pwc-archive/papers-with-abstracts huggingface dataset:
Differentially Private Gaussian Processes
Authors: Michael Thomas Smith, Max Zwiessele, Neil D. Lawrence
Date: 2016-06-02 00:00:00
Tasks: Gaussian Processes, regression

A major challenge for machine learning is increasing the availability of data
while respecting the privacy of individuals. Here we combine the provable
privacy guarantees of the differential privacy framework with the flexibility
of Gaussian processes (GPs). We propose a method using GPs to provide
differentially private (DP) regression. We then improve this method by crafting
the DP noise covariance structure to efficiently protect the training data,
while minimising the scale of the added noise. We find that this cloaking
method achieves the greatest accuracy, while still providing privacy
guarantees, and offers practical DP for regression over multi-dimensional
inputs. Together these methods provide a starter toolkit for combining
differential privacy and GPs.
Universal Reinforcement Learning Algorithms: Survey and Experiments
Authors: John Aslanides, Jan Leike, Marcus Hutter
Date: 2017-05-30 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Survey

Many state-of-the-art reinforcement learning (RL) algorithms typically assume
that the environment is an ergodic Markov Decision Process (MDP). In contrast,
the field of universal reinforcement learning (URL) is concerned with
algorithms that make as few assumptions as possible about the environment. The
universal Bayesian agent AIXI and a family of related URL algorithms have been
developed in this setting. While numerous theoretical optimality results have
been proven for these agents, there has been no empirical investigation of
their behavior to date. We present a short and accessible survey of these URL
algorithms under a unified notation and framework, along with results of some
experiments that qualitatively illustrate some properties of the resulting
policies, and their relative performance on partially-observable gridworld
environments. We also present an open-source reference implementation of the
algorithms which we hope will facilitate further understanding of, and
experimentation with, these ideas.
Interpreting and Extending The Guided Filter Via Cyclic Coordinate Descent
Authors: Longquan Dai
Date: 2017-05-30 00:00:00

In this paper, we will disclose that the Guided Filter (GF) can be
interpreted as the Cyclic Coordinate Descent (CCD) solver of a Least Square
(LS) objective function. This discovery implies a possible way to extend GF
because we can alter the objective function of GF and define new filters as the
first pass iteration of the CCD solver of modified objective functions.
Moreover, referring to the iterative minimizing procedure of CCD, we can derive
new rolling filtering schemes. Hence, under the guidance of this discovery, we
not only propose new GF-like filters adapting to the specific requirements of
applications but also offer thoroughly explanations for two rolling filtering
schemes of GF as well as the way to extend them. Experiments show that our new
filters and extensions produce state-of-the-art results.
Understanding Abuse: A Typology of Abusive Language Detection Subtasks
Authors: Zeerak Waseem, Thomas Davidson, Dana Warmsley, Ingmar Weber
Date: 2017-05-28 00:00:00
Proceeding: WS 2017 8
Tasks: Abuse Detection, Abusive Language

As the body of research on abusive language detection and analysis grows,
there is a need for critical consideration of the relationships between
different subtasks that have been grouped under this label. Based on work on
hate speech, cyberbullying, and online abuse we propose a typology that
captures central similarities and differences between subtasks and we discuss
its implications for data annotation and feature construction. We emphasize the
practical actions that can be taken by researchers to best approach their
abusive language detection subtask of interest.
Saliency Revisited: Analysis of Mouse Movements versus Fixations
Authors: Hamed R. -Tavakoli, Fawad Ahmed, Ali Borji, Jorma Laaksonen
Date: 2017-05-30 00:00:00
Proceeding: CVPR 2017 7
Tasks: Model Selection, Saliency Prediction

