pwc-archive/papers-with-abstracts huggingface dataset:
Maximum Margin Interval Trees
Authors: Alexandre Drouin, Toby Dylan Hocking, François Laviolette
Date: 2017-10-11 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: regression

Learning a regression function using censored or interval-valued output data
is an important problem in fields such as genomics and medicine. The goal is to
learn a real-valued prediction function, and the training output labels
indicate an interval of possible values. Whereas most existing algorithms for
this task are linear models, in this paper we investigate learning nonlinear
tree models. We propose to learn a tree by minimizing a margin-based
discriminative objective function, and we provide a dynamic programming
algorithm for computing the optimal solution in log-linear time. We show
empirically that this algorithm achieves state-of-the-art speed and prediction
accuracy in a benchmark of several data sets.
Application of Deep Learning in Neuroradiology: Automated Detection of Basal Ganglia Hemorrhage using 2D-Convolutional Neural Networks
Authors: Vishal Desai, Adam E. Flanders, Paras Lakhani
Date: 2017-10-10 00:00:00
Tasks: Data Augmentation, General Classification, image-classification, Image Classification

Background: Deep learning techniques have achieved high accuracy in image
classification tasks, and there is interest in applicability to neuroimaging
critical findings. This study evaluates the efficacy of 2D deep convolutional
neural networks (DCNNs) for detecting basal ganglia (BG) hemorrhage on
noncontrast head CT.
  Materials and Methods: 170 unique de-identified HIPAA-compliant noncontrast
head CTs were obtained, those with and without BG hemorrhage. 110 cases were
held-out for test, and 60 were split into training (45) and validation (15),
consisting of 20 right, 20 left, and 20 no BG hemorrhage. Data augmentation was
performed to increase size and variation of the training dataset by 48-fold.
Two DCNNs were used to classify the images-AlexNet and GoogLeNet-using
untrained networks and those pre-trained on ImageNet. Area under the curves
(AUC) for the receiver-operator characteristic (ROC) curves were calculated,
using the DeLong method for statistical comparison of ROCs.
  Results: The best performing model was the pre-trained augmented GoogLeNet,
which had an AUC of 1.00 in classification of hemorrhage. Preprocessing
augmentation increased accuracy for all networks (p<0.001), and pretrained
networks outperformed untrained ones (p<0.001) for the unaugmented models. The
best performing GoogLeNet model (AUC 1.00) outperformed the best performing
AlexNet model (AUC 0.95)(p=0.01).
  Conclusion: For this dataset, the best performing DCNN identified BG
hemorrhage on noncontrast head CT with an AUC of 1.00. Pretrained networks and
data augmentation increased classifier accuracy. Future prospective research
would be important to determine if the accuracy can be maintained on a larger
cohort of patients and for very small hemorrhages.
Detection and Analysis of Human Emotions through Voice and Speech Pattern Processing
Authors: Poorna Banerjee Dasgupta
Date: 2017-10-27 00:00:00

The ability to modulate vocal sounds and generate speech is one of the
features which set humans apart from other living beings. The human voice can
be characterized by several attributes such as pitch, timbre, loudness, and
vocal tone. It has often been observed that humans express their emotions by
varying different vocal attributes during speech generation. Hence, deduction
of human emotions through voice and speech analysis has a practical
plausibility and could potentially be beneficial for improving human
conversational and persuasion skills. This paper presents an algorithmic
approach for detection and analysis of human emotions with the help of voice
and speech processing. The proposed approach has been developed with the
objective of incorporation with futuristic artificial intelligence systems for
improving human-computer interactions.
Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition
Authors: Fei Tao, Gang Liu
Date: 2017-10-27 00:00:00
Tasks: Emotion Classification, Emotion Recognition, General Classification

