pwc-archive/papers-with-abstracts huggingface dataset:
Metacontrol for Adaptive Imagination-Based Optimization
Authors: Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia
Date: 2017-05-07 00:00:00
Tasks: Decision Making, Reinforcement Learning

Many machine learning systems are built to solve the hardest examples of a
particular task, which often makes them large and expensive to run---especially
with respect to the easier examples, which might require much less computation.
For an agent with a limited computational budget, this "one-size-fits-all"
approach may result in the agent wasting valuable computation on easy examples,
while not spending enough on hard examples. Rather than learning a single,
fixed policy for solving all instances of a task, we introduce a metacontroller
which learns to optimize a sequence of "imagined" internal simulations over
predictive models of the world in order to construct a more informed, and more
economical, solution. The metacontroller component is a model-free
reinforcement learning agent, which decides both how many iterations of the
optimization procedure to run, as well as which model to consult on each
iteration. The models (which we call "experts") can be state transition models,
action-value functions, or any other mechanism that provides information useful
for solving the task, and can be learned on-policy or off-policy in parallel
with the metacontroller. When the metacontroller, controller, and experts were
trained with "interaction networks" (Battaglia et al., 2016) as expert models,
our approach was able to solve a challenging decision-making problem under
complex non-linear dynamics. The metacontroller learned to adapt the amount of
computation it performed to the difficulty of the task, and learned how to
choose which experts to consult by factoring in both their reliability and
individual computational resource costs. This allowed the metacontroller to
achieve a lower overall cost (task loss plus computational cost) than more
traditional fixed policy approaches. These results demonstrate that our
approach is a powerful framework for using...
Credible Review Detection with Limited Information using Consistency Analysis
Authors: Subhabrata Mukherjee, Sourav Dutta, Gerhard Weikum
Date: 2017-05-07 00:00:00
Tasks: Topic Models

Online reviews provide viewpoints on the strengths and shortcomings of
products/services, influencing potential customers' purchasing decisions.
However, the proliferation of non-credible reviews -- either fake (promoting/
demoting an item), incompetent (involving irrelevant aspects), or biased --
entails the problem of identifying credible reviews. Prior works involve
classifiers harnessing rich information about items/users -- which might not be
readily available in several domains -- that provide only limited
interpretability as to why a review is deemed non-credible. This paper presents
a novel approach to address the above issues. We utilize latent topic models
leveraging review texts, item ratings, and timestamps to derive consistency
features without relying on item/user histories, unavailable for "long-tail"
items/users. We develop models, for computing review credibility scores to
provide interpretable evidence for non-credible reviews, that are also
transferable to other domains -- addressing the scarcity of labeled data.
Experiments on real-world datasets demonstrate improvements over
state-of-the-art baselines.
Towards Applying the OPRA Theory to Shape Similarity
Authors: Christopher H. Dorr, Reinhard Moratz
Date: 2017-05-07 00:00:00

The motivation for using qualitative shape descriptions is as follows:
qualitative shape descriptions can implicitly act as a schema for measuring the
similarity of shapes, which has the potential to be cognitively adequate. Then,
shapes which are similar to each other would also be similar for a pattern
recognition algorithm. There is substantial work in pattern recognition and
computer vision dealing with shape similarity. Here with our approach to
qualitative shape descriptions and shape similarity, the focus is on achieving
a representation using only simple predicates that a human could even apply
without computer support.
DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout
Authors: Davide Bacciu, Francesco Crecchi, Davide Morelli
Date: 2017-05-07 00:00:00

The paper presents a novel, principled approach to train recurrent neural
networks from the Reservoir Computing family that are robust to missing part of
the input features at prediction time. By building on the ensembling properties
of Dropout regularization, we propose a methodology, named DropIn, which
efficiently trains a neural model as a committee machine of subnetworks, each
capable of predicting with a subset of the original input features. We discuss
the application of the DropIn methodology in the context of Reservoir Computing
models and targeting applications characterized by input sources that are
unreliable or prone to be disconnected, such as in pervasive wireless sensor
networks and ambient intelligence. We provide an experimental assessment using
real-world data from such application domains, showing how the Dropin
methodology allows to maintain predictive performances comparable to those of a
model without missing features, even when 20\%-50\% of the inputs are not
available.
Learning of Gaussian Processes in Distributed and Communication Limited Systems
Authors: Mostafa Tavassolipour, Seyed Abolfazl Motahari, Mohammad-Taghi Manzuri Shalmani
Date: 2017-05-07 00:00:00
Tasks: Gaussian Processes, Quantization

