pwc-archive/papers-with-abstracts huggingface dataset:
The method of artificial systems
Authors: Christopher A. Tucker
Date: 2015-07-06 00:00:00
Tasks: Philosophy

This document is written with the intention to describe in detail a method
and means by which a computer program can reason about the world and in so
doing, increase its analogue to a living system. As the literature is rife and
it is apparent we, as scientists and engineers, have not found the solution,
this document will attempt the solution by grounding its intellectual arguments
within tenets of human cognition in Western philosophy. The result will be a
characteristic description of a method to describe an artificial system
analogous to that performed for a human. The approach was the substance of my
Master's thesis, explored more deeply during the course of my postdoc research.
It focuses primarily on context awareness and choice set within a boundary of
available epistemology, which serves to describe it. Expanded upon, such a
description strives to discover agreement with Kant's critique of reason to
understand how it could be applied to define the architecture of its design.
The intention has never been to mimic human or biological systems, rather, to
understand the profoundly fundamental rules, when leveraged correctly, results
in an artificial consciousness as noumenon while in keeping with the perception
of it as phenomenon.
Learning to Multi-Task by Active Sampling
Authors: Sahil Sharma, Ashutosh Jha, Parikshit Hegde, Balaraman Ravindran
Date: 2017-02-20 00:00:00
Proceeding: ICLR 2018 1
Tasks: Active Learning, Meta-Learning, Multi-Task Learning, Reinforcement Learning

One of the long-standing challenges in Artificial Intelligence for learning
goal-directed behavior is to build a single agent which can solve multiple
tasks. Recent progress in multi-task learning for goal-directed sequential
problems has been in the form of distillation based learning wherein a student
network learns from multiple task-specific expert networks by mimicking the
task-specific policies of the expert networks. While such approaches offer a
promising solution to the multi-task learning problem, they require supervision
from large expert networks which require extensive data and computation time
for training. In this work, we propose an efficient multi-task learning
framework which solves multiple goal-directed tasks in an on-line setup without
the need for expert supervision. Our work uses active learning principles to
achieve multi-task learning by sampling the harder tasks more than the easier
ones. We propose three distinct models under our active sampling framework. An
adaptive method with extremely competitive multi-tasking performance. A
UCB-based meta-learner which casts the problem of picking the next task to
train on as a multi-armed bandit problem. A meta-learning method that casts the
next-task picking problem as a full Reinforcement Learning problem and uses
actor critic methods for optimizing the multi-tasking performance directly. We
demonstrate results in the Atari 2600 domain on seven multi-tasking instances:
three 6-task instances, one 8-task instance, two 12-task instances and one
21-task instance.
Sketched Answer Set Programming
Authors: Sergey Paramonov, Christian Bessiere, Anton Dries, Luc De Raedt
Date: 2017-05-21 00:00:00

Answer Set Programming (ASP) is a powerful modeling formalism for
combinatorial problems. However, writing ASP models is not trivial. We propose
a novel method, called Sketched Answer Set Programming (SkASP), aiming at
supporting the user in resolving this issue. The user writes an ASP program
while marking uncertain parts open with question marks. In addition, the user
provides a number of positive and negative examples of the desired program
behaviour. The sketched model is rewritten into another ASP program, which is
solved by traditional methods. As a result, the user obtains a functional and
reusable ASP program modelling her problem. We evaluate our approach on 21 well
known puzzles and combinatorial problems inspired by Karp's 21 NP-complete
problems and demonstrate a use-case for a database application based on ASP.
Scene Flow Estimation: A Survey
Authors: Zike Yan, Xuezhi Xiang
Date: 2016-12-08 00:00:00
Tasks: Scene Flow Estimation, Survey

This paper is the first to review the scene flow estimation field, which
analyzes and compares methods, technical challenges, evaluation methodologies
and performance of scene flow estimation. Existing algorithms are categorized
in terms of scene representation, data source, and calculation scheme, and the
pros and cons in each category are compared briefly. The datasets and
evaluation protocols are enumerated, and the performance of the most
representative methods is presented. A future vision is illustrated with few
questions arisen for discussion. This survey presents a general introduction
and analysis of scene flow estimation.
UMDFaces: An Annotated Face Dataset for Training Deep Networks
Authors: Ankan Bansal, Anirudh Nanduri, Carlos Castillo, Rajeev Ranjan, Rama Chellappa
Date: 2016-11-04 00:00:00
Tasks: Face Detection, Face Recognition, Keypoint Detection

