pwc-archive/papers-with-abstracts huggingface dataset:
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU
Authors: Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz
Date: 2016-11-18 00:00:00
Tasks: CPU, GPU, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

We introduce a hybrid CPU/GPU version of the Asynchronous Advantage
Actor-Critic (A3C) algorithm, currently the state-of-the-art method in
reinforcement learning for various gaming tasks. We analyze its computational
traits and concentrate on aspects critical to leveraging the GPU's
computational power. We introduce a system of queues and a dynamic scheduling
strategy, potentially helpful for other asynchronous algorithms as well. Our
hybrid CPU/GPU version of A3C, based on TensorFlow, achieves a significant
speed up compared to a CPU implementation; we make it publicly available to
other researchers at https://github.com/NVlabs/GA3C .
How to Escape Saddle Points Efficiently
Authors: Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M. Kakade, Michael. I. Jordan
Date: 2017-03-02 00:00:00
Proceeding: ICML 2017 8

This paper shows that a perturbed form of gradient descent converges to a
second-order stationary point in a number iterations which depends only
poly-logarithmically on dimension (i.e., it is almost "dimension-free"). The
convergence rate of this procedure matches the well-known convergence rate of
gradient descent to first-order stationary points, up to log factors. When all
saddle points are non-degenerate, all second-order stationary points are local
minima, and our result thus shows that perturbed gradient descent can escape
saddle points almost for free. Our results can be directly applied to many
machine learning applications, including deep learning. As a particular
concrete example of such an application, we show that our results can be used
directly to establish sharp global convergence rates for matrix factorization.
Our results rely on a novel characterization of the geometry around saddle
points, which may be of independent interest to the non-convex optimization
community.
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
Authors: Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov, Frank Wood
Date: 2017-03-02 00:00:00

We draw a formal connection between using synthetic training data to optimize
neural network parameters and approximate, Bayesian, model-based reasoning. In
particular, training a neural network using synthetic data can be viewed as
learning a proposal distribution generator for approximate inference in the
synthetic-data generative model. We demonstrate this connection in a
recognition task where we develop a novel Captcha-breaking architecture and
train it using synthetic data, demonstrating both state-of-the-art performance
and a way of computing task-specific posterior uncertainty. Using a neural
network trained this way, we also demonstrate successful breaking of real-world
Captchas currently used by Facebook and Wikipedia. Reasoning from these
empirical results and drawing connections with Bayesian modeling, we discuss
the robustness of synthetic data results and suggest important considerations
for ensuring good neural network generalization when training with synthetic
data.
Inference Compilation and Universal Probabilistic Programming
Authors: Tuan Anh Le, Atilim Gunes Baydin, Frank Wood
Date: 2016-10-31 00:00:00
Tasks: Probabilistic Programming

We introduce a method for using deep neural networks to amortize the cost of
inference in models from the family induced by universal probabilistic
programming languages, establishing a framework that combines the strengths of
probabilistic programming and deep learning methods. We call what we do
"compilation of inference" because our method transforms a denotational
specification of an inference problem in the form of a probabilistic program
written in a universal programming language into a trained neural network
denoted in a neural network specification language. When at test time this
neural network is fed observational data and executed, it performs approximate
inference in the original model specified by the probabilistic program. Our
training objective and learning procedure are designed to allow the trained
neural network to be used as a proposal distribution in a sequential importance
sampling inference engine. We illustrate our method on mixture models and
Captcha solving and show significant speedups in the efficiency of inference.
Araguaia Medical Vision Lab at ISIC 2017 Skin Lesion Classification Challenge
Authors: Rafael Teixeira Sousa, Larissa Vasconcellos de Moraes
Date: 2017-03-02 00:00:00
Tasks: General Classification, Lesion Classification, Skin Lesion Classification