It is of fundamental importance to find algorithms obtaining optimal
performance for learning of statistical models in distributed and communication
limited systems. Aiming at characterizing the optimal strategies, we consider
learning of Gaussian Processes (GPs) in distributed systems as a pivotal
example. We first address a very basic problem: how many bits are required to
estimate the inner-products of Gaussian vectors across distributed machines?
Using information theoretic bounds, we obtain an optimal solution for the
problem which is based on vector quantization. Two suboptimal and more
practical schemes are also presented as substitute for the vector quantization
scheme. In particular, it is shown that the performance of one of the practical
schemes which is called per-symbol quantization is very close to the optimal
one. Schemes provided for the inner-product calculations are incorporated into
our proposed distributed learning methods for GPs. Experimental results show
that with spending few bits per symbol in our communication scheme, our
proposed methods outperform previous zero rate distributed GP learning schemes
such as Bayesian Committee Model (BCM) and Product of experts (PoE).
Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently
Authors: Muthuraman Chidambaram, Yanjun Qi
Date: 2017-02-22 00:00:00
Tasks: Attribute, Style Transfer

The idea of style transfer has largely only been explored in image-based
tasks, which we attribute in part to the specific nature of loss functions used
for style transfer. We propose a general formulation of style transfer as an
extension of generative adversarial networks, by using a discriminator to
regularize a generator with an otherwise separate loss function. We apply our
approach to the task of learning to play chess in the style of a specific
player, and present empirical evidence for the viability of our approach.
A New Medical Diagnosis Method Based on Z-Numbers
Authors: Dong Wu, Xiang Liu, Feng Xue, Hanqing Zheng, Yehang Shou, Wen Jiang
Date: 2017-05-07 00:00:00
Tasks: Decision Making, Medical Diagnosis

How to handle uncertainty in medical diagnosis is an open issue. In this
paper, a new decision making methodology based on Z-numbers is presented.
Firstly, the experts' opinions are represented by Z-numbers. Z-number is an
ordered pair of fuzzy numbers denoted as Z = (A, B). Then, a new method for
ranking fuzzy numbers is proposed. And based on the proposed fuzzy number
ranking method, a novel method is presented to transform the Z-numbers into
Basic Probability Assignment (BPA). As a result, the information from different
sources is combined by the Dempster' combination rule. The final decision
making is more reasonable due to the advantage of information fusion. Finally,
two experiments, risk analysis and medical diagnosis, are illustrated to show
the efficiency of the proposed methodology.
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding
Authors: Yawen Huang, Ling Shao, Alejandro F. Frangi
Date: 2017-05-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Dictionary Learning, Image Generation, Super-Resolution

Magnetic Resonance Imaging (MRI) offers high-resolution \emph{in vivo}
imaging and rich functional and anatomical multimodality tissue contrast. In
practice, however, there are challenges associated with considerations of
scanning costs, patient comfort, and scanning time that constrain how much data
can be acquired in clinical or research studies. In this paper, we explore the
possibility of generating high-resolution and multimodal images from
low-resolution single-modality imagery. We propose the weakly-supervised joint
convolutional sparse coding to simultaneously solve the problems of
super-resolution (SR) and cross-modality image synthesis. The learning process
requires only a few registered multimodal image pairs as the training set.
Additionally, the quality of the joint dictionary learning can be improved
using a larger set of unpaired images. To combine unpaired data from different
image resolutions/modalities, a hetero-domain image alignment term is proposed.
Local image neighborhoods are naturally preserved by operating on the whole
image domain (as opposed to image patches) and using joint convolutional sparse
coding. The paired images are enhanced in the joint learning process with
unpaired data and an additional maximum mean discrepancy term, which minimizes
the dissimilarity between their feature distributions. Experiments show that
the proposed method outperforms state-of-the-art techniques on both SR
reconstruction and simultaneous SR and cross-modality synthesis.
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA
Authors: Xin-Yu Zhang, Srinjoy Das, Ojash Neopane, Ken Kreutz-Delgado
Date: 2017-05-07 00:00:00
Tasks: CPU, Denoising, General Classification, Generative Adversarial Network, GPU, image-classification, Image Classification, Image Denoising, Scene Segmentation, speech-recognition, Speech Recognition, Super-Resolution

