pwc-archive/papers-with-abstracts huggingface dataset:
Using Deep Q-Learning to Control Optimization Hyperparameters
Authors: Samantha Hansen
Date: 2016-02-12 00:00:00
Tasks: Q-Learning, Reinforcement Learning

We present a novel definition of the reinforcement learning state, actions
and reward function that allows a deep Q-network (DQN) to learn to control an
optimization hyperparameter. Using Q-learning with experience replay, we train
two DQNs to accept a state representation of an objective function as input and
output the expected discounted return of rewards, or q-values, connected to the
actions of either adjusting the learning rate or leaving it unchanged. The two
DQNs learn a policy similar to a line search, but differ in the number of
allowed actions. The trained DQNs in combination with a gradient-based update
routine form the basis of the Q-gradient descent algorithms. To demonstrate the
viability of this framework, we show that the DQN's q-values associated with
optimal action converge and that the Q-gradient descent algorithms outperform
gradient descent with an Armijo or nonmonotone line search. Unlike traditional
optimization methods, Q-gradient descent can incorporate any objective
statistic and by varying the actions we gain insight into the type of learning
rate adjustment strategies that are successful for neural network optimization.
Preserving Color in Neural Artistic Style Transfer
Authors: Leon A. Gatys, Matthias Bethge, Aaron Hertzmann, Eli Shechtman
Date: 2016-06-19 00:00:00
Tasks: Style Transfer

This note presents an extension to the neural artistic style transfer
algorithm (Gatys et al.). The original algorithm transforms an image to have
the style of another given image. For example, a photograph can be transformed
to have the style of a famous painting. Here we address a potential shortcoming
of the original method: the algorithm transfers the colors of the original
painting, which can alter the appearance of the scene in undesirable ways. We
describe simple linear methods for transferring style while preserving colors.
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation
Authors: Akash Srivastava, James Zou, Ryan P. Adams, Charles Sutton
Date: 2016-06-19 00:00:00
Tasks: Clustering

A good clustering can help a data analyst to explore and understand a data
set, but what constitutes a good clustering may depend on domain-specific and
application-specific criteria. These criteria can be difficult to formalize,
even when it is easy for an analyst to know a good clustering when they see
one. We present a new approach to interactive clustering for data exploration
called TINDER, based on a particularly simple feedback mechanism, in which an
analyst can reject a given clustering and request a new one, which is chosen to
be different from the previous clustering while fitting the data well. We
formalize this interaction in a Bayesian framework as a method for prior
elicitation, in which each different clustering is produced by a prior
distribution that is modified to discourage previously rejected clusterings. We
show that TINDER successfully produces a diverse set of clusterings, each of
equivalent quality, that are much more diverse than would be obtained by
randomized restarts.
Tight Performance Bounds for Compressed Sensing With Conventional and Group Sparsity
Authors: Shashank Ranjan, Mathukumalli Vidyasagar
Date: 2016-06-19 00:00:00
Tasks: compressed sensing

In this paper, we study the problem of recovering a group sparse vector from
a small number of linear measurements. In the past the common approach has been
to use various "group sparsity-inducing" norms such as the Group LASSO norm for
this purpose. By using the theory of convex relaxations, we show that it is
also possible to use $\ell_1$-norm minimization for group sparse recovery. We
introduce a new concept called group robust null space property (GRNSP), and
show that, under suitable conditions, a group version of the restricted
isometry property (GRIP) implies the GRNSP, and thus leads to group sparse
recovery. When all groups are of equal size, our bounds are less conservative
than known bounds. Moreover, our results apply even to situations where where
the groups have different sizes. When specialized to conventional sparsity, our
bounds reduce to one of the well-known "best possible" conditions for sparse
recovery. This relationship between GRNSP and GRIP is new even for conventional
sparsity, and substantially streamlines the proofs of some known results. Using
this relationship, we derive bounds on the $\ell_p$-norm of the residual error
vector for all $p \in [1,2]$, and not just when $p = 2$. When the measurement
matrix consists of random samples of a sub-Gaussian random variable, we present
bounds on the number of measurements, which are less conservative than
currently known bounds.
$\ell_p$-Box ADMM: A Versatile Framework for Integer Programming
Authors: Baoyuan Wu, Bernard Ghanem
Date: 2016-04-26 00:00:00
Tasks: Clustering, Graph Matching