This paper revisits visual saliency prediction by evaluating the recent
advancements in this field such as crowd-sourced mouse tracking-based databases
and contextual annotations. We pursue a critical and quantitative approach
towards some of the new challenges including the quality of mouse tracking
versus eye tracking for model training and evaluation. We extend quantitative
evaluation of models in order to incorporate contextual information by
proposing an evaluation methodology that allows accounting for contextual
factors such as text, faces, and object attributes. The proposed contextual
evaluation scheme facilitates detailed analysis of models and helps identify
their pros and cons. Through several experiments, we find that (1) mouse
tracking data has lower inter-participant visual congruency and higher
dispersion, compared to the eye tracking data, (2) mouse tracking data does not
totally agree with eye tracking in general and in terms of different contextual
regions in specific, and (3) mouse tracking data leads to acceptable results in
training current existing models, and (4) mouse tracking data is less reliable
for model selection and evaluation. The contextual evaluation also reveals
that, among the studied models, there is no single model that performs best on
all the tested annotations.
Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks
Authors: Hannah Spitzer, Katrin Amunts, Stefan Harmeling, Timo Dickscheid
Date: 2017-05-30 00:00:00

Microscopic analysis of histological sections is considered the "gold
standard" to verify structural parcellations in the human brain. Its high
resolution allows the study of laminar and columnar patterns of cell
distributions, which build an important basis for the simulation of cortical
areas and networks. However, such cytoarchitectonic mapping is a semiautomatic,
time consuming process that does not scale with high throughput imaging. We
present an automatic approach for parcellating histological sections at 2um
resolution. It is based on a convolutional neural network that combines
topological information from probabilistic atlases with the texture features
learned from high-resolution cell-body stained images. The model is applied to
visual areas and trained on a sparse set of partial annotations. We show how
predictions are transferable to new brains and spatially consistent across
sections.
Constrained Policy Optimization
Authors: Joshua Achiam, David Held, Aviv Tamar, Pieter Abbeel
Date: 2017-05-30 00:00:00
Proceeding: ICML 2017 8
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

For many applications of reinforcement learning it can be more convenient to
specify both a reward function and constraints, rather than trying to design
behavior through the reward function. For example, systems that physically
interact with or around humans should satisfy safety constraints. Recent
advances in policy search algorithms (Mnih et al., 2016, Schulman et al., 2015,
Lillicrap et al., 2016, Levine et al., 2016) have enabled new capabilities in
high-dimensional control, but do not consider the constrained setting.
  We propose Constrained Policy Optimization (CPO), the first general-purpose
policy search algorithm for constrained reinforcement learning with guarantees
for near-constraint satisfaction at each iteration. Our method allows us to
train neural network policies for high-dimensional control while making
guarantees about policy behavior all throughout training. Our guarantees are
based on a new theoretical result, which is of independent interest: we prove a
bound relating the expected returns of two policies to an average divergence
between them. We demonstrate the effectiveness of our approach on simulated
robot locomotion tasks where the agent must satisfy constraints motivated by
safety.
Towards Better Response Times and Higher-Quality Queries in Interactive Knowledge Base Debugging
Authors: Patrick Rodler
Date: 2016-09-08 00:00:00
Tasks: Active Learning, Logical Reasoning

Many AI applications rely on knowledge encoded in a locigal knowledge base
(KB). The most essential benefit of such logical KBs is the opportunity to
perform automatic reasoning which however requires a KB to meet some minimal
quality criteria such as consistency. Without adequate tool assistance, the
task of resolving such violated quality criteria in a KB can be extremely hard,
especially when the problematic KB is large and complex. To this end,
interactive KB debuggers have been introduced which ask a user queries whether
certain statements must or must not hold in the intended domain. The given
answers help to gradually restrict the search space for KB repairs.
  Existing interactive debuggers often rely on a pool-based strategy for query
computation. A pool of query candidates is precomputed, from which the best
candidate according to some query quality criterion is selected to be shown to
the user. This often leads to the generation of many unnecessary query
candidates and thus to a high number of expensive calls to logical reasoning
services. We tackle this issue by an in-depth mathematical analysis of diverse
real-valued active learning query selection measures in order to determine
qualitative criteria that make a query favorable. These criteria are the key to
devising efficient heuristic query search methods. The proposed methods enable
for the first time a completely reasoner-free query generation for interactive
KB debugging while at the same time guaranteeing optimality conditions, e.g.
minimal cardinality or best understandability for the user, of the generated
query that existing methods cannot realize.
  Further, we study different relations between active learning measures. The
obtained picture gives a hint about which measures are more favorable in which
situation or which measures always lead to the same outcomes, based on given
types of queries.
Decorrelation of Neutral Vector Variables: Theory and Applications
Authors: Zhanyu Ma, Jing-Hao Xue, Arne Leijon, Zheng-Hua Tan, Zhen Yang, Jun Guo
Date: 2017-05-30 00:00:00

