pwc-archive/papers-with-abstracts huggingface dataset:
Enabling Robots to Communicate their Objectives
Authors: Sandy H. Huang, David Held, Pieter Abbeel, Anca D. Dragan
Date: 2017-02-11 00:00:00
Tasks: Autonomous Driving, Reinforcement Learning

The overarching goal of this work is to efficiently enable end-users to
correctly anticipate a robot's behavior in novel situations. Since a robot's
behavior is often a direct result of its underlying objective function, our
insight is that end-users need to have an accurate mental model of this
objective function in order to understand and predict what the robot will do.
While people naturally develop such a mental model over time through observing
the robot act, this familiarization process may be lengthy. Our approach
reduces this time by having the robot model how people infer objectives from
observed behavior, and then it selects those behaviors that are maximally
informative. The problem of computing a posterior over objectives from observed
behavior is known as Inverse Reinforcement Learning (IRL), and has been applied
to robots learning human objectives. We consider the problem where the roles of
human and robot are swapped. Our main contribution is to recognize that unlike
robots, humans will not be exact in their IRL inference. We thus introduce two
factors to define candidate approximate-inference models for human learning in
this setting, and analyze them in a user study in the autonomous driving
domain. We show that certain approximate-inference models lead to the robot
generating example behaviors that better enable users to anticipate what it
will do in novel situations. Our results also suggest, however, that additional
research is needed in modeling how humans extrapolate from examples of robot
behavior.
View Synthesis by Appearance Flow
Authors: Tinghui Zhou, Shubham Tulsiani, Weilun Sun, Jitendra Malik, Alexei A. Efros
Date: 2016-05-11 00:00:00
Tasks: Novel View Synthesis

We address the problem of novel view synthesis: given an input image,
synthesizing new images of the same object or scene observed from arbitrary
viewpoints. We approach this as a learning task but, critically, instead of
learning to synthesize pixels from scratch, we learn to copy them from the
input image. Our approach exploits the observation that the visual appearance
of different views of the same instance is highly correlated, and such
correlation could be explicitly learned by training a convolutional neural
network (CNN) to predict appearance flows -- 2-D coordinate vectors specifying
which pixels in the input view could be used to reconstruct the target view.
Furthermore, the proposed framework easily generalizes to multiple input views
by learning how to optimally combine single-view predictions. We show that for
both objects and scenes, our approach is able to synthesize novel views of
higher perceptual quality than previous CNN-based techniques.
SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
Authors: Soroush Mehri, Kundan Kumar, Ishaan Gulrajani, Rithesh Kumar, Shubham Jain, Jose Sotelo, Aaron Courville, Yoshua Bengio
Date: 2016-12-22 00:00:00
Tasks: Audio Generation, Speech Synthesis, Temporal Sequences

In this paper we propose a novel model for unconditional audio generation
based on generating one audio sample at a time. We show that our model, which
profits from combining memory-less modules, namely autoregressive multilayer
perceptrons, and stateful recurrent neural networks in a hierarchical structure
is able to capture underlying sources of variations in the temporal sequences
over very long time spans, on three datasets of different nature. Human
evaluation on the generated samples indicate that our model is preferred over
competing models. We also show how each component of the model contributes to
the exhibited performance.
A Collective, Probabilistic Approach to Schema Mapping: Appendix
Authors: Angelika Kimmig, Alex Memory, Renee J. Miller, Lise Getoor
Date: 2017-02-11 00:00:00

In this appendix we provide additional supplementary material to "A
Collective, Probabilistic Approach to Schema Mapping." We include an additional
extended example, supplementary experiment details, and proof for the
complexity result stated in the main paper.
On the Global-Local Dichotomy in Sparsity Modeling
Authors: Dmitry Batenkov, Yaniv Romano, Michael Elad
Date: 2017-02-11 00:00:00