This paper revisits the integer programming (IP) problem, which plays a
fundamental role in many computer vision and machine learning applications. The
literature abounds with many seminal works that address this problem, some
focusing on continuous approaches (e.g. linear program relaxation) while others
on discrete ones (e.g., min-cut). However, a limited number of them are
designed to handle the general IP form and even these methods cannot adequately
satisfy the simultaneous requirements of accuracy, feasibility, and
scalability. To this end, we propose a novel and versatile framework called
$\ell_p$-box ADMM, which is based on two parts. (1) The discrete constraint is
equivalently replaced by the intersection of a box and a $(n-1)$-dimensional
sphere (defined through the $\ell_p$ norm). (2) We infuse this equivalence into
the ADMM (Alternating Direction Method of Multipliers) framework to handle
these continuous constraints separately and to harness its attractive
properties. More importantly, the ADMM update steps can lead to manageable
sub-problems in the continuous domain. To demonstrate its efficacy, we consider
an instance of the framework, namely $\ell_2$-box ADMM applied to binary
quadratic programming (BQP). Here, the ADMM steps are simple, computationally
efficient, and theoretically guaranteed to converge to a KKT point. We
demonstrate the applicability of $\ell_2$-box ADMM on three important
applications: MRF energy minimization, graph matching, and clustering. Results
clearly show that it significantly outperforms existing generic IP solvers both
in runtime and objective. It also achieves very competitive performance vs.
state-of-the-art methods specific to these applications.
Deep Learning in Bioinformatics
Authors: Seonwoo Min, Byunghan Lee, Sungroh Yoon
Date: 2016-03-21 00:00:00
Tasks: Deep Learning

In the era of big data, transformation of biomedical big data into valuable
knowledge has been one of the most important challenges in bioinformatics. Deep
learning has advanced rapidly since the early 2000s and now demonstrates
state-of-the-art performance in various fields. Accordingly, application of
deep learning in bioinformatics to gain insight from data has been emphasized
in both academia and industry. Here, we review deep learning in bioinformatics,
presenting examples of current research. To provide a useful and comprehensive
perspective, we categorize research both by the bioinformatics domain (i.e.,
omics, biomedical imaging, biomedical signal processing) and deep learning
architecture (i.e., deep neural networks, convolutional neural networks,
recurrent neural networks, emergent architectures) and present brief
descriptions of each study. Additionally, we discuss theoretical and practical
issues of deep learning in bioinformatics and suggest future research
directions. We believe that this review will provide valuable insights and
serve as a starting point for researchers to apply deep learning approaches in
their bioinformatics studies.
Statistical Parametric Speech Synthesis Using Bottleneck Representation From Sequence Auto-encoder
Authors: Sivanand Achanta, KNRK Raju Alluri, Suryakanth V. Gangashetty
Date: 2016-06-19 00:00:00
Tasks: Speech Synthesis

In this paper, we describe a statistical parametric speech synthesis approach
with unit-level acoustic representation. In conventional deep neural network
based speech synthesis, the input text features are repeated for the entire
duration of phoneme for mapping text and speech parameters. This mapping is
learnt at the frame-level which is the de-facto acoustic representation.
However much of this computational requirement can be drastically reduced if
every unit can be represented with a fixed-dimensional representation. Using
recurrent neural network based auto-encoder, we show that it is indeed possible
to map units of varying duration to a single vector. We then use this acoustic
representation at unit-level to synthesize speech using deep neural network
based statistical parametric speech synthesis technique. Results show that the
proposed approach is able to synthesize at the same quality as the conventional
frame based approach at a highly reduced computational cost.
Can Machine Generate Traditional Chinese Poetry? A Feigenbaum Test
Authors: Qixin Wang, Tianyi Luo, Dong Wang
Date: 2016-06-19 00:00:00
Tasks: Game of Go