Long short-term memory (LSTM) is normally used in recurrent neural network
(RNN) as basic recurrent unit. However,conventional LSTM assumes that the state
at current time step depends on previous time step. This assumption constraints
the time dependency modeling capability. In this study, we propose a new
variation of LSTM, advanced LSTM (A-LSTM), for better temporal context
modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The
A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based
weighted pooling RNN can also complement the state-of-the-art emotion
classification framework. This shows the advantage of A-LSTM.
Dual Path Networks for Multi-Person Human Pose Estimation
Authors: Guanghan Ning, Zhihai He
Date: 2017-10-27 00:00:00
Tasks: Pose Estimation, regression

The task of multi-person human pose estimation in natural scenes is quite
challenging. Existing methods include both top-down and bottom-up approaches.
The main advantage of bottom-up methods is its excellent tradeoff between
estimation accuracy and computational cost. We follow this path and aim to
design smaller, faster, and more accurate neural networks for the regression of
keypoints and limb association vectors. These two regression tasks are
naturally dependent on each other. In this work, we propose a dual-path network
specially designed for multi-person human pose estimation, and compare our
performance with the openpose network in aspects of model size, forward speed,
and estimation accuracy.
Enhanced Biologically Inspired Model for Image Recognition Based on a Novel Patch Selection Method with Moment
Authors: Yan-Feng Lu, Li-Hao Jia, Hong Qaio, Yi Li
Date: 2017-10-27 00:00:00
Tasks: Object Categorization

Biologically inspired model (BIM) for image recognition is a robust
computational architecture, which has attracted widespread attention. BIM can
be described as a four-layer structure based on the mechanisms of the visual
cortex. Although the performance of BIM for image recognition is robust, it
takes the randomly selected ways for the patch selection, which is sightless,
and results in heavy computing burden. To address this issue, we propose a
novel patch selection method with oriented Gaussian-Hermite moment (PSGHM), and
we enhanced the BIM based on the proposed PSGHM, named as PBIM. In contrast to
the conventional BIM which adopts the random method to select patches within
the feature representation layers processed by multi-scale Gabor filter banks,
the proposed PBIM takes the PSGHM way to extract a small number of
representation features while offering promising distinctiveness. To show the
effectiveness of the proposed PBIM, experimental studies on object
categorization are conducted on the CalTech05, TU Darmstadt (TUD), and GRAZ01
databases. Experimental results demonstrate that the performance of PBIM is a
significant improvement on that of the conventional BIM.
Deep Haptic Model Predictive Control for Robot-Assisted Dressing
Authors: Zackory Erickson, Henry M. Clever, Greg Turk, C. Karen Liu, Charles C. Kemp
Date: 2017-09-27 00:00:00
Tasks: Common Sense Reasoning, Model Predictive Control

Robot-assisted dressing offers an opportunity to benefit the lives of many people with disabilities, such as some older adults. However, robots currently lack common sense about the physical implications of their actions on people. The physical implications of dressing are complicated by non-rigid garments, which can result in a robot indirectly applying high forces to a person's body. We present a deep recurrent model that, when given a proposed action by the robot, predicts the forces a garment will apply to a person's body. We also show that a robot can provide better dressing assistance by using this model with model predictive control. The predictions made by our model only use haptic and kinematic observations from the robot's end effector, which are readily attainable. Collecting training data from real world physical human-robot interaction can be time consuming, costly, and put people at risk. Instead, we train our predictive model using data collected in an entirely self-supervised fashion from a physics-based simulation. We evaluated our approach with a PR2 robot that attempted to pull a hospital gown onto the arms of 10 human participants. With a 0.2s prediction horizon, our controller succeeded at high rates and lowered applied force while navigating the garment around a persons fist and elbow without getting caught. Shorter prediction horizons resulted in significantly reduced performance with the sleeve catching on the participants' fists and elbows, demonstrating the value of our model's predictions. These behaviors of mitigating catches emerged from our deep predictive model and the controller objective function, which primarily penalizes high forces.
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data
Authors: Alon Brutzkus, Amir Globerson, Eran Malach, Shai Shalev-Shwartz
Date: 2017-10-27 00:00:00
Proceeding: ICLR 2018 1
Tasks: Generalization Bounds