The traditional sparse modeling approach, when applied to inverse problems
with large data such as images, essentially assumes a sparse model for small
overlapping data patches. While producing state-of-the-art results, this
methodology is suboptimal, as it does not attempt to model the entire global
signal in any meaningful way - a nontrivial task by itself. In this paper we
propose a way to bridge this theoretical gap by constructing a global model
from the bottom up. Given local sparsity assumptions in a dictionary, we show
that the global signal representation must satisfy a constrained
underdetermined system of linear equations, which can be solved efficiently by
modern optimization methods such as Alternating Direction Method of Multipliers
(ADMM). We investigate conditions for unique and stable recovery, and provide
numerical evidence corroborating the theory.
Distributed Mapping with Privacy and Communication Constraints: Lightweight Algorithms and Object-based Models
Authors: Siddharth Choudhary, Luca Carlone, Carlos Nieto, John Rogers, Henrik I. Christensen, Frank Dellaert
Date: 2017-02-11 00:00:00
Tasks: Sensor Fusion

We consider the following problem: a team of robots is deployed in an unknown
environment and it has to collaboratively build a map of the area without a
reliable infrastructure for communication. The backbone for modern mapping
techniques is pose graph optimization, which estimates the trajectory of the
robots, from which the map can be easily built. The first contribution of this
paper is a set of distributed algorithms for pose graph optimization: rather
than sending all sensor data to a remote sensor fusion server, the robots
exchange very partial and noisy information to reach an agreement on the pose
graph configuration. Our approach can be considered as a distributed
implementation of the two-stage approach of Carlone et al., where we use the
Successive Over-Relaxation (SOR) and the Jacobi Over-Relaxation (JOR) as
workhorses to split the computation among the robots. As a second contribution,
we extend %and demonstrate the applicability of the proposed distributed
algorithms to work with object-based map models. The use of object-based models
avoids the exchange of raw sensor measurements (e.g., point clouds) further
reducing the communication burden. Our third contribution is an extensive
experimental evaluation of the proposed techniques, including tests in
realistic Gazebo simulations and field experiments in a military test facility.
Abundant experimental evidence suggests that one of the proposed algorithms
(the Distributed Gauss-Seidel method or DGS) has excellent performance. The DGS
requires minimal information exchange, has an anytime flavor, scales well to
large teams, is robust to noise, and is easy to implement. Our field tests show
that the combined use of our distributed algorithms and object-based models
reduces the communication requirements by several orders of magnitude and
enables distributed mapping with large teams of robots in real-world problems.
Fast Resampling of 3D Point Clouds via Graphs
Authors: Siheng Chen, Dong Tian, Chen Feng, Anthony Vetro, Jelena Kovačević
Date: 2017-02-11 00:00:00

To reduce cost in storing, processing and visualizing a large-scale point
cloud, we consider a randomized resampling strategy to select a representative
subset of points while preserving application-dependent features. The proposed
strategy is based on graphs, which can represent underlying surfaces and lend
themselves well to efficient computation. We use a general feature-extraction
operator to represent application-dependent features and propose a general
reconstruction error to evaluate the quality of resampling. We obtain a general
form of optimal resampling distribution by minimizing the reconstruction error.
The proposed optimal resampling distribution is guaranteed to be shift,
rotation and scale-invariant in the 3D space. We next specify the
feature-extraction operator to be a graph filter and study specific resampling
strategies based on all-pass, low-pass, high-pass graph filtering and graph
filter banks. We finally apply the proposed methods to three applications:
large-scale visualization, accurate registration and robust shape modeling. The
empirical performance validates the effectiveness and efficiency of the
proposed resampling methods.
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
Authors: Vanya V. Valindria, Ioannis Lavdas, Wenjia Bai, Konstantinos Kamnitsas, Eric O. Aboagye, Andrea G. Rockall, Daniel Rueckert, Ben Glocker
Date: 2017-02-11 00:00:00
Tasks: General Classification, Organ Segmentation, Segmentation