In recent years deep learning algorithms have shown extremely high
performance on machine learning tasks such as image classification and speech
recognition. In support of such applications, various FPGA accelerator
architectures have been proposed for convolutional neural networks (CNNs) that
enable high performance for classification tasks at lower power than CPU and
GPU processors. However, to date, there has been little research on the use of
FPGA implementations of deconvolutional neural networks (DCNNs). DCNNs, also
known as generative CNNs, encode high-dimensional probability distributions and
have been widely used for computer vision applications such as scene
completion, scene segmentation, image creation, image denoising, and
super-resolution imaging. We propose an FPGA architecture for deconvolutional
networks built around an accelerator which effectively handles the complex
memory access patterns needed to perform strided deconvolutions, and that
supports convolution as well. We also develop a three-step design optimization
method that systematically exploits statistical analysis, design space
exploration and VLSI optimization. To verify our FPGA deconvolutional
accelerator design methodology we train DCNNs offline on two representative
datasets using the generative adversarial network method (GAN) run on
Tensorflow, and then map these DCNNs to an FPGA DCNN-plus-accelerator
implementation to perform generative inference on a Xilinx Zynq-7000 FPGA. Our
DCNN implementation achieves a peak performance density of 0.012 GOPs/DSP.
Learning Discriminative Relational Features for Sequence Labeling
Authors: Naveen Nair, Ajay Nagesh, Ganesh Ramakrishnan
Date: 2017-05-07 00:00:00

Discovering relational structure between input features in sequence labeling
models has shown to improve their accuracy in several problem settings.
However, the search space of relational features is exponential in the number
of basic input features. Consequently, approaches that learn relational
features, tend to follow a greedy search strategy. In this paper, we study the
possibility of optimally learning and applying discriminative relational
features for sequence labeling. For learning features derived from inputs at a
particular sequence position, we propose a Hierarchical Kernels-based approach
(referred to as Hierarchical Kernel Learning for Structured Output Spaces -
StructHKL). This approach optimally and efficiently explores the hierarchical
structure of the feature space for problems with structured output spaces such
as sequence labeling. Since the StructHKL approach has limitations in learning
complex relational features derived from inputs at relative positions, we
propose two solutions to learn relational features namely, (i) enumerating
simple component features of complex relational features and discovering their
compositions using StructHKL and (ii) leveraging relational kernels, that
compute the similarity between instances implicitly, in the sequence labeling
problem. We perform extensive empirical evaluation on publicly available
datasets and record our observations on settings in which certain approaches
are effective.
Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks
Authors: Yifan Liu, Zengchang Qin, Zhenbo Luo, Hua Wang
Date: 2017-05-04 00:00:00
Tasks: Image Generation

Recently, realistic image generation using deep neural networks has become a
hot topic in machine learning and computer vision. Images can be generated at
the pixel level by learning from a large collection of images. Learning to
generate colorful cartoon images from black-and-white sketches is not only an
interesting research problem, but also a potential application in digital
entertainment. In this paper, we investigate the sketch-to-image synthesis
problem by using conditional generative adversarial networks (cGAN). We propose
the auto-painter model which can automatically generate compatible colors for a
sketch. The new model is not only capable of painting hand-draw sketch with
proper colors, but also allowing users to indicate preferred colors.
Experimental results on two sketch datasets show that the auto-painter performs
better that existing image-to-image methods.
Experimental results : Reinforcement Learning of POMDPs using Spectral Methods
Authors: Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
Date: 2017-05-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a new reinforcement learning algorithm for partially observable
Markov decision processes (POMDP) based on spectral decomposition methods.
While spectral methods have been previously employed for consistent learning of
(passive) latent variable models such as hidden Markov models, POMDPs are more
challenging since the learner interacts with the environment and possibly
changes the future observations in the process. We devise a learning algorithm
running through epochs, in each epoch we employ spectral techniques to learn
the POMDP parameters from a trajectory generated by a fixed policy. At the end
of the epoch, an optimization oracle returns the optimal memoryless planning
policy which maximizes the expected reward based on the estimated POMDP model.
We prove an order-optimal regret bound with respect to the optimal memoryless
policy and efficient scaling with respect to the dimensionality of observation
and action spaces.
Information Theoretic Limits for Linear Prediction with Graph-Structured Sparsity
Authors: Adarsh Barik, Jean Honorio, Mohit Tawarmalani
Date: 2017-01-26 00:00:00
Tasks: General Classification, regression

We analyze the necessary number of samples for sparse vector recovery in a
noisy linear prediction setup. This model includes problems such as linear
regression and classification. We focus on structured graph models. In
particular, we prove that sufficient number of samples for the weighted graph
model proposed by Hegde and others is also necessary. We use the Fano's
inequality on well constructed ensembles as our main tool in establishing
information theoretic lower bounds.
Encapsulating models and approximate inference programs in probabilistic modules
Authors: Marco F. Cusumano-Towner, Vikash K. Mansinghka
Date: 2016-12-14 00:00:00

