pwc-archive/papers-with-abstracts huggingface dataset:
Cautious Deep Learning
Authors: Yotam Hechtlinger, Barnabás Póczos, Larry Wasserman
Date: 2018-05-24 00:00:00
Tasks: Conformal Prediction, Deep Learning

Most classifiers operate by selecting the maximum of an estimate of the
conditional distribution $p(y|x)$ where $x$ stands for the features of the
instance to be classified and $y$ denotes its label. This often results in a
{\em hubristic bias}: overconfidence in the assignment of a definite label.
Usually, the observations are concentrated on a small volume but the classifier
provides definite predictions for the entire space. We propose constructing
conformal prediction sets which contain a set of labels rather than a single
label. These conformal prediction sets contain the true label with probability
$1-\alpha$. Our construction is based on $p(x|y)$ rather than $p(y|x)$ which
results in a classifier that is very cautious: it outputs the null set ---
meaning "I don't know" --- when the object does not resemble the training
examples. An important property of our approach is that adversarial attacks are
likely to be predicted as the null set or would also include the true label. We
demonstrate the performance on the ImageNet ILSVRC dataset and the CelebA and
IMDB-Wiki facial datasets using high dimensional features obtained from state
of the art convolutional neural networks.
Invariant Representations without Adversarial Training
Authors: Daniel Moyer, Shuyang Gao, Rob Brekelmans, Greg Ver Steeg, Aram Galstyan
Date: 2018-05-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Representation Learning

Representations of data that are invariant to changes in specified factors are useful for a wide range of problems: removing potential biases in prediction problems, controlling the effects of covariates, and disentangling meaningful factors of variation. Unfortunately, learning representations that exhibit invariance to arbitrary nuisance factors yet remain useful for other tasks is challenging. Existing approaches cast the trade-off between task performance and invariance in an adversarial way, using an iterative minimax optimization. We show that adversarial training is unnecessary and sometimes counter-productive; we instead cast invariant representation learning as a single information-theoretic objective that can be directly optimized. We demonstrate that this approach matches or exceeds performance of state-of-the-art adversarial approaches for learning fair representations and for generative modeling with controllable transformations.
A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning
Authors: Dan Nguyen, Troy Long, Xun Jia, Weiguo Lu, Xuejun Gu, Zohaib Iqbal, Steve Jiang
Date: 2017-09-26 00:00:00
Tasks: Anatomy

With the advancement of treatment modalities in radiation therapy for cancer
patients, outcomes have improved, but at the cost of increased treatment plan
complexity and planning time. The accurate prediction of dose distributions
would alleviate this issue by guiding clinical plan optimization to save time
and maintain high quality plans. We have modified a convolutional deep network
model, U-net (originally designed for segmentation purposes), for predicting
dose from patient image contours of the planning target volume (PTV) and organs
at risk (OAR). We show that, as an example, we are able to accurately predict
the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer
patients, where the average Dice similarity coefficient is 0.91 when comparing
the predicted vs. true isodose volumes between 0% and 100% of the prescription
dose. The average value of the absolute differences in [max, mean] dose is
found to be under 5% of the prescription dose, specifically for each structure
is [1.80%, 1.03%](PTV), [1.94%, 4.22%](Bladder), [1.80%, 0.48%](Body), [3.87%,
1.79%](L Femoral Head), [5.07%, 2.55%](R Femoral Head), and [1.26%,
1.62%](Rectum) of the prescription dose. We thus managed to map a desired
radiation dose distribution from a patient's PTV and OAR contours. As an
additional advantage, relatively little data was used in the techniques and
models described in this paper.
Dyna Planning using a Feature Based Generative Model
Authors: Ryan Faulkner, Doina Precup
Date: 2018-05-23 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Dyna-style reinforcement learning is a powerful approach for problems where
not much real data is available. The main idea is to supplement real
trajectories, or sequences of sampled states over time, with simulated ones
sampled from a learned model of the environment. However, in large state
spaces, the problem of learning a good generative model of the environment has
been open so far. We propose to use deep belief networks to learn an
environment model for use in Dyna. We present our approach and validate it
empirically on problems where the state observations consist of images. Our
results demonstrate that using deep belief networks, which are full generative
models, significantly outperforms the use of linear expectation models,
proposed in Sutton et al. (2008)
Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms
Authors: Matthew M. Dunlop, Dejan Slepčev, Andrew M. Stuart, Matthew Thorpe
Date: 2018-05-23 00:00:00