Recent progress in face detection (including keypoint detection), and
recognition is mainly being driven by (i) deeper convolutional neural network
architectures, and (ii) larger datasets. However, most of the large datasets
are maintained by private companies and are not publicly available. The
academic computer vision community needs larger and more varied datasets to
make further progress.
  In this paper we introduce a new face dataset, called UMDFaces, which has
367,888 annotated faces of 8,277 subjects. We also introduce a new face
recognition evaluation protocol which will help advance the state-of-the-art in
this area. We discuss how a large dataset can be collected and annotated using
human annotators and deep networks. We provide human curated bounding boxes for
faces. We also provide estimated pose (roll, pitch and yaw), locations of
twenty-one key-points and gender information generated by a pre-trained neural
network. In addition, the quality of keypoint annotations has been verified by
humans for about 115,000 images. Finally, we compare the quality of the dataset
with other publicly available face datasets at similar scales.
Convergence of backpropagation with momentum for network architectures with skip connections
Authors: Chirag Agarwal, Joe Klobusicky, Dan Schonfeld
Date: 2017-05-21 00:00:00

We study a class of deep neural networks with networks that form a directed acyclic graph (DAG). For backpropagation defined by gradient descent with adaptive momentum, we show weights converge for a large class of nonlinear activation functions. The proof generalizes the results of Wu et al. (2008) who showed convergence for a feed forward network with one hidden layer. For an example of the effectiveness of DAG architectures, we describe an example of compression through an autoencoder, and compare against sequential feed forward networks under several metrics.
Stochastic Quasi-Newton Methods for Nonconvex Stochastic Optimization
Authors: Xiao Wang, Shiqian Ma, Donald Goldfarb, Wei Liu
Date: 2016-07-05 00:00:00
Tasks: Binary Classification, General Classification, Stochastic Optimization

In this paper we study stochastic quasi-Newton methods for nonconvex
stochastic optimization, where we assume that noisy information about the
gradients of the objective function is available via a stochastic first-order
oracle (SFO). We propose a general framework for such methods, for which we
prove almost sure convergence to stationary points and analyze its worst-case
iteration complexity. When a randomly chosen iterate is returned as the output
of such an algorithm, we prove that in the worst-case, the SFO-calls complexity
is $O(\epsilon^{-2})$ to ensure that the expectation of the squared norm of the
gradient is smaller than the given accuracy tolerance $\epsilon$. We also
propose a specific algorithm, namely a stochastic damped L-BFGS (SdLBFGS)
method, that falls under the proposed framework. {Moreover, we incorporate the
SVRG variance reduction technique into the proposed SdLBFGS method, and analyze
its SFO-calls complexity. Numerical results on a nonconvex binary
classification problem using SVM, and a multiclass classification problem using
neural networks are reported.
Balanced Policy Evaluation and Learning
Authors: Nathan Kallus
Date: 2017-05-21 00:00:00
Proceeding: NeurIPS 2018 12

We present a new approach to the problems of evaluating and learning personalized decision policies from observational data of past contexts, decisions, and outcomes. Only the outcome of the enacted decision is available and the historical policy is unknown. These problems arise in personalized medicine using electronic health records and in internet advertising. Existing approaches use inverse propensity weighting (or, doubly robust versions) to make historical outcome (or, residual) data look like it were generated by a new policy being evaluated or learned. But this relies on a plug-in approach that rejects data points with a decision that disagrees with the new policy, leading to high variance estimates and ineffective learning. We propose a new, balance-based approach that too makes the data look like the new policy but does so directly by finding weights that optimize for balance between the weighted data and the target policy in the given, finite sample, which is equivalent to minimizing worst-case or posterior conditional mean square error. Our policy learner proceeds as a two-level optimization problem over policies and weights. We demonstrate that this approach markedly outperforms existing ones both in evaluation and learning, which is unsurprising given the wider support of balance-based weights. We establish extensive theoretical consistency guarantees and regret bounds that support this empirical success.
Automatic Liver Lesion Detection using Cascaded Deep Residual Networks
Authors: Lei Bi, Jinman Kim, Ashnil Kumar, Dagan Feng
Date: 2017-04-10 00:00:00
Tasks: Lesion Detection, Segmentation, Tumor Segmentation