When integrating computational tools such as automatic segmentation into
clinical practice, it is of utmost importance to be able to assess the level of
accuracy on new data, and in particular, to detect when an automatic method
fails. However, this is difficult to achieve due to absence of ground truth.
Segmentation accuracy on clinical data might be different from what is found
through cross-validation because validation data is often used during
incremental method development, which can lead to overfitting and unrealistic
performance expectations. Before deployment, performance is quantified using
different metrics, for which the predicted segmentation is compared to a
reference segmentation, often obtained manually by an expert. But little is
known about the real performance after deployment when a reference is
unavailable. In this paper, we introduce the concept of reverse classification
accuracy (RCA) as a framework for predicting the performance of a segmentation
method on new data. In RCA we take the predicted segmentation from a new image
to train a reverse classifier which is evaluated on a set of reference images
with available ground truth. The hypothesis is that if the predicted
segmentation is of good quality, then the reverse classifier will perform well
on at least some of the reference images. We validate our approach on
multi-organ segmentation with different classifiers and segmentation methods.
Our results indicate that it is indeed possible to predict the quality of
individual segmentations, in the absence of ground truth. Thus, RCA is ideal
for integration into automatic processing pipelines in clinical routine and as
part of large-scale image analysis studies.
Recurrent Mixture Density Network for Spatiotemporal Visual Attention
Authors: Loris Bazzani, Hugo Larochelle, Lorenzo Torresani
Date: 2016-03-27 00:00:00
Tasks: Action Classification, Saliency Prediction

In many computer vision tasks, the relevant information to solve the problem
at hand is mixed to irrelevant, distracting information. This has motivated
researchers to design attentional models that can dynamically focus on parts of
images or videos that are salient, e.g., by down-weighting irrelevant pixels.
In this work, we propose a spatiotemporal attentional model that learns where
to look in a video directly from human fixation data. We model visual attention
with a mixture of Gaussians at each frame. This distribution is used to express
the probability of saliency for each pixel. Time consistency in videos is
modeled hierarchically by: 1) deep 3D convolutional features to represent
spatial and short-term time relations and 2) a long short-term memory network
on top that aggregates the clip-level representation of sequential clips and
therefore expands the temporal domain from few frames to seconds. The
parameters of the proposed model are optimized via maximum likelihood
estimation using human fixations as training data, without knowledge of the
action in each video. Our experiments on Hollywood2 show state-of-the-art
performance on saliency prediction for video. We also show that our attentional
model trained on Hollywood2 generalizes well to UCF101 and it can be leveraged
to improve action classification accuracy on both datasets.
Parallel Long Short-Term Memory for Multi-stream Classification
Authors: Mohamed Bouaziz, Mohamed Morchid, Richard Dufour, Georges Linarès, Renato de Mori
Date: 2017-02-11 00:00:00
Tasks: Classification, General Classification

Recently, machine learning methods have provided a broad spectrum of original
and efficient algorithms based on Deep Neural Networks (DNN) to automatically
predict an outcome with respect to a sequence of inputs. Recurrent hidden cells
allow these DNN-based models to manage long-term dependencies such as Recurrent
Neural Networks (RNN) and Long Short-Term Memory (LSTM). Nevertheless, these
RNNs process a single input stream in one (LSTM) or two (Bidirectional LSTM)
directions. But most of the information available nowadays is from multistreams
or multimedia documents, and require RNNs to process these information
synchronously during the training. This paper presents an original LSTM-based
architecture, named Parallel LSTM (PLSTM), that carries out multiple parallel
synchronized input sequences in order to predict a common output. The proposed
PLSTM method could be used for parallel sequence classification purposes. The
PLSTM approach is evaluated on an automatic telecast genre sequences
classification task and compared with different state-of-the-art architectures.
Results show that the proposed PLSTM method outperforms the baseline n-gram
models as well as the state-of-the-art LSTM approach.
A Minimax Algorithm Better Than Alpha-beta?: No and Yes
Authors: Aske Plaat, Jonathan Schaeffer, Wim Pijls, Arie de Bruin
Date: 2017-02-11 00:00:00

