pwc-archive/papers-with-abstracts huggingface dataset:
Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners
Authors: Yao Zhou, Arun Reddy Nelakurthi, Jingrui He
Date: 2018-04-17 00:00:00
Tasks: Diversity

With the increasing demand for large amount of labeled data, crowdsourcing
has been used in many large-scale data mining applications. However, most
existing works in crowdsourcing mainly focus on label inference and incentive
design. In this paper, we address a different problem of adaptive crowd
teaching, which is a sub-area of machine teaching in the context of
crowdsourcing. Compared with machines, human beings are extremely good at
learning a specific target concept (e.g., classifying the images into given
categories) and they can also easily transfer the learned concepts into similar
learning tasks. Therefore, a more effective way of utilizing crowdsourcing is
by supervising the crowd to label in the form of teaching. In order to perform
the teaching and expertise estimation simultaneously, we propose an adaptive
teaching framework named JEDI to construct the personalized optimal teaching
set for the crowdsourcing workers. In JEDI teaching, the teacher assumes that
each learner has an exponentially decayed memory. Furthermore, it ensures
comprehensiveness in the learning process by carefully balancing teaching
diversity and learner's accurate learning in terms of teaching usefulness.
Finally, we validate the effectiveness and efficacy of JEDI teaching in
comparison with the state-of-the-art techniques on multiple data sets with both
synthetic learners and real crowdsourcing workers.
Towards Deep Cellular Phenotyping in Placental Histology
Authors: Michael Ferlaino, Craig A. Glastonbury, Carolina Motta-Mejia, Manu Vatish, Ingrid Granne, Stephen Kennedy, Cecilia M. Lindgren, Christoffer Nellåker
Date: 2018-04-09 00:00:00
Tasks: Transfer Learning

The placenta is a complex organ, playing multiple roles during fetal
development. Very little is known about the association between placental
morphological abnormalities and fetal physiology. In this work, we present an
open sourced, computationally tractable deep learning pipeline to analyse
placenta histology at the level of the cell. By utilising two deep
Convolutional Neural Network architectures and transfer learning, we can
robustly localise and classify placental cells within five classes with an
accuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic
knowledge that is capable of both stratifying five distinct cell populations
and learn intraclass phenotypic variance. We envisage that the automation of
this pipeline to population scale studies of placenta histology has the
potential to improve our understanding of basic cellular placental biology and
its variations, particularly its role in predicting adverse birth outcomes.
Enhancing the Accuracy and Fairness of Human Decision Making
Authors: Isabel Valera, Adish Singla, Manuel Gomez Rodriguez
Date: 2018-05-25 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Decision Making, Fairness, Sequential Decision Making

Societies often rely on human experts to take a wide variety of decisions
affecting their members, from jail-or-release decisions taken by judges and
stop-and-frisk decisions taken by police officers to accept-or-reject decisions
taken by academics. In this context, each decision is taken by an expert who is
typically chosen uniformly at random from a pool of experts. However, these
decisions may be imperfect due to limited experience, implicit biases, or
faulty probabilistic reasoning. Can we improve the accuracy and fairness of the
overall decision making process by optimizing the assignment between experts
and decisions?
  In this paper, we address the above problem from the perspective of
sequential decision making and show that, for different fairness notions from
the literature, it reduces to a sequence of (constrained) weighted bipartite
matchings, which can be solved efficiently using algorithms with approximation
guarantees. Moreover, these algorithms also benefit from posterior sampling to
actively trade off exploitation---selecting expert assignments which lead to
accurate and fair decisions---and exploration---selecting expert assignments to
learn about the experts' preferences and biases. We demonstrate the
effectiveness of our algorithms on both synthetic and real-world data and show
that they can significantly improve both the accuracy and fairness of the
decisions taken by pools of experts.
ORGaNICs: A Theory of Working Memory in Brains and Machines
Authors: David J. Heeger, Wayne E. Mackey
Date: 2018-03-16 00:00:00

