pwc-archive/papers-with-abstracts huggingface dataset:
O$^2$TD: (Near)-Optimal Off-Policy TD Learning
Authors: Bo Liu, Daoming Lyu, Wen Dong, Saad Biaz
Date: 2017-04-17 00:00:00

Temporal difference learning and Residual Gradient methods are the most
widely used temporal difference based learning algorithms; however, it has been
shown that none of their objective functions is optimal w.r.t approximating the
true value function $V$. Two novel algorithms are proposed to approximate the
true value function $V$. This paper makes the following contributions: (1) A
batch algorithm that can help find the approximate optimal off-policy
prediction of the true value function $V$. (2) A linear computational cost (per
step) near-optimal algorithm that can learn from a collection of off-policy
samples. (3) A new perspective of the emphatic temporal difference learning
which bridges the gap between off-policy optimality and off-policy stability.
HPatches: A benchmark and evaluation of handcrafted and learned local descriptors
Authors: Vassileios Balntas, Karel Lenc, Andrea Vedaldi, Krystian Mikolajczyk
Date: 2017-04-19 00:00:00
Proceeding: CVPR 2017 7
Tasks: Retrieval

In this paper, we propose a novel benchmark for evaluating local image
descriptors. We demonstrate that the existing datasets and evaluation protocols
do not specify unambiguously all aspects of evaluation, leading to ambiguities
and inconsistencies in results reported in the literature. Furthermore, these
datasets are nearly saturated due to the recent improvements in local
descriptors obtained by learning them from large annotated datasets. Therefore,
we introduce a new large dataset suitable for training and testing modern
descriptors, together with strictly defined evaluation protocols in several
tasks such as matching, retrieval and classification. This allows for more
realistic, and thus more reliable comparisons in different application
scenarios. We evaluate the performance of several state-of-the-art descriptors
and analyse their properties. We show that a simple normalisation of
traditional hand-crafted descriptors can boost their performance to the level
of deep learning based descriptors within a realistic benchmarks evaluation.
Stochastic Descent Analysis of Representation Learning Algorithms
Authors: Richard M. Golden
Date: 2014-12-18 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Representation Learning

Although stochastic approximation learning methods have been widely used in
the machine learning literature for over 50 years, formal theoretical analyses
of specific machine learning algorithms are less common because stochastic
approximation theorems typically possess assumptions which are difficult to
communicate and verify. This paper presents a new stochastic approximation
theorem for state-dependent noise with easily verifiable assumptions applicable
to the analysis and design of important deep learning algorithms including:
adaptive learning, contrastive divergence learning, stochastic descent
expectation maximization, and active learning.
Surprising properties of dropout in deep networks
Authors: David P. Helmbold, Philip M. Long
Date: 2016-02-14 00:00:00

We analyze dropout in deep networks with rectified linear units and the
quadratic loss. Our results expose surprising differences between the behavior
of dropout and more traditional regularizers like weight decay. For example, on
some simple data sets dropout training produces negative weights even though
the output is the sum of the inputs. This provides a counterpoint to the
suggestion that dropout discourages co-adaptation of weights. We also show that
the dropout penalty can grow exponentially in the depth of the network while
the weight-decay penalty remains essentially linear, and that dropout is
insensitive to various re-scalings of the input features, outputs, and network
weights. This last insensitivity implies that there are no isolated local
minima of the dropout training criterion. Our work uncovers new properties of
dropout, extends our understanding of why dropout succeeds, and lays the
foundation for further progress.
REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics
Authors: Mohan Sridharan, Michael Gelfond, Shiqi Zhang, Jeremy Wyatt
Date: 2015-08-17 00:00:00

This paper describes an architecture for robots that combines the
complementary strengths of probabilistic graphical models and declarative
programming to represent and reason with logic-based and probabilistic
descriptions of uncertainty and domain knowledge. An action language is
extended to support non-boolean fluents and non-deterministic causal laws. This
action language is used to describe tightly-coupled transition diagrams at two
levels of granularity, with a fine-resolution transition diagram defined as a
refinement of a coarse-resolution transition diagram of the domain. The
coarse-resolution system description, and a history that includes (prioritized)
defaults, are translated into an Answer Set Prolog (ASP) program. For any given
goal, inference in the ASP program provides a plan of abstract actions. To
implement each such abstract action, the robot automatically zooms to the part
of the fine-resolution transition diagram relevant to this action. A
probabilistic representation of the uncertainty in sensing and actuation is
then included in this zoomed fine-resolution system description, and used to
construct a partially observable Markov decision process (POMDP). The policy
obtained by solving the POMDP is invoked repeatedly to implement the abstract
action as a sequence of concrete actions, with the corresponding observations
being recorded in the coarse-resolution history and used for subsequent
reasoning. The architecture is evaluated in simulation and on a mobile robot
moving objects in an indoor domain, to show that it supports reasoning with
violation of defaults, noisy observations and unreliable actions, in complex
domains.
Superpixels: An Evaluation of the State-of-the-Art
Authors: David Stutz, Alexander Hermans, Bastian Leibe
Date: 2016-12-06 00:00:00
Tasks: Superpixels