This paper has three main contributions to our understanding of fixed-depth
minimax search: (A) A new formulation for Stockman's SSS* algorithm, based on
Alpha-Beta, is presented. It solves all the perceived drawbacks of SSS*,
finally transforming it into a practical algorithm. In effect, we show that
SSS* = alpha-beta + ransposition tables. The crucial step is the realization
that transposition tables contain so-called solution trees, structures that are
used in best-first search algorithms like SSS*. Having created a practical
version, we present performance measurements with tournament game-playing
programs for three different minimax games, yielding results that contradict a
number of publications. (B) Based on the insights gained in our attempts at
understanding SSS*, we present a framework that facilitates the construction of
several best-first fixed- depth game-tree search algorithms, known and new. The
framework is based on depth-first null-window Alpha-Beta search, enhanced with
storage to allow for the refining of previous search results. It focuses
attention on the essential differences between algorithms. (C) We present a new
instance of the framework, MTD(f). It is well-suited for use with iterative
deepening, and performs better than algorithms that are currently used in most
state-of-the-art game-playing programs. We provide experimental evidence to
explain why MTD(f) performs better than the other fixed-depth minimax
algorithms.
Automated Identification of Drug-Drug Interactions in Pediatric Congestive Heart Failure Patients
Authors: Daniel Miller
Date: 2017-02-11 00:00:00
Tasks: Kidney Function

Congestive Heart Failure, or CHF, is a serious medical condition that can
result in fluid buildup in the body as a result of a weak heart. When the heart
can't pump enough blood to efficiently deliver nutrients and oxygen to the
body, kidney function may be impaired, resulting in fluid retention. CHF
patients require a broad drug regimen to maintain the delicate system balance,
particularly between their heart and kidneys. These drugs include ACE
inhibitors and Beta Blockers to control blood pressure, anticoagulants to
prevent blood clots, and diuretics to reduce fluid overload. Many of these
drugs may interact, and potential effects of these interactions must be weighed
against their benefits. For this project, we consider a set of 44 drugs
identified as specifically relevant for treating CHF by pediatric cardiologists
at Lucile Packard Children's Hospital. This list was generated as part of our
current work at the LPCH Heart Center. The goal of this project is to identify
and evaluate potentially harmful drug-drug interactions (DDIs) within pediatric
patients with Congestive Heart Failure. This identification will be done
autonomously, so that it may continuously update by evaluating newly published
literature.
Adversarial examples in the physical world
Authors: Alexey Kurakin, Ian Goodfellow, Samy Bengio
Date: 2016-07-08 00:00:00
Tasks: BIG-bench Machine Learning

Most existing machine learning classifiers are highly vulnerable to
adversarial examples. An adversarial example is a sample of input data which
has been modified very slightly in a way that is intended to cause a machine
learning classifier to misclassify it. In many cases, these modifications can
be so subtle that a human observer does not even notice the modification at
all, yet the classifier still makes a mistake. Adversarial examples pose
security concerns because they could be used to perform an attack on machine
learning systems, even if the adversary has no access to the underlying model.
Up to now, all previous work have assumed a threat model in which the adversary
can feed data directly into the machine learning classifier. This is not always
the case for systems operating in the physical world, for example those which
are using signals from cameras and other sensors as an input. This paper shows
that even in such physical world scenarios, machine learning systems are
vulnerable to adversarial examples. We demonstrate this by feeding adversarial
images obtained from cell-phone camera to an ImageNet Inception classifier and
measuring the classification accuracy of the system. We find that a large
fraction of adversarial examples are classified incorrectly even when perceived
through the camera.
Fixing the Infix: Unsupervised Discovery of Root-and-Pattern Morphology
Authors: Tarek Sakakini, Suma Bhat, Pramod Viswanath
Date: 2017-02-07 00:00:00

We present an unsupervised and language-agnostic method for learning
root-and-pattern morphology in Semitic languages. This form of morphology,
abundant in Semitic languages, has not been handled in prior unsupervised
approaches. We harness the syntactico-semantic information in distributed word
representations to solve the long standing problem of root-and-pattern
discovery in Semitic languages. Moreover, we construct an unsupervised root
extractor based on the learned rules. We prove the validity of learned rules
across Arabic, Hebrew, and Amharic, alongside showing that our root extractor
compares favorably with a widely used, carefully engineered root extractor:
ISRI.
Adversarial Machine Learning at Scale
Authors: Alexey Kurakin, Ian Goodfellow, Samy Bengio
Date: 2016-11-04 00:00:00
Tasks: BIG-bench Machine Learning