Automatic segmentation of liver lesions is a fundamental requirement towards
the creation of computer aided diagnosis (CAD) and decision support systems
(CDS). Traditional segmentation approaches depend heavily upon hand-crafted
features and a priori knowledge of the user. As such, these methods are
difficult to adopt within a clinical environment. Recently, deep learning
methods based on fully convolutional networks (FCNs) have been successful in
many segmentation problems primarily because they leverage a large labelled
dataset to hierarchically learn the features that best correspond to the
shallow visual appearance as well as the deep semantics of the areas to be
segmented. However, FCNs based on a 16 layer VGGNet architecture have limited
capacity to add additional layers. Therefore, it is challenging to learn more
discriminative features among different classes for FCNs. In this study, we
overcome these limitations using deep residual networks (ResNet) to segment
liver lesions. ResNet contain skip connections between convolutional layers,
which solved the problem of the training degradation of training accuracy in
very deep networks and thereby enables the use of additional layers for
learning more discriminative features. In addition, we achieve more precise
boundary definitions through a novel cascaded ResNet architecture with
multi-scale fusion to gradually learn and infer the boundaries of both the
liver and the liver lesions. Our proposed method achieved 4th place in the ISBI
2017 Liver Tumor Segmentation Challenge by the submission deadline.
Instrument-Armed Bandits
Authors: Nathan Kallus
Date: 2017-05-21 00:00:00
Tasks: Ethics

We extend the classic multi-armed bandit (MAB) model to the setting of
noncompliance, where the arm pull is a mere instrument and the treatment
applied may differ from it, which gives rise to the instrument-armed bandit
(IAB) problem. The IAB setting is relevant whenever the experimental units are
human since free will, ethics, and the law may prohibit unrestricted or forced
application of treatment. In particular, the setting is relevant in bandit
models of dynamic clinical trials and other controlled trials on human
interventions. Nonetheless, the setting has not been fully investigate in the
bandit literature. We show that there are various and divergent notions of
regret in this setting, all of which coincide only in the classic MAB setting.
We characterize the behavior of these regrets and analyze standard MAB
algorithms. We argue for a particular kind of regret that captures the causal
effect of treatments but show that standard MAB algorithms cannot achieve
sublinear control on this regret. Instead, we develop new algorithms for the
IAB problem, prove new regret bounds for them, and compare them to standard MAB
algorithms in numerical examples.
The Extreme Value Machine
Authors: Ethan M. Rudd, Lalit P. Jain, Walter J. Scheirer, Terrance E. Boult
Date: 2015-06-19 00:00:00
Tasks: Incremental Learning

It is often desirable to be able to recognize when inputs to a recognition
function learned in a supervised manner correspond to classes unseen at
training time. With this ability, new class labels could be assigned to these
inputs by a human operator, allowing them to be incorporated into the
recognition function --- ideally under an efficient incremental update
mechanism. While good algorithms that assume inputs from a fixed set of classes
exist, e.g., artificial neural networks and kernel machines, it is not
immediately obvious how to extend them to perform incremental learning in the
presence of unknown query classes. Existing algorithms take little to no
distributional information into account when learning recognition functions and
lack a strong theoretical foundation. We address this gap by formulating a
novel, theoretically sound classifier --- the Extreme Value Machine (EVM). The
EVM has a well-grounded interpretation derived from statistical Extreme Value
Theory (EVT), and is the first classifier to be able to perform nonlinear
kernel-free variable bandwidth incremental learning. Compared to other
classifiers in the same deep network derived feature space, the EVM is accurate
and efficient on an established benchmark partition of the ImageNet dataset.
Spelling Correction as a Foreign Language
Authors: Yingbo Zhou, Utkarsh Porwal, Roberto Konow
Date: 2017-05-21 00:00:00
Tasks: Decoder, Feature Engineering, Language Modeling, Language Modelling, Machine Translation, Spelling Correction, Translation