Neural networks exhibit good generalization behavior in the
over-parameterized regime, where the number of network parameters exceeds the
number of observations. Nonetheless, current generalization bounds for neural
networks fail to explain this phenomenon. In an attempt to bridge this gap, we
study the problem of learning a two-layer over-parameterized neural network,
when the data is generated by a linearly separable function. In the case where
the network has Leaky ReLU activations, we provide both optimization and
generalization guarantees for over-parameterized networks. Specifically, we
prove convergence rates of SGD to a global minimum and provide generalization
guarantees for this global minimum that are independent of the network size.
Therefore, our result clearly shows that the use of SGD for optimization both
finds a global minimum, and avoids overfitting despite the high capacity of the
model. This is the first theoretical demonstration that SGD can avoid
overfitting, when learning over-specified neural network classifiers.
Towards a new paradigm for assistive technology at home: research challenges, design issues and performance assessment
Authors: Luca Buoncompagni, Barbara Bruno, Antonella Giuni, Fulvio Mastrogiovanni, Renato Zaccaria
Date: 2017-10-27 00:00:00

Providing elderly and people with special needs, including those suffering
from physical disabilities and chronic diseases, with the possibility of
retaining their independence at best is one of the most important challenges
our society is expected to face. Assistance models based on the home care
paradigm are being adopted rapidly in almost all industrialized and emerging
countries. Such paradigms hypothesize that it is necessary to ensure that the
so-called Activities of Daily Living are correctly and regularly performed by
the assisted person to increase the perception of an improved quality of life.
This chapter describes the computational inference engine at the core of
Arianna, a system able to understand whether an assisted person performs a
given set of ADL and to motivate him/her in performing them through a
speech-mediated motivational dialogue, using a set of nearables to be installed
in an apartment, plus a wearable to be worn or fit in garments.
Generalized Optimal Matching Methods for Causal Inference
Authors: Nathan Kallus
Date: 2016-12-26 00:00:00
Tasks: Causal Inference

We develop an encompassing framework for matching, covariate balancing, and
doubly-robust methods for causal inference from observational data called
generalized optimal matching (GOM). The framework is given by generalizing a
new functional-analytical formulation of optimal matching, giving rise to the
class of GOM methods, for which we provide a single unified theory to analyze
tractability, consistency, and efficiency. Many commonly used existing methods
are included in GOM and, using their GOM interpretation, can be extended to
optimally and automatically trade off balance for variance and outperform their
standard counterparts. As a subclass, GOM gives rise to kernel optimal matching
(KOM), which, as supported by new theoretical and empirical results, is notable
for combining many of the positive properties of other methods in one. KOM,
which is solved as a linearly-constrained convex-quadratic optimization
problem, inherits both the interpretability and model-free consistency of
matching but can also achieve the $\sqrt{n}$-consistency of well-specified
regression and the efficiency and robustness of doubly robust methods. In
settings of limited overlap, KOM enables a very transparent method for interval
estimation for partial identification and robust coverage. We demonstrate these
benefits in examples with both synthetic and real data
Finding Robust Solutions to Stable Marriage
Authors: Begum Genc, Mohamed Siala, Barry O'Sullivan, Gilles Simonin
Date: 2017-05-24 00:00:00

We study the notion of robustness in stable matching problems. We first
define robustness by introducing (a,b)-supermatches. An $(a,b)$-supermatch is a
stable matching in which if $a$ pairs break up it is possible to find another
stable matching by changing the partners of those $a$ pairs and at most $b$
other pairs. In this context, we define the most robust stable matching as a
$(1,b)$-supermatch where b is minimum. We show that checking whether a given
stable matching is a $(1,b)$-supermatch can be done in polynomial time. Next,
we use this procedure to design a constraint programming model, a local search
approach, and a genetic algorithm to find the most robust stable matching. Our
empirical evaluation on large instances show that local search outperforms the
other approaches.
Deep Gaussian Covariance Network
Authors: Kevin Cremanns, Dirk Roos
Date: 2017-10-17 00:00:00
Tasks: Gaussian Processes, Mixture-of-Experts, Time Series Analysis