In this paper, we propose novel strategies for neutral vector variable
decorrelation. Two fundamental invertible transformations, namely serial
nonlinear transformation and parallel nonlinear transformation, are proposed to
carry out the decorrelation. For a neutral vector variable, which is not
multivariate Gaussian distributed, the conventional principal component
analysis (PCA) cannot yield mutually independent scalar variables. With the two
proposed transformations, a highly negatively correlated neutral vector can be
transformed to a set of mutually independent scalar variables with the same
degrees of freedom. We also evaluate the decorrelation performances for the
vectors generated from a single Dirichlet distribution and a mixture of
Dirichlet distributions. The mutual independence is verified with the distance
correlation measurement. The advantages of the proposed decorrelation
strategies are intensively studied and demonstrated with synthesized data and
practical application evaluations.
Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution
Authors: Ryutaro Tanno, Daniel E. Worrall, Aurobrata Ghosh, Enrico Kaden, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
Date: 2017-05-01 00:00:00
Tasks: Bayesian Inference, Image Generation, Super-Resolution

In this work, we investigate the value of uncertainty modeling in 3D
super-resolution with convolutional neural networks (CNNs). Deep learning has
shown success in a plethora of medical image transformation problems, such as
super-resolution (SR) and image synthesis. However, the highly ill-posed nature
of such problems results in inevitable ambiguity in the learning of networks.
We propose to account for intrinsic uncertainty through a per-patch
heteroscedastic noise model and for parameter uncertainty through approximate
Bayesian inference in the form of variational dropout. We show that the
combined benefits of both lead to the state-of-the-art performance SR of
diffusion MR brain images in terms of errors compared to ground truth. We
further show that the reduced error scores produce tangible benefits in
downstream tractography. In addition, the probabilistic nature of the methods
naturally confers a mechanism to quantify uncertainty over the super-resolved
output. We demonstrate through experiments on both healthy and pathological
brains the potential utility of such an uncertainty measure in the risk
assessment of the super-resolved images for subsequent clinical use.
Quantum Low Entropy based Associative Reasoning or QLEAR Learning
Authors: Marko V. Jankovic
Date: 2017-05-30 00:00:00
Tasks: Clustering, General Classification

In this paper, we propose the classification method based on a learning
paradigm we are going to call Quantum Low Entropy based Associative Reasoning
or QLEAR learning. The approach is based on the idea that classification can be
understood as supervised clustering, where a quantum entropy in the context of
the quantum probabilistic model, will be used as a "capturer" (measure, or
external index), of the "natural structure" of the data. By using quantum
entropy we do not make any assumption about linear separability of the data
that are going to be classified. The basic idea is to find close neighbors to a
query sample and then use relative change in the quantum entropy as a measure
of similarity of the newly arrived sample with the representatives of interest.
In other words, method is based on calculation of quantum entropy of the
referent system and its relative change with the addition of the newly arrived
sample. Referent system consists of vectors that represent individual classes
and that are the most similar, in Euclidean distance sense, to the vector that
is analyzed. Here, we analyze the classification problem in the context of
measuring similarities to prototype examples of categories. While nearest
neighbor classifiers are natural in this setting, they suffer from the problem
of high variance (in bias-variance decomposition) in the case of limited
sampling. Alternatively, one could use machine learning techniques (like
support vector machines) but they involve time-consuming optimization. Here we
propose a hybrid of nearest neighbor and machine learning technique which deals
naturally with the multi-class setting, has reasonable computational complexity
both in training and at run time, and yields excellent results in practice.
Exploiting Restricted Boltzmann Machines and Deep Belief Networks in Compressed Sensing
Authors: Luisa F. Polania, Kenneth E. Barner
Date: 2017-05-30 00:00:00
Tasks: compressed sensing

