
Tasks: Attribute, Position, Reinforcement Learning, Reinforcement Learning (RL)

We propose the cascade attribute learning network (CALNet), which can learn
attributes in a control task separately and assemble them together. Our
contribution is twofold: first we propose attribute learning in reinforcement
learning (RL). Attributes used to be modeled using constraint functions or
terms in the objective function, making it hard to transfer. Attribute
learning, on the other hand, models these task properties as modules in the
policy network. We also propose using novel cascading compensative networks in
the CALNet to learn and assemble attributes. Using the CALNet, one can zero
shoot an unseen task by separately learning all its attributes, and assembling
the attribute modules. We have validated the capacity of our model on a wide
variety of control problems with attributes in time, position, velocity and
acceleration phases.
Sparse Inverse Covariance Estimation for Chordal Structures
Authors: Salar Fattahi, Richard Y. Zhang, Somayeh Sojoudi
Date: 2017-11-24 00:00:00
Tasks: Form, Matrix Completion

In this paper, we consider the Graphical Lasso (GL), a popular optimization
problem for learning the sparse representations of high-dimensional datasets,
which is well-known to be computationally expensive for large-scale problems.
Recently, we have shown that the sparsity pattern of the optimal solution of GL
is equivalent to the one obtained from simply thresholding the sample
covariance matrix, for sparse graphs under different conditions. We have also
derived a closed-form solution that is optimal when the thresholded sample
covariance matrix has an acyclic structure. As a major generalization of the
previous result, in this paper we derive a closed-form solution for the GL for
graphs with chordal structures. We show that the GL and thresholding
equivalence conditions can significantly be simplified and are expected to hold
for high-dimensional problems if the thresholded sample covariance matrix has a
chordal structure. We then show that the GL and thresholding equivalence is
enough to reduce the GL to a maximum determinant matrix completion problem and
drive a recursive closed-form solution for the GL when the thresholded sample
covariance matrix has a chordal structure. For large-scale problems with up to
450 million variables, the proposed method can solve the GL problem in less
than 2 minutes, while the state-of-the-art methods converge in more than 2
hours.
Deep Reinforcement Learning that Matters
Authors: Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger
Date: 2017-09-19 00:00:00
Tasks: Atari Games, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, significant progress has been made in solving challenging
problems across various domains using deep reinforcement learning (RL).
Reproducing existing work and accurately judging the improvements offered by
novel methods is vital to sustaining this progress. Unfortunately, reproducing
results for state-of-the-art deep RL methods is seldom straightforward. In
particular, non-determinism in standard benchmark environments, combined with
variance intrinsic to the methods, can make reported results tough to
interpret. Without significance metrics and tighter standardization of
experimental reporting, it is difficult to determine whether improvements over
the prior state-of-the-art are meaningful. In this paper, we investigate
challenges posed by reproducibility, proper experimental techniques, and
reporting procedures. We illustrate the variability in reported metrics and
results when comparing against common baselines and suggest guidelines to make
future results in deep RL more reproducible. We aim to spur discussion about
how to ensure continued progress in the field by minimizing wasted effort
stemming from results that are non-reproducible and easily misinterpreted.
Geometric robustness of deep networks: analysis and improvement
Authors: Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6

Deep convolutional neural networks have been shown to be vulnerable to
arbitrary geometric transformations. However, there is no systematic method to
measure the invariance properties of deep networks to such transformations. We
propose ManiFool as a simple yet scalable algorithm to measure the invariance
of deep networks. In particular, our algorithm measures the robustness of deep
networks to geometric transformations in a worst-case regime as they can be
problematic for sensitive applications. Our extensive experimental results show
that ManiFool can be used to measure the invariance of fairly complex networks
on high dimensional datasets and these values can be used for analyzing the
reasons for it. Furthermore, we build on Manifool to propose a new adversarial
training scheme and we show its effectiveness on improving the invariance
properties of deep neural networks.
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning
Authors: Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon, David Meger, Joelle Pineau, Doina Precup
Date: 2017-09-20 00:00:00
Tasks: continuous-control, Continuous Control, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Reinforcement learning has shown promise in learning policies that can solve
complex problems. However, manually specifying a good reward function can be
difficult, especially for intricate tasks. Inverse reinforcement learning
offers a useful paradigm to learn the underlying reward function directly from
expert demonstrations. Yet in reality, the corpus of demonstrations may contain
trajectories arising from a diverse set of underlying reward functions rather
than a single one. Thus, in inverse reinforcement learning, it is useful to
consider such a decomposition. The options framework in reinforcement learning
is specifically designed to decompose policies in a similar light. We therefore
extend the options framework and propose a method to simultaneously recover
reward options in addition to policy options. We leverage adversarial methods
to learn joint reward-policy options using only observed expert states. We show
that this approach works well in both simple and complex continuous control
tasks and shows significant performance increases in one-shot transfer
learning.
Cross-Domain Self-supervised Multi-task Feature Learning using Synthetic Imagery
Authors: Zhongzheng Ren, Yong Jae Lee
Date: 2017-11-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Multi-Task Learning, Transfer Learning