The correlation length-scale next to the noise variance are the most used
hyperparameters for the Gaussian processes. Typically, stationary covariance
functions are used, which are only dependent on the distances between input
points and thus invariant to the translations in the input space. The
optimization of the hyperparameters is commonly done by maximizing the log
marginal likelihood. This works quite well, if the distances are uniform
distributed. In the case of a locally adapted or even sparse input space, the
prediction of a test point can be worse dependent of its position. A possible
solution to this, is the usage of a non-stationary covariance function, where
the hyperparameters are calculated by a deep neural network. So that the
correlation length scales and possibly the noise variance are dependent on the
test point. Furthermore, different types of covariance functions are trained
simultaneously, so that the Gaussian process prediction is an additive overlay
of different covariance matrices. The right covariance functions combination
and its hyperparameters are learned by the deep neural network. Additional, the
Gaussian process will be able to be trained by batches or online and so it can
handle arbitrarily large data sets. We call this framework Deep Gaussian
Covariance Network (DGCP). There are also further extensions to this framework
possible, for example sequentially dependent problems like time series or the
local mixture of experts. The basic framework and some extension possibilities
will be presented in this work. Moreover, a comparison to some recent state of
the art surrogate model methods will be performed, also for a time dependent
problem.
On the Complexity of Robust Stable Marriage
Authors: Begum Genc, Mohamed Siala, Gilles Simonin, Barry O'Sullivan
Date: 2017-09-18 00:00:00

Robust Stable Marriage (RSM) is a variant of the classical Stable Marriage problem, where the robustness of a given stable matching is measured by the number of modifications required for repairing it in case an unforeseen event occurs. We focus on the complexity of finding an (a,b)-supermatch. An (a,b)-supermatch is defined as a stable matching in which if any 'a' (non-fixed) men/women break up it is possible to find another stable matching by changing the partners of those 'a' men/women and also the partners of at most 'b' other couples. In order to show deciding if there exists an (a,b)-supermatch is NP-Complete, we first introduce a SAT formulation that is NP-Complete by using Schaefer's Dichotomy Theorem. Then, we show the equivalence between the SAT formulation and finding a (1,1)-supermatch on a specific family of instances.
Inverse Reinforcement Learning Under Noisy Observations
Authors: Shervin Shahryari, Prashant Doshi
Date: 2017-10-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the problem of performing inverse reinforcement learning when the
trajectory of the expert is not perfectly observed by the learner. Instead, a
noisy continuous-time observation of the trajectory is provided to the learner.
This problem exhibits wide-ranging applications and the specific application we
consider here is the scenario in which the learner seeks to penetrate a
perimeter patrolled by a robot. The learner's field of view is limited due to
which it cannot observe the patroller's complete trajectory. Instead, we allow
the learner to listen to the expert's movement sound, which it can also use to
estimate the expert's state and action using an observation model. We treat the
expert's state and action as hidden data and present an algorithm based on
expectation maximization and maximum entropy principle to solve the non-linear,
non-convex problem. Related work considers discrete-time observations and an
observation model that does not include actions. In contrast, our technique
takes expectations over both state and action of the expert, enabling learning
even in the presence of extreme noise and broader applications.
On the Taut String Interpretation of the One-dimensional Rudin-Osher-Fatemi Model: A New Proof, a Fundamental Estimate and Some Applications
Authors: Niels Chr. Overgaard
Date: 2017-10-27 00:00:00
Tasks: Denoising, regression

A new proof of the equivalence of the Taut String Algorithm and the
one-dimensional Rudin-Osher-Fatemi model is presented. Based on duality and the
projection theorem in Hilbert space, the proof is strictly elementary.
Existence and uniqueness of solutions to both denoising models follow as
by-products. The standard convergence properties of the denoised signal, as the
regularizing parameter tends to zero, are recalled and efficient proofs
provided. Moreover, a new and fundamental bound on the denoised signal is
derived. This bound implies, among other things, the strong convergence (in the
space of functions of bounded variation) of the denoised signal to the insignal
as the regularization parameter vanishes. The methods developed in the paper
can be modified to cover other interesting applications such as isotonic
regression.
Set2Model Networks: Learning Discriminatively To Learn Generative Models
Authors: A. Vakhitov, A. Kuzmin, V. Lempitsky
Date: 2016-12-22 00:00:00
Tasks: Image Retrieval, Meta-Learning, Retrieval