Superpixels group perceptually similar pixels to create visually meaningful
entities while heavily reducing the number of primitives for subsequent
processing steps. As of these properties, superpixel algorithms have received
much attention since their naming in 2003. By today, publicly available
superpixel algorithms have turned into standard tools in low-level vision. As
such, and due to their quick adoption in a wide range of applications,
appropriate benchmarks are crucial for algorithm selection and comparison.
Until now, the rapidly growing number of algorithms as well as varying
experimental setups hindered the development of a unifying benchmark. We
present a comprehensive evaluation of 28 state-of-the-art superpixel algorithms
utilizing a benchmark focussing on fair comparison and designed to provide new
insights relevant for applications. To this end, we explicitly discuss
parameter optimization and the importance of strictly enforcing connectivity.
Furthermore, by extending well-known metrics, we are able to summarize
algorithm performance independent of the number of generated superpixels,
thereby overcoming a major limitation of available benchmarks. Furthermore, we
discuss runtime, robustness against noise, blur and affine transformations,
implementation details as well as aspects of visual quality. Finally, we
present an overall ranking of superpixel algorithms which redefines the
state-of-the-art and enables researchers to easily select appropriate
algorithms and the corresponding implementations which themselves are made
publicly available as part of our benchmark at
davidstutz.de/projects/superpixel-benchmark/.
End-to-End Multi-View Networks for Text Classification
Authors: Hongyu Guo, Colin Cherry, Jiang Su
Date: 2017-04-19 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

We propose a multi-view network for text classification. Our method
automatically creates various views of its input text, each taking the form of
soft attention weights that distribute the classifier's focus among a set of
base features. For a bag-of-words representation, each view focuses on a
different subset of the text's words. Aggregating many such views results in a
more discriminative and robust representation. Through a novel architecture
that both stacks and concatenates views, we produce a network that emphasizes
both depth and width, allowing training to converge quickly. Using our
multi-view architecture, we establish new state-of-the-art accuracies on two
benchmark tasks.
Robust Estimation of Multiple Inlier Structures
Authors: Xiang Yang, Peter Meer
Date: 2016-09-20 00:00:00

The robust estimator presented in this paper processes each structure
independently. The scales of the structures are estimated adaptively and no
threshold is involved in spite of different objective functions. The user has
to specify only the number of elemental subsets for random sampling. After
classifying all the input data, the segmented structures are sorted by their
strengths and the strongest inlier structures come out at the top. Like any
robust estimators, this algorithm also has limitations which are described in
detail. Several synthetic and real examples are presented to illustrate every
aspect of the algorithm.
On interestingness measures of formal concepts
Authors: Sergei O. Kuznetsov, Tatiana Makhalova
Date: 2016-11-08 00:00:00
Tasks: Clustering

Formal concepts and closed itemsets proved to be of big importance for
knowledge discovery, both as a tool for concise representation of association
rules and a tool for clustering and constructing domain taxonomies and
ontologies. Exponential explosion makes it difficult to consider the whole
concept lattice arising from data, one needs to select most useful and
interesting concepts. In this paper interestingness measures of concepts are
considered and compared with respect to various aspects, such as efficiency of
computation and applicability to noisy data and performing ranking correlation.
Generative Face Completion
Authors: Yijun Li, Sifei Liu, Jimei Yang, Ming-Hsuan Yang
Date: 2017-04-19 00:00:00
Proceeding: CVPR 2017 7
Tasks: Facial Inpainting, Semantic Parsing

In this paper, we propose an effective face completion algorithm using a deep
generative model. Different from well-studied background completion, the face
completion task is more challenging as it often requires to generate
semantically new pixels for the missing key components (e.g., eyes and mouths)
that contain large appearance variations. Unlike existing nonparametric
algorithms that search for patches to synthesize, our algorithm directly
generates contents for missing regions based on a neural network. The model is
trained with a combination of a reconstruction loss, two adversarial losses and
a semantic parsing loss, which ensures pixel faithfulness and local-global
contents consistency. With extensive experimental results, we demonstrate
qualitatively and quantitatively that our model is able to deal with a large
area of missing pixels in arbitrary shapes and generate realistic face
completion results.
SkiMap: An Efficient Mapping Framework for Robot Navigation
Authors: Daniele De Gregorio, Luigi Di Stefano
Date: 2017-04-19 00:00:00
Tasks: Robot Navigation

We present a novel mapping framework for robot navigation which features a
multi-level querying system capable to obtain rapidly representations as
diverse as a 3D voxel grid, a 2.5D height map and a 2D occupancy grid. These
are inherently embedded into a memory and time efficient core data structure
organized as a Tree of SkipLists. Compared to the well-known Octree
representation, our approach exhibits a better time efficiency, thanks to its
simple and highly parallelizable computational structure, and a similar memory
footprint when mapping large workspaces. Peculiarly within the realm of mapping
for robot navigation, our framework supports realtime erosion and
re-integration of measurements upon reception of optimized poses from the
sensor tracker, so as to improve continuously the accuracy of the map.
Deterministic Quantum Annealing Expectation-Maximization Algorithm
Authors: Hideyuki Miyahara, Koji Tsumura, Yuki Sughiyama
Date: 2017-04-19 00:00:00