Scalings in which the graph Laplacian approaches a differential operator in
the large graph limit are used to develop understanding of a number of
algorithms for semi-supervised learning; in particular the extension, to this
graph setting, of the probit algorithm, level set and kriging methods, are
studied. Both optimization and Bayesian approaches are considered, based around
a regularizing quadratic form found from an affine transformation of the
Laplacian, raised to a, possibly fractional, exponent. Conditions on the
parameters defining this quadratic form are identified under which well-defined
limiting continuum analogues of the optimization and Bayesian semi-supervised
learning problems may be found, thereby shedding light on the design of
algorithms in the large graph setting. The large graph limits of the
optimization formulations are tackled through $\Gamma-$convergence, using the
recently introduced $TL^p$ metric. The small labelling noise limits of the
Bayesian formulations are also identified, and contrasted with pre-existing
harmonic function approaches to the problem.
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Authors: Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow
Date: 2018-04-24 00:00:00
Proceeding: NeurIPS 2018 12

Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. SSL algorithms based on deep neural networks have recently proven successful on standard benchmark tasks. However, we argue that these benchmarks fail to address many issues that these algorithms would face in real-world applications. After creating a unified reimplementation of various widely-used SSL techniques, we test them in a suite of experiments designed to address these issues. We find that the performance of simple baselines which do not use unlabeled data is often underreported, that SSL methods differ in sensitivity to the amount of labeled and unlabeled data, and that performance can degrade substantially when the unlabeled dataset contains out-of-class examples. To help guide SSL research towards real-world applicability, we make our unified reimplemention and evaluation platform publicly available.
Learning Determinantal Point Processes by Corrective Negative Sampling
Authors: Zelda Mariet, Mike Gartrell, Suvrit Sra
Date: 2018-02-15 00:00:00
Tasks: Diversity, Language Modeling, Language Modelling, Point Processes

Determinantal Point Processes (DPPs) have attracted significant interest from
the machine-learning community due to their ability to elegantly and tractably
model the delicate balance between quality and diversity of sets. DPPs are
commonly learned from data using maximum likelihood estimation (MLE). While
fitting observed sets well, MLE for DPPs may also assign high likelihoods to
unobserved sets that are far from the true generative distribution of the data.
To address this issue, which reduces the quality of the learned model, we
introduce a novel optimization problem, Contrastive Estimation (CE), which
encodes information about "negative" samples into the basic learning model. CE
is grounded in the successful use of negative information in machine-vision and
language modeling. Depending on the chosen negative distribution (which may be
static or evolve during optimization), CE assumes two different forms, which we
analyze theoretically and experimentally. We evaluate our new model on
real-world datasets; on a challenging dataset, CE learning delivers a
considerable improvement in predictive performance over a DPP learned without
using contrastive information.
Approximation by Combinations of ReLU and Squared ReLU Ridge Functions with $ \ell^1 $ and $ \ell^0 $ Controls
Authors: Jason M. Klusowski, Andrew R. Barron
Date: 2016-07-26 00:00:00

We establish $ L^{\infty} $ and $ L^2 $ error bounds for functions of many
variables that are approximated by linear combinations of ReLU (rectified
linear unit) and squared ReLU ridge functions with $ \ell^1 $ and $ \ell^0 $
controls on their inner and outer parameters. With the squared ReLU ridge
function, we show that the $ L^2 $ approximation error is inversely
proportional to the inner layer $ \ell^0 $ sparsity and it need only be
sublinear in the outer layer $ \ell^0 $ sparsity. Our constructions are
obtained using a variant of the Jones-Barron probabilistic method, which can be
interpreted as either stratified sampling with proportionate allocation or
two-stage cluster sampling. We also provide companion error lower bounds that
reveal near optimality of our constructions. Despite the sparsity assumptions,
we showcase the richness and flexibility of these ridge combinations by
defining a large family of functions, in terms of certain spectral conditions,
that are particularly well approximated by them.
Implicit Language Model in LSTM for OCR
Authors: Ekraam Sabir, Stephen Rawls, Prem Natarajan
Date: 2018-05-23 00:00:00
Tasks: Language Modeling, Language Modelling, model, Optical Character Recognition (OCR)

Neural networks have become the technique of choice for OCR, but many aspects
of how and why they deliver superior performance are still unknown. One key
difference between current neural network techniques using LSTMs and the
previous state-of-the-art HMM systems is that HMM systems have a strong
independence assumption. In comparison LSTMs have no explicit constraints on
the amount of context that can be considered during decoding. In this paper we
show that they learn an implicit LM and attempt to characterize the strength of
the LM in terms of equivalent n-gram context. We show that this implicitly
learned language model provides a 2.4\% CER improvement on our synthetic test
set when compared against a test set of random characters (i.e. not naturally
occurring sequences), and that the LSTM learns to use up to 5 characters of
context (which is roughly 88 frames in our configuration). We believe that this
is the first ever attempt at characterizing the strength of the implicit LM in
LSTM based OCR systems.
Improving Object Counting with Heatmap Regulation
Authors: Shubhra Aich, Ian Stavness
Date: 2018-03-14 00:00:00
Tasks: Crowd Counting, Object, Object Counting, regression