Adversarial examples are malicious inputs designed to fool machine learning
models. They often transfer from one model to another, allowing attackers to
mount black box attacks without knowledge of the target model's parameters.
Adversarial training is the process of explicitly training a model on
adversarial examples, in order to make it more robust to attack or to reduce
its test error on clean inputs. So far, adversarial training has primarily been
applied to small problems. In this research, we apply adversarial training to
ImageNet. Our contributions include: (1) recommendations for how to succesfully
scale adversarial training to large models and datasets, (2) the observation
that adversarial training confers robustness to single-step attack methods, (3)
the finding that multi-step attack methods are somewhat less transferable than
single-step attack methods, so single-step attacks are the best for mounting
black-box attacks, and (4) resolution of a "label leaking" effect that causes
adversarially trained models to perform better on adversarial examples than on
clean examples, because the adversarial example construction process uses the
true label and the model can learn to exploit regularities in the construction
process.
Multi-Resolution Dual-Tree Wavelet Scattering Network for Signal Classification
Authors: Amarjot Singh, Nick Kingsbury
Date: 2017-02-10 00:00:00
Tasks: Classification, General Classification, Translation

This paper introduces a Deep Scattering network that utilizes Dual-Tree
complex wavelets to extract translation invariant representations from an input
signal. The computationally efficient Dual-Tree wavelets decompose the input
signal into densely spaced representations over scales. Translation invariance
is introduced in the representations by applying a non-linearity over a region
followed by averaging. The discriminatory information in the densely spaced,
locally smooth, signal representations aids the learning of the classifier. The
proposed network is shown to outperform Mallat's ScatterNet on four datasets
with different modalities on classification accuracy.
Learning Concept Embeddings for Efficient Bag-of-Concepts Densification
Authors: Walid Shalaby, Wlodek Zadrozny
Date: 2017-02-10 00:00:00
Tasks: Document Classification

Explicit concept space models have proven efficacy for text representation in
many natural language and text mining applications. The idea is to embed
textual structures into a semantic space of concepts which captures the main
ideas, objects, and the characteristics of these structures. The so called Bag
of Concepts (BoC) representation suffers from data sparsity causing low
similarity scores between similar texts due to low concept overlap. To address
this problem, we propose two neural embedding models to learn continuous
concept vectors. Once they are learned, we propose an efficient vector
aggregation method to generate fully continuous BoC representations. We
evaluate our concept embedding models on three tasks: 1) measuring entity
semantic relatedness and ranking where we achieve 1.6% improvement in
correlation scores, 2) dataless concept categorization where we achieve
state-of-the-art performance and reduce the categorization error rate by more
than 5% compared to five prior word and entity embedding models, and 3)
dataless document classification where our models outperform the sparse BoC
representations. In addition, by exploiting our efficient linear time vector
aggregation method, we achieve better accuracy scores with much less concept
dimensions compared to previous BoC densification methods which operate in
polynomial time and require hundreds of dimensions in the BoC representation.
Batch Policy Gradient Methods for Improving Neural Conversation Models
Authors: Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter
Date: 2017-02-10 00:00:00
Tasks: Chatbot, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study reinforcement learning of chatbots with recurrent neural network
architectures when the rewards are noisy and expensive to obtain. For instance,
a chatbot used in automated customer service support can be scored by quality
assurance agents, but this process can be expensive, time consuming and noisy.
Previous reinforcement learning work for natural language processing uses
on-policy updates and/or is designed for on-line learning settings. We
demonstrate empirically that such strategies are not appropriate for this
setting and develop an off-policy batch policy gradient method (BPG). We
demonstrate the efficacy of our method via a series of synthetic experiments
and an Amazon Mechanical Turk experiment on a restaurant recommendations
dataset.