In this paper, we reformulated the spell correction problem as a machine translation task under the encoder-decoder framework. This reformulation enabled us to use a single model for solving the problem that is traditionally formulated as learning a language model and an error model. This model employs multi-layer recurrent neural networks as an encoder and a decoder. We demonstrate the effectiveness of this model using an internal dataset, where the training data is automatically obtained from user logs. The model offers competitive performance as compared to the state of the art methods but does not require any feature engineering nor hand tuning between models.
DeepNav: Learning to Navigate Large Cities
Authors: Samarth Brahmbhatt, James Hays
Date: 2017-01-31 00:00:00
Proceeding: CVPR 2017 7
Tasks: Navigate

We present DeepNav, a Convolutional Neural Network (CNN) based algorithm for
navigating large cities using locally visible street-view images. The DeepNav
agent learns to reach its destination quickly by making the correct navigation
decisions at intersections. We collect a large-scale dataset of street-view
images organized in a graph where nodes are connected by roads. This dataset
contains 10 city graphs and more than 1 million street-view images. We propose
3 supervised learning approaches for the navigation task and show how A* search
in the city graph can be used to generate supervision for the learning. Our
annotation process is fully automated using publicly available mapping services
and requires no human input. We evaluate the proposed DeepNav models on 4
held-out cities for navigating to 5 different types of destinations. Our
algorithms outperform previous work that uses hand-crafted features and Support
Vector Regression (SVR)[19].
Forward Thinking: Building Deep Random Forests
Authors: Kevin Miller, Chris Hettinger, Jeffrey Humpherys, Tyler Jarvis, David Kartchner
Date: 2017-05-20 00:00:00

The success of deep neural networks has inspired many to wonder whether other
learners could benefit from deep, layered architectures. We present a general
framework called forward thinking for deep learning that generalizes the
architectural flexibility and sophistication of deep neural networks while also
allowing for (i) different types of learning functions in the network, other
than neurons, and (ii) the ability to adaptively deepen the network as needed
to improve results. This is done by training one layer at a time, and once a
layer is trained, the input data are mapped forward through the layer to create
a new learning problem. The process is then repeated, transforming the data
through multiple layers, one at a time, rendering a new dataset, which is
expected to be better behaved, and on which a final output layer can achieve
good performance. In the case where the neurons of deep neural nets are
replaced with decision trees, we call the result a Forward Thinking Deep Random
Forest (FTDRF). We demonstrate a proof of concept by applying FTDRF on the
MNIST dataset. We also provide a general mathematical formulation that allows
for other types of deep learning problems to be considered.
Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos
Authors: De-An Huang, Joseph J. Lim, Li Fei-Fei, Juan Carlos Niebles
Date: 2017-03-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Referring Expression

We propose an unsupervised method for reference resolution in instructional
videos, where the goal is to temporally link an entity (e.g., "dressing") to
the action (e.g., "mix yogurt") that produced it. The key challenge is the
inevitable visual-linguistic ambiguities arising from the changes in both
visual appearance and referring expression of an entity in the video. This
challenge is amplified by the fact that we aim to resolve references with no
supervision. We address these challenges by learning a joint visual-linguistic
model, where linguistic cues can help resolve visual ambiguities and vice
versa. We verify our approach by learning our model unsupervisedly using more
than two thousand unstructured cooking videos from YouTube, and show that our
visual-linguistic model can substantially improve upon state-of-the-art
linguistic only model on reference resolution in instructional videos.
Honey Bee Dance Modeling in Real-time using Machine Learning
Authors: Abolfazl Saghafi, Chris P. Tsokos
Date: 2017-05-20 00:00:00
Tasks: BIG-bench Machine Learning