Working memory is a cognitive process that is responsible for temporarily
holding and manipulating information. Most of the empirical neuroscience
research on working memory has focused on measuring sustained activity in
prefrontal cortex (PFC) and/or parietal cortex during simple delayed-response
tasks, and most of the models of working memory have been based on neural
integrators. But working memory means much more than just holding a piece of
information online. We describe a new theory of working memory, based on a
recurrent neural circuit that we call ORGaNICs (Oscillatory Recurrent GAted
Neural Integrator Circuits). ORGaNICs are a variety of Long Short Term Memory
units (LSTMs), imported from machine learning and artificial intelligence.
ORGaNICs can be used to explain the complex dynamics of delay-period activity
in prefrontal cortex (PFC) during a working memory task. The theory is
analytically tractable so that we can characterize the dynamics, and the theory
provides a means for reading out information from the dynamically varying
responses at any point in time, in spite of the complex dynamics. ORGaNICs can
be implemented with a biophysical (electrical circuit) model of pyramidal
cells, combined with shunting inhibition via a thalamocortical loop. Although
introduced as a computational theory of working memory, ORGaNICs are also
applicable to models of sensory processing, motor preparation and motor
control. ORGaNICs offer computational advantages compared to other varieties of
LSTMs that are commonly used in AI applications. Consequently, ORGaNICs are a
framework for canonical computation in brains and machines.
Learning Self-Imitating Diverse Policies
Authors: Tanmay Gangwani, Qiang Liu, Jian Peng
Date: 2018-05-25 00:00:00
Proceeding: ICLR 2019 5
Tasks: continuous-control, Continuous Control, Decision Making, Deep Reinforcement Learning, Imitation Learning, MuJoCo, Policy Gradient Methods, Q-Learning, Reinforcement Learning, Sequential Decision Making

The success of popular algorithms for deep reinforcement learning, such as
policy-gradients and Q-learning, relies heavily on the availability of an
informative reward signal at each timestep of the sequential decision-making
process. When rewards are only sparsely available during an episode, or a
rewarding feedback is provided only after episode termination, these algorithms
perform sub-optimally due to the difficultly in credit assignment.
Alternatively, trajectory-based policy optimization methods, such as
cross-entropy method and evolution strategies, do not require per-timestep
rewards, but have been found to suffer from high sample complexity by
completing forgoing the temporal nature of the problem. Improving the
efficiency of RL algorithms in real-world problems with sparse or episodic
rewards is therefore a pressing need. In this work, we introduce a
self-imitation learning algorithm that exploits and explores well in the sparse
and episodic reward settings. We view each policy as a state-action visitation
distribution and formulate policy optimization as a divergence minimization
problem. We show that with Jensen-Shannon divergence, this divergence
minimization problem can be reduced into a policy-gradient algorithm with
shaped rewards learned from experience replays. Experimental results indicate
that our algorithm works comparable to existing algorithms in environments with
dense rewards, and significantly better in environments with sparse and
episodic rewards. We then discuss limitations of self-imitation learning, and
propose to solve them by using Stein variational policy gradient descent with
the Jensen-Shannon kernel to learn multiple diverse policies. We demonstrate
its effectiveness on a challenging variant of continuous-control MuJoCo
locomotion tasks.
Semantic Binary Segmentation using Convolutional Networks without Decoders
Authors: Shubhra Aich, William van der Kamp, Ian Stavness
Date: 2018-05-01 00:00:00
Tasks: Decoder, Image Segmentation, Road Segmentation, Segmentation, Semantic Segmentation

In this paper, we propose an efficient architecture for semantic image
segmentation using the depth-to-space (D2S) operation. Our D2S model is
comprised of a standard CNN encoder followed by a depth-to-space reordering of
the final convolutional feature maps. Our approach eliminates the decoder
portion of traditional encoder-decoder segmentation models and reduces the
amount of computation almost by half. As a participant of the DeepGlobe Road
Extraction competition, we evaluate our models on the corresponding road
segmentation dataset. Our highly efficient D2S models exhibit comparable
performance to standard segmentation models with much lower computational cost.
Forecasting the successful execution of horizontal strategy in a diversified corporation via a DEMATEL-supported artificial neural network - A case study
Authors: Hossein Sabzian, Hossein Gharib, Javad Noori, Mohammad Ali Shafia, Mohammad Javad Sheikh
Date: 2018-05-25 00:00:00
Tasks: Decision Making