This paper introduces the probabilistic module interface, which allows
encapsulation of complex probabilistic models with latent variables alongside
custom stochastic approximate inference machinery, and provides a
platform-agnostic abstraction barrier separating the model internals from the
host probabilistic inference system. The interface can be seen as a stochastic
generalization of a standard simulation and density interface for probabilistic
primitives. We show that sound approximate inference algorithms can be
constructed for networks of probabilistic modules, and we demonstrate that the
interface can be implemented using learned stochastic inference networks and
MCMC and SMC approximate inference programs.
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming
Authors: Marco F. Cusumano-Towner, Vikash K. Mansinghka
Date: 2016-12-07 00:00:00
Tasks: Probabilistic Programming

A key limitation of sampling algorithms for approximate inference is that it
is difficult to quantify their approximation error. Widely used sampling
schemes, such as sequential importance sampling with resampling and
Metropolis-Hastings, produce output samples drawn from a distribution that may
be far from the target posterior distribution. This paper shows how to
upper-bound the symmetric KL divergence between the output distribution of a
broad class of sequential Monte Carlo (SMC) samplers and their target posterior
distributions, subject to assumptions about the accuracy of a separate
gold-standard sampler. The proposed method applies to samplers that combine
multiple particles, multinomial resampling, and rejuvenation kernels. The
experiments show the technique being used to estimate bounds on the divergence
of SMC samplers for posterior inference in a Bayesian linear regression model
and a Dirichlet process mixture model.
Adversarial Training Methods for Semi-Supervised Text Classification
Authors: Takeru Miyato, Andrew M. Dai, Ian Goodfellow
Date: 2016-05-25 00:00:00
Tasks: Classification, General Classification, Semi-Supervised Text Classification, Sentiment Analysis, Text Classification, Word Embeddings

Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting. However, both methods require making small perturbations to numerous entries of the input vector, which is inappropriate for sparse high-dimensional inputs such as one-hot word representations. We extend adversarial and virtual adversarial training to the text domain by applying perturbations to the word embeddings in a recurrent neural network rather than to the original input itself. The proposed method achieves state of the art results on multiple benchmark semi-supervised and purely supervised tasks. We provide visualizations and analysis showing that the learned word embeddings have improved in quality and that while training, the model is less prone to overfitting. Code is available at https://github.com/tensorflow/models/tree/master/research/adversarial_text.
People on Drugs: Credibility of User Statements in Health Communities
Authors: Subhabrata Mukherjee, Gerhard Weikum, Cristian Danescu-Niculescu-Mizil
Date: 2017-05-06 00:00:00
Tasks: Misinformation

Online health communities are a valuable source of information for patients
and physicians. However, such user-generated resources are often plagued by
inaccuracies and misinformation. In this work we propose a method for
automatically establishing the credibility of user-generated medical statements
and the trustworthiness of their authors by exploiting linguistic cues and
distant supervision from expert sources. To this end we introduce a
probabilistic graphical model that jointly learns user trustworthiness,
statement credibility, and language objectivity. We apply this methodology to
the task of extracting rare or unknown side-effects of medical drugs --- this
being one of the problems where large scale non-expert data has the potential
to complement expert medical knowledge. We show that our method can reliably
extract side-effects and filter out false statements, while identifying
trustworthy users that are likely to contribute valuable medical information.
Item Recommendation with Evolving User Preferences and Experience
Authors: Subhabrata Mukherjee, Hemank Lamba, Gerhard Weikum
Date: 2017-05-06 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Current recommender systems exploit user and item similarities by
collaborative filtering. Some advanced methods also consider the temporal
evolution of item ratings as a global background process. However, all prior
methods disregard the individual evolution of a user's experience level and how
this is expressed in the user's writing in a review community. In this paper,
we model the joint evolution of user experience, interest in specific item
facets, writing style, and rating behavior. This way we can generate individual
recommendations that take into account the user's maturity level (e.g.,
recommending art movies rather than blockbusters for a cinematography expert).
As only item ratings and review texts are observables, we capture the user's
experience and interests in a latent model learned from her reviews, vocabulary
and writing style. We develop a generative HMM-LDA model to trace user
evolution, where the Hidden Markov Model (HMM) traces her latent experience
progressing over time -- with solely user reviews and ratings as observables
over time. The facets of a user's interest are drawn from a Latent Dirichlet
Allocation (LDA) model derived from her reviews, as a function of her (again
latent) experience level. In experiments with five real-world datasets, we show
that our model improves the rating prediction over state-of-the-art baselines,
by a substantial margin. We also show, in a use-case study, that our model
performs well in the assessment of user experience levels.