This paper describes the participation of Araguaia Medical Vision Lab at the
International Skin Imaging Collaboration 2017 Skin Lesion Challenge. We
describe the use of deep convolutional neural networks in attempt to classify
images of Melanoma and Seborrheic Keratosis lesions. With use of finetuned
GoogleNet and AlexNet we attained results of 0.950 and 0.846 AUC on Seborrheic
Keratosis and Melanoma respectively.
Non-line-of-sight tracking of people at long range
Authors: Susan Chan, Ryan E. Warburton, Genevieve Gariepy, Jonathan Leach, Daniele Faccio
Date: 2017-03-02 00:00:00
Tasks: Autonomous Driving, Position

A remote-sensing system that can determine the position of hidden objects has
applications in many critical real-life scenarios, such as search and rescue
missions and safe autonomous driving. Previous work has shown the ability to
range and image objects hidden from the direct line of sight, employing
advanced optical imaging technologies aimed at small objects at short range. In
this work we demonstrate a long-range tracking system based on single laser
illumination and single-pixel single-photon detection. This enables us to track
one or more people hidden from view at a stand-off distance of over 50~m. These
results pave the way towards next generation LiDAR systems that will
reconstruct not only the direct-view scene but also the main elements hidden
behind walls or corners.
Exact Topology Reconstruction of Radial Dynamical Systems with Applications to Distribution System of the Power Grid
Authors: Saurav Talukdar, Deepjyoti Deka, Donatello Materassi, Murti V. Salapaka
Date: 2017-03-02 00:00:00

In this article we present a method to reconstruct the interconnectedness of
dynamically related stochastic processes, where the interactions are
bi-directional and the underlying topology is a tree. Our approach is based on
multivariate Wiener filtering which recovers spurious edges apart from the true
edges in the topology reconstruction. The main contribution of this work is to
show that all spurious links obtained using Wiener filtering can be eliminated
if the underlying topology is a tree based on which we present a three stage
network reconstruction procedure for trees. We illustrate the effectiveness of
the method developed by applying it on a typical distribution system of the
electric grid.
Towards CNN Map Compression for camera relocalisation
Authors: Luis Contreras, Walterio Mayol-Cuevas
Date: 2017-03-02 00:00:00
Tasks: Camera Relocalization, Position

This paper presents a study on the use of Convolutional Neural Networks for
camera relocalisation and its application to map compression. We follow state
of the art visual relocalisation results and evaluate response to different
data inputs -- namely, depth, grayscale, RGB, spatial position and combinations
of these. We use a CNN map representation and introduce the notion of CNN map
compression by using a smaller CNN architecture. We evaluate our proposal in a
series of publicly available datasets. This formulation allows us to improve
relocalisation accuracy by increasing the number of training trajectories while
maintaining a constant-size CNN.
Learning a Natural Language Interface with Neural Programmer
Authors: Arvind Neelakantan, Quoc V. Le, Martin Abadi, Andrew McCallum, Dario Amodei
Date: 2016-11-28 00:00:00
Tasks: Natural Language Queries, Program induction, Question Answering

Learning a natural language interface for database tables is a challenging
task that involves deep language understanding and multi-step reasoning. The
task is often approached by mapping natural language queries to logical forms
or programs that provide the desired response when executed on the database. To
our knowledge, this paper presents the first weakly supervised, end-to-end
neural network model to induce such programs on a real-world dataset. We
enhance the objective function of Neural Programmer, a neural network with
built-in discrete operations, and apply it on WikiTableQuestions, a natural
language question-answering dataset. The model is trained end-to-end with weak
supervision of question-answer pairs, and does not require domain-specific
grammars, rules, or annotations that are key elements in previous approaches to
program induction. The main experimental result in this paper is that a single
Neural Programmer model achieves 34.2% accuracy using only 10,000 examples with
weak supervision. An ensemble of 15 models, with a trivial combination
technique, achieves 37.7% accuracy, which is competitive to the current
state-of-the-art accuracy of 37.1% obtained by a traditional natural language
semantic parser.
Encrypted accelerated least squares regression
Authors: Pedro M. Esperança, Louis J. M. Aslett, Chris C. Holmes
Date: 2017-03-02 00:00:00
Tasks: regression