Recent progress in neural learning demonstrated that machines can do well in
regularized tasks, e.g., the game of Go. However, artistic activities such as
poem generation are still widely regarded as human's special capability. In
this paper, we demonstrate that a simple neural model can imitate human in some
tasks of art generation. We particularly focus on traditional Chinese poetry,
and show that machines can do as well as many contemporary poets and weakly
pass the Feigenbaum Test, a variant of Turing test in professional domains. Our
method is based on an attention-based recurrent neural network, which accepts a
set of keywords as the theme and generates poems by looking at each keyword
during the generation. A number of techniques are proposed to improve the
model, including character vector initialization, attention to input and
hybrid-style training. Compared to existing poetry generation methods, our
model can generate much more theme-consistent and semantic-rich poems.
Building an Interpretable Recommender via Loss-Preserving Transformation
Authors: Amit Dhurandhar, Sechan Oh, Marek Petrik
Date: 2016-06-19 00:00:00
Tasks: Classification, General Classification, Multi-class Classification, Recommendation Systems

We propose a method for building an interpretable recommender system for
personalizing online content and promotions. Historical data available for the
system consists of customer features, provided content (promotions), and user
responses. Unlike in a standard multi-class classification setting,
misclassification costs depend on both recommended actions and customers. Our
method transforms such a data set to a new set which can be used with standard
interpretable multi-class classification algorithms. The transformation has the
desirable property that minimizing the standard misclassification penalty in
this new space is equivalent to minimizing the custom cost function.
Eye Tracking for Everyone
Authors: Kyle Krafka, Aditya Khosla, Petr Kellnhofer, Harini Kannan, Suchendra Bhandarkar, Wojciech Matusik, Antonio Torralba
Date: 2016-06-18 00:00:00
Proceeding: CVPR 2016 6
Tasks: Gaze Estimation

From scientific research to commercial applications, eye tracking is an
important tool across many domains. Despite its range of applications, eye
tracking has yet to become a pervasive technology. We believe that we can put
the power of eye tracking in everyone's palm by building eye tracking software
that works on commodity hardware such as mobile phones and tablets, without the
need for additional sensors or devices. We tackle this problem by introducing
GazeCapture, the first large-scale dataset for eye tracking, containing data
from over 1450 people consisting of almost 2.5M frames. Using GazeCapture, we
train iTracker, a convolutional neural network for eye tracking, which achieves
a significant reduction in error over previous approaches while running in real
time (10-15fps) on a modern mobile device. Our model achieves a prediction
error of 1.71cm and 2.53cm without calibration on mobile phones and tablets
respectively. With calibration, this is reduced to 1.34cm and 2.12cm. Further,
we demonstrate that the features learned by iTracker generalize well to other
datasets, achieving state-of-the-art results. The code, data, and models are
available at http://gazecapture.csail.mit.edu.
Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models
Authors: Jascha Sohl-Dickstein, Diederik P. Kingma
Date: 2015-04-29 00:00:00
Tasks: Time Series, Time Series Analysis

We observe that the standard log likelihood training objective for a
Recurrent Neural Network (RNN) model of time series data is equivalent to a
variational Bayesian training objective, given the proper choice of generative
and inference models. This perspective may motivate extensions to both RNNs and
variational Bayesian models. We propose one such extension, where multiple
particles are used for the hidden state of an RNN, allowing a natural
representation of uncertainty or multimodality.
Experiments on Parallel Training of Deep Neural Network using Model Averaging
Authors: Hang Su, Haoyu Chen
Date: 2015-07-05 00:00:00