In human learning, it is common to use multiple sources of information
jointly. However, most existing feature learning approaches learn from only a
single task. In this paper, we propose a novel multi-task deep network to learn
generalizable high-level visual representations. Since multi-task learning
requires annotations for multiple properties of the same training instance, we
look to synthetic images to train our network. To overcome the domain
difference between real and synthetic data, we employ an unsupervised feature
space domain adaptation method based on adversarial learning. Given an input
synthetic RGB image, our network simultaneously predicts its surface normal,
depth, and instance contour, while also minimizing the feature space domain
differences between real and synthetic data. Through extensive experiments, we
demonstrate that our network learns more transferable representations compared
to single-task baselines. Our learned representation produces state-of-the-art
transfer learning results on PASCAL VOC 2007 classification and 2012 detection.
Video Enhancement with Task-Oriented Flow
Authors: Tianfan Xue, Baian Chen, Jiajun Wu, Donglai Wei, William T. Freeman
Date: 2017-11-24 00:00:00
Tasks: Denoising, Motion Estimation, Optical Flow Estimation, Super-Resolution, Video Denoising, Video Enhancement, Video Frame Interpolation, Video Super-Resolution

Many video enhancement algorithms rely on optical flow to register frames in a video sequence. Precise flow estimation is however intractable; and optical flow itself is often a sub-optimal representation for particular video processing tasks. In this paper, we propose task-oriented flow (TOFlow), a motion representation learned in a self-supervised, task-specific manner. We design a neural network with a trainable motion estimation component and a video processing component, and train them jointly to learn the task-oriented flow. For evaluation, we build Vimeo-90K, a large-scale, high-quality video dataset for low-level video processing. TOFlow outperforms traditional optical flow on standard benchmarks as well as our Vimeo-90K dataset in three video processing tasks: frame interpolation, video denoising/deblocking, and video super-resolution.
Efficient and Invariant Convolutional Neural Networks for Dense Prediction
Authors: Hongyang Gao, Shuiwang Ji
Date: 2017-11-24 00:00:00
Tasks: Image Segmentation, Prediction, Semantic Segmentation

Convolutional neural networks have shown great success on feature extraction
from raw input data such as images. Although convolutional neural networks are
invariant to translations on the inputs, they are not invariant to other
transformations, including rotation and flip. Recent attempts have been made to
incorporate more invariance in image recognition applications, but they are not
applicable to dense prediction tasks, such as image segmentation. In this
paper, we propose a set of methods based on kernel rotation and flip to enable
rotation and flip invariance in convolutional neural networks. The kernel
rotation can be achieved on kernels of 3 $\times$ 3, while kernel flip can be
applied on kernels of any size. By rotating in eight or four angles, the
convolutional layers could produce the corresponding number of feature maps
based on eight or four different kernels. By using flip, the convolution layer
can produce three feature maps. By combining produced feature maps using
maxout, the resource requirement could be significantly reduced while still
retain the invariance properties. Experimental results demonstrate that the
proposed methods can achieve various invariance at reasonable resource
requirements in terms of both memory and time.
Distance to Center of Mass Encoding for Instance Segmentation
Authors: Thomio Watanabe, Denis Wolf
Date: 2017-11-24 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Object Localization, Segmentation, Semantic Segmentation

The instance segmentation can be considered an extension of the object
detection problem where bounding boxes are replaced by object contours.
Strictly speaking the problem requires to identify each pixel instance and
class independently of the artifice used for this mean. The advantage of
instance segmentation over the usual object detection lies in the precise
delineation of objects improving object localization. Additionally, object
contours allow the evaluation of partial occlusion with basic image processing
algorithms. This work approaches the instance segmentation problem as an
annotation problem and presents a novel technique to encode and decode ground
truth annotations. We propose a mathematical representation of instances that
any deep semantic segmentation model can learn and generalize. Each individual
instance is represented by a center of mass and a field of vectors pointing to
it. This encoding technique has been denominated Distance to Center of Mass
Encoding (DCME).