Information that is stored in an encrypted format is, by definition, usually
not amenable to statistical analysis or machine learning methods. In this paper
we present detailed analysis of coordinate and accelerated gradient descent
algorithms which are capable of fitting least squares and penalised ridge
regression models, using data encrypted under a fully homomorphic encryption
scheme. Gradient descent is shown to dominate in terms of encrypted
computational speed, and theoretical results are proven to give parameter
bounds which ensure correctness of decryption. The characteristics of encrypted
computation are empirically shown to favour a non-standard acceleration
technique. This demonstrates the possibility of approximating conventional
statistical regression methods using encrypted data without compromising
privacy.
SLIM: Semi-Lazy Inference Mechanism for Plan Recognition
Authors: Retuh Mirsky, Ya'akov, Gal
Date: 2017-03-02 00:00:00

Plan Recognition algorithms require to recognize a complete hierarchy
explaining the agent's actions and goals. While the output of such algorithms
is informative to the recognizer, the cost of its calculation is high in
run-time, space, and completeness. Moreover, performing plan recognition online
requires the observing agent to reason about future actions that have not yet
been seen and maintain a set of hypotheses to support all possible options.
This paper presents a new and efficient algorithm for online plan recognition
called SLIM (Semi-Lazy Inference Mechanism). It combines both a bottom-up and
top-down parsing processes, which allow it to commit only to the minimum
necessary actions in real-time, but still provide complete hypotheses post
factum. We show both theoretically and empirically that although the
computational cost of this process is still exponential, there is a significant
improvement in run-time when compared to a state of the art of plan recognition
algorithm.
Reinforcement Learning With Temporal Logic Rewards
Authors: Xiao Li, Cristian-Ioan Vasile, Calin Belta
Date: 2016-12-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) depends critically on the choice of reward
functions used to capture the de- sired behavior and constraints of a robot.
Usually, these are handcrafted by a expert designer and represent heuristics
for relatively simple tasks. Real world applications typically involve more
complex tasks with rich temporal and logical structure. In this paper we take
advantage of the expressive power of temporal logic (TL) to specify complex
rules the robot should follow, and incorporate domain knowledge into learning.
We propose Truncated Linear Temporal Logic (TLTL) as specifications language,
that is arguably well suited for the robotics applications, together with
quantitative semantics, i.e., robustness degree. We propose a RL approach to
learn tasks expressed as TLTL formulae that uses their associated robustness
degree as reward functions, instead of the manually crafted heuristics trying
to capture the same specifications. We show in simulated trials that learning
is faster and policies obtained using the proposed approach outperform the ones
learned using heuristic rewards in terms of the robustness degree, i.e., how
well the tasks are satisfied. Furthermore, we demonstrate the proposed RL
approach in a toast-placing task learned by a Baxter robot.
A Neural Knowledge Language Model
Authors: Sungjin Ahn, Heeyoul Choi, Tanel Pärnamaa, Yoshua Bengio
Date: 2016-08-01 00:00:00
Tasks: Language Modeling, Language Modelling, model

Current language models have a significant limitation in the ability to
encode and decode factual knowledge. This is mainly because they acquire such
knowledge from statistical co-occurrences although most of the knowledge words
are rarely observed. In this paper, we propose a Neural Knowledge Language
Model (NKLM) which combines symbolic knowledge provided by the knowledge graph
with the RNN language model. By predicting whether the word to generate has an
underlying fact or not, the model can generate such knowledge-related words by
copying from the description of the predicted fact. In experiments, we show
that the NKLM significantly improves the performance while generating a much
smaller number of unknown words.
Keyframe-Based Visual-Inertial Online SLAM with Relocalization
Authors: Anton Kasyanov, Francis Engelmann, Jörg Stückler, Bastian Leibe
Date: 2017-02-07 00:00:00
Tasks: Pose Tracking, Simultaneous Localization and Mapping