In this work we apply model averaging to parallel training of deep neural
network (DNN). Parallelization is done in a model averaging manner. Data is
partitioned and distributed to different nodes for local model updates, and
model averaging across nodes is done every few minibatches. We use multiple
GPUs for data parallelization, and Message Passing Interface (MPI) for
communication between nodes, which allows us to perform model averaging
frequently without losing much time on communication. We investigate the
effectiveness of Natural Gradient Stochastic Gradient Descent (NG-SGD) and
Restricted Boltzmann Machine (RBM) pretraining for parallel training in
model-averaging framework, and explore the best setups in term of different
learning rate schedules, averaging frequencies and minibatch sizes. It is shown
that NG-SGD and RBM pretraining benefits parameter-averaging based model
training. On the 300h Switchboard dataset, a 9.3 times speedup is achieved
using 16 GPUs and 17 times speedup using 32 GPUs with limited decoding accuracy
loss.
Interpretable Two-level Boolean Rule Learning for Classification
Authors: Guolong Su, Dennis Wei, Kush R. Varshney, Dmitry M. Malioutov
Date: 2016-06-18 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Form, General Classification, Interpretable Machine Learning, Vocal Bursts Valence Prediction

As a contribution to interpretable machine learning research, we develop a
novel optimization framework for learning accurate and sparse two-level Boolean
rules. We consider rules in both conjunctive normal form (AND-of-ORs) and
disjunctive normal form (OR-of-ANDs). A principled objective function is
proposed to trade classification accuracy and interpretability, where we use
Hamming loss to characterize accuracy and sparsity to characterize
interpretability. We propose efficient procedures to optimize these objectives
based on linear programming (LP) relaxation, block coordinate descent, and
alternating minimization. Experiments show that our new algorithms provide very
good tradeoffs between accuracy and interpretability.
Automatic 3D Reconstruction for Symmetric Shapes
Authors: Atishay Jain
Date: 2016-06-18 00:00:00
Tasks: 3D Reconstruction

Generic 3D reconstruction from a single image is a difficult problem. A lot
of data loss occurs in the projection. A domain based approach to
reconstruction where we solve a smaller set of problems for a particular use
case lead to greater returns. The project provides a way to automatically
generate full 3-D renditions of actual symmetric images that have some prior
information provided in the pipeline by a recognition algorithm. We provide a
critical analysis on how this can be enhanced and improved to provide a general
reconstruction framework for automatic reconstruction for any symmetric shape.
Conditional Image Generation with PixelCNN Decoders
Authors: Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu
Date: 2016-06-16 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Conditional Image Generation, Decoder, Density Estimation, Descriptive, Image Generation

This work explores conditional image generation with a new image density
model based on the PixelCNN architecture. The model can be conditioned on any
vector, including descriptive labels or tags, or latent embeddings created by
other networks. When conditioned on class labels from the ImageNet database,
the model is able to generate diverse, realistic scenes representing distinct
animals, objects, landscapes and structures. When conditioned on an embedding
produced by a convolutional network given a single image of an unseen face, it
generates a variety of new portraits of the same person with different facial
expressions, poses and lighting conditions. We also show that conditional
PixelCNN can serve as a powerful decoder in an image autoencoder. Additionally,
the gated convolutional layers in the proposed model improve the log-likelihood
of PixelCNN to match the state-of-the-art performance of PixelRNN on ImageNet,
with greatly reduced computational cost.
Online and Offline Handwritten Chinese Character Recognition: A Comprehensive Study and New Benchmark
Authors: Xu-Yao Zhang, Yoshua Bengio, Cheng-Lin Liu
Date: 2016-06-18 00:00:00
Tasks: Data Augmentation, Offline Handwritten Chinese Character Recognition