Nowadays, competition is getting tougher as market shrinks because of
financial crisis of the late 2000s. Organizations are tensely forced to
leverage their core competencies to survive through attracting more customers
and gaining more efficacious operations. In such a situation, diversified
corporations which run multiple businesses have opportunities to get
competitive advantage and differentiate themselves by executing horizontal
strategy. Since this strategy completely engages a number of business units of
a diversified corporation through resource sharing among them, any effort to
implement it will fail if being not supported by enough information. However,
for successful execution of horizontal strategy, managers should have reliable
information concerning its success probability in advance. To provide such a
precious information, a three-step framework has been developed. In the first
step, major influencers on successful execution of horizontal strategy have
been captured through literature study and interviewing subject matter experts.
In the second step through the decision making trial and evaluation laboratory
(DEMATEL) methodology, critical success factors (CSFs) have been extracted from
major influencers and a success probability assessment index system (SPAIS) has
been formed. In the third step, due to the statistical nature (multivariate and
distribution free) of SPAIS, an artificial neural network has been designed for
enabling organizational managers to forecast the success probability of
horizontal strategy execution in a multi-business corporation far better than
other classical models.
UMDSub at SemEval-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword Embedding
Authors: Zhenduo Wang, Ted Pedersen
Date: 2018-05-25 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Task 2

This paper describes the UMDSub system that participated in Task 2 of
SemEval-2018. We developed a system that predicts an emoji given the raw text
in a English tweet. The system is a Multi-channel Convolutional Neural Network
based on subword embeddings for the representation of tweets. This model
improves on character or word based methods by about 2\%. Our system placed
21st of 48 participating systems in the official evaluation.
UMDuluth-CS8761 at SemEval-2018 Task 9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings
Authors: Arshia Z. Hassan, Manikya S. Vallabhajosyula, Ted Pedersen
Date: 2018-05-25 00:00:00
Tasks: Hypernym Discovery, Word Embeddings

Hypernym Discovery is the task of identifying potential hypernyms for a given
term. A hypernym is a more generalized word that is super-ordinate to more
specific words. This paper explores several approaches that rely on
co-occurrence frequencies of word pairs, Hearst Patterns based on regular
expressions, and word embeddings created from the UMBC corpus. Our system
Babbage participated in Subtask 1A for English and placed 6th of 19 systems
when identifying concept hypernyms, and 12th of 18 systems for entity
hypernyms.
Duluth UROP at SemEval-2018 Task 2: Multilingual Emoji Prediction with Ensemble Learning and Oversampling
Authors: Shuning Jin, Ted Pedersen
Date: 2018-05-25 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Ensemble Learning, regression, Task 2

This paper describes the Duluth UROP systems that participated in
SemEval--2018 Task 2, Multilingual Emoji Prediction. We relied on a variety of
ensembles made up of classifiers using Naive Bayes, Logistic Regression, and
Random Forests. We used unigram and bigram features and tried to offset the
skewness of the data through the use of oversampling. Our task evaluation
results place us 19th of 48 systems in the English evaluation, and 5th of 21 in
the Spanish. After the evaluation we realized that some simple changes to
preprocessing could significantly improve our results. After making these
changes we attained results that would have placed us sixth in the English
evaluation, and second in the Spanish.
Learning Restricted Boltzmann Machines via Influence Maximization
Authors: Guy Bresler, Frederic Koehler, Ankur Moitra, Elchanan Mossel
Date: 2018-05-25 00:00:00
Tasks: Collaborative Filtering, Dimensionality Reduction