In this paper, we propose a simple and effective way to improve one-look
regression models for object counting from images. We use class activation map
visualizations to illustrate the drawbacks of learning a pure one-look
regression model for a counting task. Based on these insights, we enhance
one-look regression counting models by regulating activation maps from the
final convolution layer of the network with coarse ground-truth activation maps
generated from simple dot annotations. We call this strategy heatmap regulation
(HR). We show that this simple enhancement effectively suppresses false
detections generated by the corresponding one-look baseline model and also
improves the performance in terms of false negatives. Evaluations are performed
on four different counting datasets --- two for car counting (CARPK, PUCPR+),
one for crowd counting (WorldExpo) and another for biological cell counting
(VGG-Cells). Adding HR to a simple VGG front-end improves performance on all
these benchmarks compared to a simple one-look baseline model and results in
state-of-the-art performance for car counting.
Accelerated Optimization in the PDE Framework: Formulations for the Manifold of Diffeomorphisms
Authors: Ganesh Sundaramoorthi, Anthony Yezzi
Date: 2018-04-04 00:00:00
Tasks: Optical Flow Estimation, valid

We consider the problem of optimization of cost functionals on the
infinite-dimensional manifold of diffeomorphisms. We present a new class of
optimization methods, valid for any optimization problem setup on the space of
diffeomorphisms by generalizing Nesterov accelerated optimization to the
manifold of diffeomorphisms. While our framework is general for infinite
dimensional manifolds, we specifically treat the case of diffeomorphisms,
motivated by optical flow problems in computer vision. This is accomplished by
building on a recent variational approach to a general class of accelerated
optimization methods by Wibisono, Wilson and Jordan, which applies in finite
dimensions. We generalize that approach to infinite dimensional manifolds. We
derive the surprisingly simple continuum evolution equations, which are partial
differential equations, for accelerated gradient descent, and relate it to
simple mechanical principles from fluid mechanics. Our approach has natural
connections to the optimal mass transport problem. This is because one can
think of our approach as an evolution of an infinite number of particles
endowed with mass (represented with a mass density) that moves in an energy
landscape. The mass evolves with the optimization variable, and endows the
particles with dynamics. This is different than the finite dimensional case
where only a single particle moves and hence the dynamics does not depend on
the mass. We derive the theory, compute the PDEs for accelerated optimization,
and illustrate the behavior of these new accelerated optimization schemes.
Modeling Interpersonal Influence of Verbal Behavior in Couples Therapy Dyadic Interactions
Authors: Sandeep Nallan Chakravarthula, Brian Baucom, Panayiotis Georgiou
Date: 2018-05-23 00:00:00

Dyadic interactions among humans are marked by speakers continuously
influencing and reacting to each other in terms of responses and behaviors,
among others. Understanding how interpersonal dynamics affect behavior is
important for successful treatment in psychotherapy domains. Traditional
schemes that automatically identify behavior for this purpose have often looked
at only the target speaker. In this work, we propose a Markov model of how a
target speaker's behavior is influenced by their own past behavior as well as
their perception of their partner's behavior, based on lexical features. Apart
from incorporating additional potentially useful information, our model can
also control the degree to which the partner affects the target speaker. We
evaluate our proposed model on the task of classifying Negative behavior in
Couples Therapy and show that it is more accurate than the single-speaker
model. Furthermore, we investigate the degree to which the optimal influence
relates to how well a couple does on the long-term, via relating to
relationship outcomes
A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training
Authors: Viacheslav Dudar, Giovanni Chierchia, Emilie Chouzenoux, Jean-Christophe Pesquet, Vladimir Semenov
Date: 2018-05-23 00:00:00

In this paper, we develop a novel second-order method for training
feed-forward neural nets. At each iteration, we construct a quadratic
approximation to the cost function in a low-dimensional subspace. We minimize
this approximation inside a trust region through a two-stage procedure: first
inside the embedded positive curvature subspace, followed by a gradient descent
step. This approach leads to a fast objective function decay, prevents
convergence to saddle points, and alleviates the need for manually tuning
parameters. We show the good performance of the proposed algorithm on benchmark
datasets.
M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search
Authors: Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao
Date: 2018-02-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Knowledge Base Completion, Link Prediction, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning to walk over a graph towards a target node for a given query and a
source node is an important problem in applications such as knowledge base
completion (KBC). It can be formulated as a reinforcement learning (RL) problem
with a known state transition model. To overcome the challenge of sparse
rewards, we develop a graph-walking agent called M-Walk, which consists of a
deep recurrent neural network (RNN) and Monte Carlo Tree Search (MCTS). The RNN
encodes the state (i.e., history of the walked path) and maps it separately to
a policy and Q-values. In order to effectively train the agent from sparse
rewards, we combine MCTS with the neural policy to generate trajectories
yielding more positive rewards. From these trajectories, the network is
improved in an off-policy manner using Q-learning, which modifies the RNN
policy via parameter sharing. Our proposed RL algorithm repeatedly applies this
policy-improvement step to learn the model. At test time, MCTS is combined with
the neural policy to predict the target node. Experimental results on several
graph-walking benchmarks show that M-Walk is able to learn better policies than
other RL-based methods, which are mainly based on policy gradients. M-Walk also
outperforms traditional KBC baselines.
Use of symmetric kernels for convolutional neural networks
Authors: Viacheslav Dudar, Vladimir Semenov
Date: 2018-05-23 00:00:00