Recent deep learning based methods have achieved the state-of-the-art
performance for handwritten Chinese character recognition (HCCR) by learning
discriminative representations directly from raw data. Nevertheless, we believe
that the long-and-well investigated domain-specific knowledge should still help
to boost the performance of HCCR. By integrating the traditional
normalization-cooperated direction-decomposed feature map (directMap) with the
deep convolutional neural network (convNet), we are able to obtain new highest
accuracies for both online and offline HCCR on the ICDAR-2013 competition
database. With this new framework, we can eliminate the needs for data
augmentation and model ensemble, which are widely used in other systems to
achieve their best results. This makes our framework to be efficient and
effective for both training and testing. Furthermore, although
directMap+convNet can achieve the best results and surpass human-level
performance, we show that writer adaptation in this case is still effective. A
new adaptation layer is proposed to reduce the mismatch between training and
test data on a particular source layer. The adaptation process can be
efficiently and effectively implemented in an unsupervised manner. By adding
the adaptation layer into the pre-trained convNet, it can adapt to the new
handwriting styles of particular writers, and the recognition accuracy can be
further improved consistently and significantly. This paper gives an overview
and comparison of recent deep learning based approaches for HCCR, and also sets
new benchmarks for both online and offline HCCR.
Egyptian Arabic to English Statistical Machine Translation System for NIST OpenMT'2015
Authors: Hassan Sajjad, Nadir Durrani, Francisco Guzman, Preslav Nakov, Ahmed Abdelali, Stephan Vogel, Wael Salloum, Ahmed El Kholy, Nizar Habash
Date: 2016-06-18 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation, Transliteration

The paper describes the Egyptian Arabic-to-English statistical machine
translation (SMT) system that the QCRI-Columbia-NYUAD (QCN) group submitted to
the NIST OpenMT'2015 competition. The competition focused on informal dialectal
Arabic, as used in SMS, chat, and speech. Thus, our efforts focused on
processing and standardizing Arabic, e.g., using tools such as 3arrib and
MADAMIRA. We further trained a phrase-based SMT system using state-of-the-art
features and components such as operation sequence model, class-based language
model, sparse features, neural network joint model, genre-based
hierarchically-interpolated language model, unsupervised transliteration
mining, phrase-table merging, and hypothesis combination. Our system ranked
second on all three genres.
Discovery and Visualization of Nonstationary Causal Models
Authors: Kun Zhang, Biwei Huang, Jiji Zhang, Bernhard Schölkopf, Clark Glymour
Date: 2015-09-27 00:00:00
Tasks: Causal Discovery

It is commonplace to encounter nonstationary data, of which the underlying
generating process may change over time or across domains. The nonstationarity
presents both challenges and opportunities for causal discovery. In this paper
we propose a principled framework to handle nonstationarity, and develop some
methods to address three important questions. First, we propose an enhanced
constraint-based method to detect variables whose local mechanisms are
nonstationary and recover the skeleton of the causal structure over observed
variables. Second, we present a way to determine some causal directions by
taking advantage of information carried by changing distributions. Third, we
develop a method for visualizing the nonstationarity of causal modules.
Experimental results on various synthetic and real-world data sets are
presented to demonstrate the efficacy of our methods.
An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels
Authors: Amirhossein Akbarnejad, Mahdieh Soleymani Baghshah
Date: 2016-06-18 00:00:00
Tasks: Dimensionality Reduction, Missing Labels, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-label classification has received considerable interest in recent
years. Multi-label classifiers have to address many problems including:
handling large-scale datasets with many instances and a large set of labels,
compensating missing label assignments in the training set, considering
correlations between labels, as well as exploiting unlabeled data to improve
prediction performance. To tackle datasets with a large set of labels,
embedding-based methods have been proposed which seek to represent the label
assignments in a low-dimensional space. Many state-of-the-art embedding-based
methods use a linear dimensionality reduction to represent the label
assignments in a low-dimensional space. However, by doing so, these methods
actually neglect the tail labels - labels that are infrequently assigned to
instances. We propose an embedding-based method that non-linearly embeds the
label vectors using an stochastic approach, thereby predicting the tail labels
more accurately. Moreover, the proposed method have excellent mechanisms for
handling missing labels, dealing with large-scale datasets, as well as
exploiting unlabeled data. With the best of our knowledge, our proposed method
is the first multi-label classifier that simultaneously addresses all of the
mentioned challenges. Experiments on real-world datasets show that our method
outperforms stateof-the-art multi-label classifiers by a large margin, in terms
of prediction performance, as well as training time.