Graphical models are a rich language for describing high-dimensional
distributions in terms of their dependence structure. While there are
algorithms with provable guarantees for learning undirected graphical models in
a variety of settings, there has been much less progress in the important
scenario when there are latent variables. Here we study Restricted Boltzmann
Machines (or RBMs), which are a popular model with wide-ranging applications in
dimensionality reduction, collaborative filtering, topic modeling, feature
extraction and deep learning.
  The main message of our paper is a strong dichotomy in the feasibility of
learning RBMs, depending on the nature of the interactions between variables:
ferromagnetic models can be learned efficiently, while general models cannot.
In particular, we give a simple greedy algorithm based on influence
maximization to learn ferromagnetic RBMs with bounded degree. In fact, we learn
a description of the distribution on the observed variables as a Markov Random
Field. Our analysis is based on tools from mathematical physics that were
developed to show the concavity of magnetization. Our algorithm extends
straighforwardly to general ferromagnetic Ising models with latent variables.
  Conversely, we show that even for a contant number of latent variables with
constant degree, without ferromagneticity the problem is as hard as sparse
parity with noise. This hardness result is based on a sharp and surprising
characterization of the representational power of bounded degree RBMs: the
distribution on their observed variables can simulate any bounded order MRF.
This result is of independent interest since RBMs are the building blocks of
deep belief networks.
Optimal Bayesian Transfer Learning
Authors: Alireza Karbalayghareh, Xiaoning Qian, Edward R. Dougherty
Date: 2018-01-02 00:00:00
Tasks: Domain Adaptation, Transfer Learning

Transfer learning has recently attracted significant research attention, as
it simultaneously learns from different source domains, which have plenty of
labeled data, and transfers the relevant knowledge to the target domain with
limited labeled data to improve the prediction performance. We propose a
Bayesian transfer learning framework where the source and target domains are
related through the joint prior density of the model parameters. The modeling
of joint prior densities enables better understanding of the "transferability"
between domains. We define a joint Wishart density for the precision matrices
of the Gaussian feature-label distributions in the source and target domains to
act like a bridge that transfers the useful information of the source domain to
help classification in the target domain by improving the target posteriors.
Using several theorems in multivariate statistics, the posteriors and posterior
predictive densities are derived in closed forms with hypergeometric functions
of matrix argument, leading to our novel closed-form and fast Optimal Bayesian
Transfer Learning (OBTL) classifier. Experimental results on both synthetic and
real-world benchmark data confirm the superb performance of the OBTL compared
to the other state-of-the-art transfer learning and domain adaptation methods.
Scalable and Robust Community Detection with Randomized Sketching
Authors: Mostafa Rahmani, Andre Beckus, Adel Karimian, George Atia
Date: 2018-05-25 00:00:00
Tasks: Clustering, Community Detection, Matrix Completion, Retrieval, Stochastic Block Model

This article explores and analyzes the unsupervised clustering of large partially observed graphs. We propose a scalable and provable randomized framework for clustering graphs generated from the stochastic block model. The clustering is first applied to a sub-matrix of the graph's adjacency matrix associated with a reduced graph sketch constructed using random sampling. Then, the clusters of the full graph are inferred based on the clusters extracted from the sketch using a correlation-based retrieval step. Uniform random node sampling is shown to improve the computational complexity over clustering of the full graph when the cluster sizes are balanced. A new random degree-based node sampling algorithm is presented which significantly improves upon the performance of the clustering algorithm even when clusters are unbalanced. This framework improves the phase transitions for matrix-decomposition-based clustering with regard to computational complexity and minimum cluster size, which are shown to be nearly dimension-free in the low inter-cluster connectivity regime. A third sampling technique is shown to improve balance by randomly sampling nodes based on spatial distribution. We provide analysis and numerical results using a convex clustering algorithm based on matrix completion.
Unsupervised Learning for Large-Scale Fiber Detection and Tracking in Microscopic Material Images
Authors: Hongkai Yu, Dazhou Guo, Zhipeng Yan, Wei Liu, Jeff Simmons, Craig P. Przybyla, Song Wang
Date: 2018-05-25 00:00:00