At this work we introduce horizontally symmetric convolutional kernels for
CNNs which make the network output invariant to horizontal flips of the image.
We also study other types of symmetric kernels which lead to vertical flip
invariance, and approximate rotational invariance. We show that usage of such
kernels acts as regularizer, and improves generalization of the convolutional
neural networks at the cost of more complicated training process.
Adaptive Stochastic Gradient Langevin Dynamics: Taming Convergence and Saddle Point Escape Time
Authors: Hejian Sang, Jia Liu
Date: 2018-05-23 00:00:00

In this paper, we propose a new adaptive stochastic gradient Langevin
dynamics (ASGLD) algorithmic framework and its two specialized versions, namely
adaptive stochastic gradient (ASG) and adaptive gradient Langevin
dynamics(AGLD), for non-convex optimization problems. All proposed algorithms
can escape from saddle points with at most $O(\log d)$ iterations, which is
nearly dimension-free. Further, we show that ASGLD and ASG converge to a local
minimum with at most $O(\log d/\epsilon^4)$ iterations. Also, ASGLD with full
gradients or ASGLD with a slowly linearly increasing batch size converge to a
local minimum with iterations bounded by $O(\log d/\epsilon^2)$, which
outperforms existing first-order methods.
Deep Active Learning for Anomaly Detection
Authors: Tiago Pimentel, Marianne Monteiro, Adriano Veloso, Nivio Ziviani
Date: 2018-05-23 00:00:00
Tasks: Active Learning, Anomaly Detection, Deep Learning, Unsupervised Anomaly Detection

Anomalies are intuitively easy for human experts to understand, but they are hard to define mathematically. Therefore, in order to have performance guarantees in unsupervised anomaly detection, priors need to be assumed on what the anomalies are. By contrast, active learning provides the necessary priors through appropriate expert feedback. Thus, in this work we present an active learning method that can be built upon existing deep learning solutions for unsupervised anomaly detection, so that outliers can be separated from normal data effectively. We introduce a new layer that can be easily attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method. We report results on both synthetic and real anomaly detection datasets, using multi-layer perceptrons and autoencoder architectures empowered with the proposed active layer, and we discuss their performance on finding clustered and low density anomalies.
Non-convex non-local flows for saliency detection
Authors: Iván Ramírez, Gonzalo Galiano, Emanuele Schiavi
Date: 2018-05-23 00:00:00
Tasks: General Classification, image-classification, Image Classification, Saliency Detection

We propose and numerically solve a new variational model for automatic
saliency detection in digital images. Using a non-local framework we consider a
family of edge preserving functions combined with a new quadratic saliency
detection term. Such term defines a constrained bilateral obstacle problem for
image classification driven by p-Laplacian operators, including the so-called
hyper-Laplacian case (0 < p < 1). The related non-convex non-local reactive
flows are then considered and applied for glioblastoma segmentation in magnetic
resonance fluid-attenuated inversion recovery (MRI-Flair) images. A fast
convolutional kernel based approximated solution is computed. The numerical
experiments show how the non-convexity related to the hyperLaplacian operators
provides monotonically better results in terms of the standard metrics.
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers
Authors: Marcel Hirt, Petros Dellaportas
Date: 2018-05-23 00:00:00
Tasks: Bayesian Inference, State Space Models, Variational Inference

We present a scalable approach to performing approximate fully Bayesian
inference in generic state space models. The proposed method is an alternative
to particle MCMC that provides fully Bayesian inference of both the dynamic
latent states and the static parameters of the model. We build up on recent
advances in computational statistics that combine variational methods with
sequential Monte Carlo sampling and we demonstrate the advantages of performing
full Bayesian inference over the static parameters rather than just performing
variational EM approximations. We illustrate how our approach enables scalable
inference in multivariate stochastic volatility models and self-exciting point
process models that allow for flexible dynamics in the latent intensity
function.