This paper proposes a CS scheme that exploits the representational power of
restricted Boltzmann machines and deep learning architectures to model the
prior distribution of the sparsity pattern of signals belonging to the same
class. The determined probability distribution is then used in a maximum a
posteriori (MAP) approach for the reconstruction. The parameters of the prior
distribution are learned from training data. The motivation behind this
approach is to model the higher-order statistical dependencies between the
coefficients of the sparse representation, with the final goal of improving the
reconstruction. The performance of the proposed method is validated on the
Berkeley Segmentation Dataset and the MNIST Database of handwritten digits.
Joint auto-encoders: a flexible multi-task learning framework
Authors: Baruch Epstein. Ron Meir, Tomer Michaeli
Date: 2017-05-30 00:00:00
Tasks: Domain Adaptation, Multi-Task Learning, Transfer Learning

The incorporation of prior knowledge into learning is essential in achieving
good performance based on small noisy samples. Such knowledge is often
incorporated through the availability of related data arising from domains and
tasks similar to the one of current interest. Ideally one would like to allow
both the data for the current task and for previous related tasks to
self-organize the learning system in such a way that commonalities and
differences between the tasks are learned in a data-driven fashion. We develop
a framework for learning multiple tasks simultaneously, based on sharing
features that are common to all tasks, achieved through the use of a modular
deep feedforward neural network consisting of shared branches, dealing with the
common features of all tasks, and private branches, learning the specific
unique aspects of each task. Once an appropriate weight sharing architecture
has been established, learning takes place through standard algorithms for
feedforward networks, e.g., stochastic gradient descent and its variations. The
method deals with domain adaptation and multi-task learning in a unified
fashion, and can easily deal with data arising from different types of sources.
Numerical experiments demonstrate the effectiveness of learning in domain
adaptation and transfer learning setups, and provide evidence for the flexible
and task-oriented representations arising in the network.
Adaptive Seeding for Gaussian Mixture Models
Authors: Johannes Blömer, Kathrin Bujna
Date: 2013-12-20 00:00:00

We present new initialization methods for the expectation-maximization
algorithm for multivariate Gaussian mixture models. Our methods are adaptions
of the well-known $K$-means++ initialization and the Gonzalez algorithm.
Thereby we aim to close the gap between simple random, e.g. uniform, and
complex methods, that crucially depend on the right choice of hyperparameters.
Our extensive experiments indicate the usefulness of our methods compared to
common techniques and methods, which e.g. apply the original $K$-means++ and
Gonzalez directly, with respect to artificial as well as real-world data sets.
Geometric descent method for convex composite minimization
Authors: Shixiang Chen, Shiqian Ma, Wei Liu
Date: 2016-12-29 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: regression

In this paper, we extend the geometric descent method recently proposed by
Bubeck, Lee and Singh to tackle nonsmooth and strongly convex composite
problems. We prove that our proposed algorithm, dubbed geometric proximal
gradient method (GeoPG), converges with a linear rate $(1-1/\sqrt{\kappa})$ and
thus achieves the optimal rate among first-order methods, where $\kappa$ is the
condition number of the problem. Numerical results on linear regression and
logistic regression with elastic net regularization show that GeoPG compares
favorably with Nesterov's accelerated proximal gradient method, especially when
the problem is ill-conditioned.
Semi-Supervised Learning for Detecting Human Trafficking
Authors: Hamidreza Alvari, Paulo Shakarian, J. E. Kelly Snyder
Date: 2017-05-30 00:00:00