Constructing 3D structures from serial section data is a long standing
problem in microscopy. The structure of a fiber reinforced composite material
can be reconstructed using a tracking-by-detection model. Tracking-by-detection
algorithms rely heavily on detection accuracy, especially the recall
performance. The state-of-the-art fiber detection algorithms perform well under
ideal conditions, but are not accurate where there are local degradations of
image quality, due to contaminants on the material surface and/or defocus blur.
Convolutional Neural Networks (CNN) could be used for this problem, but would
require a large number of manual annotated fibers, which are not available. We
propose an unsupervised learning method to accurately detect fibers on the
large scale, that is robust against local degradations of image quality. The
proposed method does not require manual annotations, but uses fiber shape/size
priors and spatio-temporal consistency in tracking to simulate the supervision
in the training of the CNN. Experiments show significant improvements over
state-of-the-art fiber detection algorithms together with advanced tracking
performance.
Parallel Architecture and Hyperparameter Search via Successive Halving and Classification
Authors: Manoj Kumar, George E. Dahl, Vijay Vasudevan, Mohammad Norouzi
Date: 2018-05-25 00:00:00
Tasks: Classification, General Classification

We present a simple and powerful algorithm for parallel black box
optimization called Successive Halving and Classification (SHAC). The algorithm
operates in $K$ stages of parallel function evaluations and trains a cascade of
binary classifiers to iteratively cull the undesirable regions of the search
space. SHAC is easy to implement, requires no tuning of its own configuration
parameters, is invariant to the scale of the objective function and can be
built using any choice of binary classifier. We adopt tree-based classifiers
within SHAC and achieve competitive performance against several strong
baselines for optimizing synthetic functions, hyperparameters and
architectures.
Neural Argument Generation Augmented with Externally Retrieved Evidence
Authors: Xinyu Hua, Lu Wang
Date: 2018-05-25 00:00:00
Proceeding: ACL 2018 7
Tasks: Decision Making, Decoder

High quality arguments are essential elements for human reasoning and
decision-making processes. However, effective argument construction is a
challenging task for both human and machines. In this work, we study a novel
task on automatically generating arguments of a different stance for a given
statement. We propose an encoder-decoder style neural network-based argument
generation model enriched with externally retrieved evidence from Wikipedia.
Our model first generates a set of talking point phrases as intermediate
representation, followed by a separate decoder producing the final argument
based on both input and the keyphrases. Experiments on a large-scale dataset
collected from Reddit show that our model constructs arguments with more
topic-relevant content than a popular sequence-to-sequence generation model
according to both automatic evaluation and human assessments.
Intrinsic Image Transformation via Scale Space Decomposition
Authors: Lechao Cheng, Chengyi Zhang, Zicheng Liao
Date: 2018-05-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Intrinsic Image Decomposition

We introduce a new network structure for decomposing an image into its
intrinsic albedo and shading. We treat this as an image-to-image transformation
problem and explore the scale space of the input and output. By expanding the
output images (albedo and shading) into their Laplacian pyramid components, we
develop a multi-channel network structure that learns the image-to-image
transformation function in successive frequency bands in parallel, within each
channel is a fully convolutional neural network with skip connections. This
network structure is general and extensible, and has demonstrated excellent
performance on the intrinsic image decomposition problem. We evaluate the
network on two benchmark datasets: the MPI-Sintel dataset and the MIT Intrinsic
Images dataset. Both quantitative and qualitative results show our model
delivers a clear progression over state-of-the-art.
How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?
Authors: Richard Y. Zhang, Cédric Josz, Somayeh Sojoudi, Javad Lavaei
Date: 2018-05-25 00:00:00
Proceeding: NeurIPS 2018 12

When the linear measurements of an instance of low-rank matrix recovery
satisfy a restricted isometry property (RIP)---i.e. they are approximately
norm-preserving---the problem is known to contain no spurious local minima, so
exact recovery is guaranteed. In this paper, we show that moderate RIP is not
enough to eliminate spurious local minima, so existing results can only hold
for near-perfect RIP. In fact, counterexamples are ubiquitous: we prove that
every x is the spurious local minimum of a rank-1 instance of matrix recovery
that satisfies RIP. One specific counterexample has RIP constant $\delta=1/2$,
but causes randomly initialized stochastic gradient descent (SGD) to fail 12%
of the time. SGD is frequently able to avoid and escape spurious local minima,
but this empirical result shows that it can occasionally be defeated by their
existence. Hence, while exact recovery guarantees will likely require a proof
of no spurious local minima, arguments based solely on norm preservation will
only be applicable to a narrow set of nearly-isotropic instances.