We present a new "learning-to-learn"-type approach that enables rapid
learning of concepts from small-to-medium sized training sets and is primarily
designed for web-initialized image retrieval. At the core of our approach is a
deep architecture (a Set2Model network) that maps sets of examples to simple
generative probabilistic models such as Gaussians or mixtures of Gaussians in
the space of high-dimensional descriptors. The parameters of the embedding into
the descriptor space are trained in the end-to-end fashion in the meta-learning
stage using a set of training learning problems. The main technical novelty of
our approach is the derivation of the backprop process through the mixture
model fitting, which makes the likelihood of the resulting models
differentiable with respect to the positions of the input descriptors.
  While the meta-learning process for a Set2Model network is discriminative, a
trained Set2Model network performs generative learning of generative models in
the descriptor space, which facilitates learning in the cases when no negative
examples are available, and whenever the concept being learned is polysemous or
represented by noisy training sets. Among other experiments, we demonstrate
that these properties allow Set2Model networks to pick visual concepts from the
raw outputs of Internet image search engines better than a set of strong
baselines.
Image matting with normalized weight and semi-supervised learning
Authors: Ping Li, Tingyan Duan, Yongfeng Cao
Date: 2017-10-27 00:00:00
Tasks: Image Matting

Image matting is an important vision problem. The main stream methods for it
combine sampling-based methods and propagation-based methods. In this paper, we
deal with the combination with a normalized weighting parameter, which could
well control the relative relationship between information from sampling and
from propagation. A reasonable value range for this parameter is given based on
statistics from the standard benchmark dataset. The matting is further improved
by introducing semi-supervised learning iterations, which automatically refine
the trimap without user's interaction. This is especially beneficial when the
trimap is coarse. The experimental results on standard benchmark dataset have
shown that both the normalized weighting parameter and the semi-supervised
learning iteration could significantly improve the matting performance.
An efficient SAT formulation for learning multiple criteria non-compensatory sorting rules from examples
Authors: K. Belahcène, C. Labreuche, N. Maudet, V. Mousseau, W. Ouerdane
Date: 2017-10-27 00:00:00

The literature on Multiple Criteria Decision Analysis (MCDA) proposes several
methods in order to sort alternatives evaluated on several attributes into
ordered classes. Non Compensatory Sorting models (NCS) assign alternatives to
classes based on the way they compare to multicriteria profiles separating the
consecutive classes. Previous works have proposed approaches to learn the
parameters of a NCS model based on a learning set. Exact approaches based on
mixed integer linear programming ensures that the learning set is best
restored, but can only handle datasets of limited size. Heuristic approaches
can handle large learning sets, but do not provide any guarantee about the
inferred model. In this paper, we propose an alternative formulation to learn a
NCS model. This formulation, based on a SAT problem, guarantees to find a model
fully consistent with the learning set (whenever it exists), and is
computationally much more efficient than existing exact MIP approaches.
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
Authors: René Schuster, Oliver Wasenmüller, Georg Kuschk, Christian Bailer, Didier Stricker
Date: 2017-10-27 00:00:00

While most scene flow methods use either variational optimization or a strong
rigid motion assumption, we show for the first time that scene flow can also be
estimated by dense interpolation of sparse matches. To this end, we find sparse
matches across two stereo image pairs that are detected without any prior
regularization and perform dense interpolation preserving geometric and motion
boundaries by using edge information. A few iterations of variational energy
minimization are performed to refine our results, which are thoroughly
evaluated on the KITTI benchmark and additionally compared to state-of-the-art
on MPI Sintel. For application in an automotive context, we further show that
an optional ego-motion model helps to boost performance and blends smoothly
into our approach to produce a segmentation of the scene into static and
dynamic parts.