Human trafficking is one of the most atrocious crimes and among the
challenging problems facing law enforcement which demands attention of global
magnitude. In this study, we leverage textual data from the website "Backpage"-
used for classified advertisement- to discern potential patterns of human
trafficking activities which manifest online and identify advertisements of
high interest to law enforcement. Due to the lack of ground truth, we rely on a
human analyst from law enforcement, for hand-labeling a small portion of the
crawled data. We extend the existing Laplacian SVM and present S3VM-R, by
adding a regularization term to exploit exogenous information embedded in our
feature space in favor of the task at hand. We train the proposed method using
labeled and unlabeled data and evaluate it on a fraction of the unlabeled data,
herein referred to as unseen data, with our expert's further verification.
Results from comparisons between our method and other semi-supervised and
supervised approaches on the labeled data demonstrate that our learner is
effective in identifying advertisements of high interest to law enforcement
Robust Tracking Using Region Proposal Networks
Authors: Jimmy Ren, ZHIYANG YU, Jianbo Liu, Rui Zhang, Wenxiu Sun, Jiahao Pang, Xiaohao Chen, Qiong Yan
Date: 2017-05-30 00:00:00
Tasks: Classification, Feature Engineering, General Classification, image-classification, Image Classification, Region Proposal, Visual Tracking

Recent advances in visual tracking showed that deep Convolutional Neural
Networks (CNN) trained for image classification can be strong feature
extractors for discriminative trackers. However, due to the drastic difference
between image classification and tracking, extra treatments such as model
ensemble and feature engineering must be carried out to bridge the two domains.
Such procedures are either time consuming or hard to generalize well across
datasets. In this paper we discovered that the internal structure of Region
Proposal Network (RPN)'s top layer feature can be utilized for robust visual
tracking. We showed that such property has to be unleashed by a novel loss
function which simultaneously considers classification accuracy and bounding
box quality. Without ensemble and any extra treatment on feature maps, our
proposed method achieved state-of-the-art results on several large scale
benchmarks including OTB50, OTB100 and VOT2016. We will make our code publicly
available.
MOBA: a New Arena for Game AI
Authors: Victor do Nascimento Silva, Luiz Chaimowicz
Date: 2017-05-30 00:00:00

Games have always been popular testbeds for Artificial Intelligence (AI). In
the last decade, we have seen the rise of the Multiple Online Battle Arena
(MOBA) games, which are the most played games nowadays. In spite of this, there
are few works that explore MOBA as a testbed for AI Research. In this paper we
present and discuss the main features and opportunities offered by MOBA games
to Game AI Research. We describe the various challenges faced along the game
and also propose a discrete model that can be used to better understand and
explore the game. With this, we aim to encourage the use of MOBA as a novel
research platform for Game AI.
Deep-LK for Efficient Adaptive Object Tracking
Authors: Chaoyang Wang, Hamed Kiani Galoogahi, Chen-Hsuan Lin, Simon Lucey
Date: 2017-05-19 00:00:00
Tasks: Object, Object Tracking, regression

In this paper we present a new approach for efficient regression based object
tracking which we refer to as Deep- LK. Our approach is closely related to the
Generic Object Tracking Using Regression Networks (GOTURN) framework of Held et
al. We make the following contributions. First, we demonstrate that there is a
theoretical relationship between siamese regression networks like GOTURN and
the classical Inverse-Compositional Lucas & Kanade (IC-LK) algorithm. Further,
we demonstrate that unlike GOTURN IC-LK adapts its regressor to the appearance
of the currently tracked frame. We argue that this missing property in GOTURN
can be attributed to its poor performance on unseen objects and/or viewpoints.
Second, we propose a novel framework for object tracking - which we refer to as
Deep-LK - that is inspired by the IC-LK framework. Finally, we show impressive
results demonstrating that Deep-LK substantially outperforms GOTURN.
Additionally, we demonstrate comparable tracking performance to current state
of the art deep-trackers whilst being an order of magnitude (i.e. 100 FPS)
computationally efficient.