Complementing images with inertial measurements has become one of the most
popular approaches to achieve highly accurate and robust real-time camera pose
tracking. In this paper, we present a keyframe-based approach to
visual-inertial simultaneous localization and mapping (SLAM) for monocular and
stereo cameras. Our visual-inertial SLAM system is based on a real-time capable
visual-inertial odometry method that provides locally consistent trajectory and
map estimates. We achieve global consistency in the estimate through online
loop-closing and non-linear optimization. Furthermore, our system supports
relocalization in a map that has been previously obtained and allows for
continued SLAM operation. We evaluate our approach in terms of accuracy,
relocalization capability and run-time efficiency on public indoor benchmark
datasets and on newly recorded outdoor sequences. We demonstrate
state-of-the-art performance of our system compared to a visual-inertial
odometry method and baseline visual SLAM approaches in recovering the
trajectory of the camera.
A Simple, Fast and Fully Automated Approach for Midline Shift Measurement on Brain Computed Tomography
Authors: Huan-Chih Wang, Shih-Hao Ho, Furen Xiao, Jen-Hai Chou
Date: 2017-03-02 00:00:00

Brain CT has become a standard imaging tool for emergent evaluation of brain
condition, and measurement of midline shift (MLS) is one of the most important
features to address for brain CT assessment. We present a simple method to
estimate MLS and propose a new alternative parameter to MLS: the ratio of MLS
over the maximal width of intracranial region (MLS/ICWMAX). Three neurosurgeons
and our automated system were asked to measure MLS and MLS/ICWMAX in the same
sets of axial CT images obtained from 41 patients admitted to ICU under
neurosurgical service. A weighted midline (WML) was plotted based on individual
pixel intensities, with higher weighted given to the darker portions. The MLS
could then be measured as the distance between the WML and ideal midline (IML)
near the foramen of Monro. The average processing time to output an automatic
MLS measurement was around 10 seconds. Our automated system achieved an overall
accuracy of 90.24% when the CT images were calibrated automatically, and
performed better when the calibrations of head rotation were done manually
(accuracy: 92.68%). MLS/ICWMAX and MLS both gave results in same confusion
matrices and produced similar ROC curve results. We demonstrated a simple, fast
and accurate automated system of MLS measurement and introduced a new parameter
(MLS/ICWMAX) as a good alternative to MLS in terms of estimating the degree of
brain deformation, especially when non-DICOM images (e.g. JPEG) are more easily
accessed.
Unsupervised Steganalysis Based on Artificial Training Sets
Authors: Daniel Lerch-Hostalot, David Megías
Date: 2017-03-02 00:00:00
Tasks: General Classification, Steganalysis

In this paper, an unsupervised steganalysis method that combines artificial
training setsand supervised classification is proposed. We provide a formal
framework for unsupervisedclassification of stego and cover images in the
typical situation of targeted steganalysis (i.e.,for a known algorithm and
approximate embedding bit rate). We also present a completeset of experiments
using 1) eight different image databases, 2) image features based on
RichModels, and 3) three different embedding algorithms: Least Significant Bit
(LSB) matching,Highly undetectable steganography (HUGO) and Wavelet Obtained
Weights (WOW). Weshow that the experimental results outperform previous methods
based on Rich Models inthe majority of the tested cases. At the same time, the
proposed approach bypasses theproblem of Cover Source Mismatch -when the
embedding algorithm and bit rate are known-, since it removes the need of a
training database when we have a large enough testing set.Furthermore, we
provide a generic proof of the proposed framework in the machine
learningcontext. Hence, the results of this paper could be extended to other
classification problemssimilar to steganalysis.
A Robust Adaptive Stochastic Gradient Method for Deep Learning
Authors: Caglar Gulcehre, Jose Sotelo, Marcin Moczulski, Yoshua Bengio
Date: 2017-03-02 00:00:00
Tasks: Deep Learning

Stochastic gradient algorithms are the main focus of large-scale optimization
problems and led to important successes in the recent advancement of the deep
learning algorithms. The convergence of SGD depends on the careful choice of
learning rate and the amount of the noise in stochastic estimates of the
gradients. In this paper, we propose an adaptive learning rate algorithm, which
utilizes stochastic curvature information of the loss function for
automatically tuning the learning rates. The information about the element-wise
curvature of the loss function is estimated from the local statistics of the
stochastic first order gradients. We further propose a new variance reduction
technique to speed up the convergence. In our experiments with deep neural
networks, we obtained better performance compared to the popular stochastic
gradient algorithms.
A Generic Online Parallel Learning Framework for Large Margin Models
Authors: Shuming Ma, Xu sun
Date: 2017-03-02 00:00:00