The waggle dance that honeybees perform is an astonishing way of
communicating the location of food source. After over 60 years of its
discovery, researchers still use manual labeling by watching hours of dance
videos to detect different transitions between dance components thus extracting
information regarding the distance and direction to the food source. We propose
an automated process to monitor and segment different components of honeybee
waggle dance. The process is highly accurate, runs in real-time, and can use
shared information between multiple dances.
Why You Should Charge Your Friends for Borrowing Your Stuff
Authors: Kijung Shin, Euiwoong Lee, Dhivya Eswaran, Ariel D. Procaccia
Date: 2017-05-20 00:00:00

We consider goods that can be shared with k-hop neighbors (i.e., the set of
nodes within k hops from an owner) on a social network. We examine incentives
to buy such a good by devising game-theoretic models where each node decides
whether to buy the good or free ride. First, we find that social inefficiency,
specifically excessive purchase of the good, occurs in Nash equilibria. Second,
the social inefficiency decreases as k increases and thus a good can be shared
with more nodes. Third, and most importantly, the social inefficiency can also
be significantly reduced by charging free riders an access cost and paying it
to owners, leading to the conclusion that organizations and system designers
should impose such a cost. These findings are supported by our theoretical
analysis in terms of the price of anarchy and the price of stability; and by
simulations based on synthetic and real social networks.
Cluster-Wise Ratio Tests for Fast Camera Localization
Authors: Raúl Díaz, Charless C. Fowlkes
Date: 2016-12-06 00:00:00
Tasks: Camera Localization, Clustering, Diagnostic

Feature point matching for camera localization suffers from scalability
problems. Even when feature descriptors associated with 3D scene points are
locally unique, as coverage grows, similar or repeated features become
increasingly common. As a result, the standard distance ratio-test used to
identify reliable image feature points is overly restrictive and rejects many
good candidate matches. We propose a simple coarse-to-fine strategy that uses
conservative approximations to robust local ratio-tests that can be computed
efficiently using global approximate k-nearest neighbor search. We treat these
forward matches as votes in camera pose space and use them to prioritize
back-matching within candidate camera pose clusters, exploiting feature
co-visibility captured by clustering the 3D model camera pose graph. This
approach achieves state-of-the-art camera localization results on a variety of
popular benchmarks, outperforming several methods that use more complicated
data structures and that make more restrictive assumptions on camera pose. We
also carry out diagnostic analyses on a difficult test dataset containing
globally repetitive structure that suggest our approach successfully adapts to
the challenges of large-scale image localization.
Phase-Shifting Separable Haar Wavelets and Applications
Authors: Mais Alnasser, Hassan Foroosh
Date: 2017-05-20 00:00:00

This paper presents a new approach for tackling the shift-invariance problem
in the discrete Haar domain, without trading off any of its desirable
properties, such as compression, separability, orthogonality, and symmetry. The
paper presents several key theoretical contributions. First, we derive closed
form expressions for phase shifting in the Haar domain both in partially
decimated and fully decimated transforms. Second, it is shown that the wavelet
coefficients of the shifted signal can be computed solely by using the
coefficients of the original transformed signal. Third, we derive closed-form
expressions for non-integer shifts, which have not been previously reported in
the literature. Fourth, we establish the complexity of the proposed phase
shifting approach using the derived analytic expressions. As an application
example of these results, we apply the new formulae to image rotation and
interpolation, and evaluate its performance against standard methods.
Combining tabu search and graph reduction to solve the maximum balanced biclique problem
Authors: Yi Zhou, Jin-Kao Hao
Date: 2017-05-20 00:00:00

The Maximum Balanced Biclique Problem is a well-known graph model with
relevant applications in diverse domains. This paper introduces a novel
algorithm, which combines an effective constraint-based tabu search procedure
and two dedicated graph reduction techniques. We verify the effectiveness of
the algorithm on 30 classical random benchmark graphs and 25 very large
real-life sparse graphs from the popular Koblenz Network Collection (KONECT).
The results show that the algorithm improves the best-known results (new lower
bounds) for 10 classical benchmarks and obtains the optimal solutions for 14
KONECT instances.