To speed up the training process, many existing systems use parallel
technology for online learning algorithms. However, most research mainly focus
on stochastic gradient descent (SGD) instead of other algorithms. We propose a
generic online parallel learning framework for large margin models, and also
analyze our framework on popular large margin algorithms, including MIRA and
Structured Perceptron. Our framework is lock-free and easy to implement on
existing systems. Experiments show that systems with our framework can gain
near linear speed up by increasing running threads, and with no loss in
accuracy.
Lock-Free Parallel Perceptron for Graph-based Dependency Parsing
Authors: Xu Sun, Shuming Ma
Date: 2017-03-02 00:00:00
Tasks: Dependency Parsing

Dependency parsing is an important NLP task. A popular approach for
dependency parsing is structured perceptron. Still, graph-based dependency
parsing has the time complexity of $O(n^3)$, and it suffers from slow training.
To deal with this problem, we propose a parallel algorithm called parallel
perceptron. The parallel algorithm can make full use of a multi-core computer
which saves a lot of training time. Based on experiments we observe that
dependency parsing with parallel perceptron can achieve 8-fold faster training
speed than traditional structured perceptron methods when using 10 threads, and
with no loss at all in accuracy.
Differentiable Programs with Neural Libraries
Authors: Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow
Date: 2016-11-07 00:00:00
Proceeding: ICML 2017 8
Tasks: Lifelong learning

We develop a framework for combining differentiable programming languages
with neural networks. Using this framework we create end-to-end trainable
systems that learn to write interpretable algorithms with perceptual
components. We explore the benefits of inductive biases for strong
generalization and modularity that come from the program-like structure of our
models. In particular, modularity allows us to learn a library of (neural)
functions which grows and improves as more tasks are solved. Empirically, we
show that this leads to lifelong learning systems that transfer knowledge to
new tasks more effectively than baselines.
Differentiable Functional Program Interpreters
Authors: John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow
Date: 2016-11-07 00:00:00
Tasks: Program Synthesis

Programming by Example (PBE) is the task of inducing computer programs from
input-output examples. It can be seen as a type of machine learning where the
hypothesis space is the set of legal programs in some programming language.
Recent work on differentiable interpreters relaxes the discrete space of
programs into a continuous space so that search over programs can be performed
using gradient-based optimization. While conceptually powerful, so far
differentiable interpreter-based program synthesis has only been capable of
solving very simple problems. In this work, we study modeling choices that
arise when constructing a differentiable programming language and their impact
on the success of synthesis. The main motivation for the modeling choices comes
from functional programming: we study the effect of memory allocation schemes,
immutable data, type systems, and built-in control-flow structures. Empirically
we show that incorporating functional programming ideas into differentiable
programming languages allows us to learn much more complex programs than is
possible with existing differentiable languages.
Sampling Variations of Lead Sheets
Authors: Pierre Roy, Alexandre Papadopoulos, François Pachet
Date: 2017-03-02 00:00:00

Machine-learning techniques have been recently used with spectacular results
to generate artefacts such as music or text. However, these techniques are
still unable to capture and generate artefacts that are convincingly
structured. In this paper we present an approach to generate structured musical
sequences. We introduce a mechanism for sampling efficiently variations of
musical sequences. Given a input sequence and a statistical model, this
mechanism samples a set of sequences whose distance to the input sequence is
approximately within specified bounds. This mechanism is implemented as an
extension of belief propagation, and uses local fields to bias the generation.
We show experimentally that sampled sequences are indeed closely correlated to
the standard musical similarity measure defined by Mongeau and Sankoff. We then
show how this mechanism can used to implement composition strategies that
enforce arbitrary structure on a musical lead sheet generation problem.