Maximum likelihood estimation (MLE) is one of the most important methods in
machine learning, and the expectation-maximization (EM) algorithm is often used
to obtain maximum likelihood estimates. However, EM heavily depends on initial
configurations and fails to find the global optimum. On the other hand, in the
field of physics, quantum annealing (QA) was proposed as a novel optimization
approach. Motivated by QA, we propose a quantum annealing extension of EM,
which we call the deterministic quantum annealing expectation-maximization
(DQAEM) algorithm. We also discuss its advantage in terms of the path integral
formulation. Furthermore, by employing numerical simulations, we illustrate how
it works in MLE and show that DQAEM outperforms EM.
Learn to Model Motion from Blurry Footages
Authors: Wenbin Li, Da Chen, Zhihan Lv, Yan Yan, Darren Cosker
Date: 2017-04-19 00:00:00
Tasks: model, Optical Flow Estimation

It is difficult to recover the motion field from a real-world footage given a
mixture of camera shake and other photometric effects. In this paper we propose
a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a
traditional optical flow energy. We first conduct a CNN architecture using a
novel learnable directional filtering layer. Such layer encodes the angle and
distance similarity matrix between blur and camera motion, which is able to
enhance the blur features of the camera-shake footages. The proposed CNNs are
then integrated into an iterative optical flow framework, which enable the
capability of modelling and solving both the blind deconvolution and the
optical flow estimation problems simultaneously. Our framework is trained
end-to-end on a synthetic dataset and yields competitive precision and
performance against the state-of-the-art approaches.
Network of Experts for Large-Scale Image Categorization
Authors: Karim Ahmed, Mohammad Haris Baig, Lorenzo Torresani
Date: 2016-04-20 00:00:00
Tasks: Image Categorization, image-classification, Image Classification

We present a tree-structured network architecture for large scale image
classification. The trunk of the network contains convolutional layers
optimized over all classes. At a given depth, the trunk splits into separate
branches, each dedicated to discriminate a different subset of classes. Each
branch acts as an expert classifying a set of categories that are difficult to
tell apart, while the trunk provides common knowledge to all experts in the
form of shared features. The training of our "network of experts" is completely
end-to-end: the partition of categories into disjoint subsets is learned
simultaneously with the parameters of the network trunk and the experts are
trained jointly by minimizing a single learning objective over all classes. The
proposed structure can be built from any existing convolutional neural network
(CNN). We demonstrate its generality by adapting 4 popular CNNs for image
categorization into the form of networks of experts. Our experiments on
CIFAR100 and ImageNet show that in every case our method yields a substantial
improvement in accuracy over the base CNN, and gives the best result achieved
so far on CIFAR100. Finally, the improvement in accuracy comes at little
additional cost: compared to the base network, the training time is only
moderately increased and the number of parameters is comparable or in some
cases even lower.
Network Dissection: Quantifying Interpretability of Deep Visual Representations
Authors: David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, Antonio Torralba
Date: 2017-04-19 00:00:00
Proceeding: CVPR 2017 7

We propose a general framework called Network Dissection for quantifying the
interpretability of latent representations of CNNs by evaluating the alignment
between individual hidden units and a set of semantic concepts. Given any CNN
model, the proposed method draws on a broad data set of visual concepts to
score the semantics of hidden units at each intermediate convolutional layer.
The units with semantics are given labels across a range of objects, parts,
scenes, textures, materials, and colors. We use the proposed method to test the
hypothesis that interpretability of units is equivalent to random linear
combinations of units, then we apply our method to compare the latent
representations of various networks when trained to solve different supervised
and self-supervised training tasks. We further analyze the effect of training
iterations, compare networks trained with different initializations, examine
the impact of network depth and width, and measure the effect of dropout and
batch normalization on the interpretability of deep visual representations. We
demonstrate that the proposed method can shed light on characteristics of CNN
models and training methods that go beyond measurements of their discriminative
power.
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing
Authors: Rik van Noord, Johan Bos
Date: 2017-04-07 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Data Augmentation, POS, POS Tagging, Semantic Parsing

We evaluate a semantic parser based on a character-based sequence-to-sequence
model in the context of the SemEval-2017 shared task on semantic parsing for
AMRs. With data augmentation, super characters, and POS-tagging we gain major
improvements in performance compared to a baseline character-level model.
Although we improve on previous character-based neural semantic parsing models,
the overall accuracy is still lower than a state-of-the-art AMR parser. An
ensemble combining our neural semantic parser with an existing, traditional
parser, yields a small gain in performance.
Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition
Authors: Shubham Toshniwal, Hao Tang, Liang Lu, Karen Livescu
Date: 2017-04-05 00:00:00
Tasks: Decoder, Phoneme Recognition, speech-recognition, Speech Recognition

End-to-end training of deep learning-based models allows for implicit
learning of intermediate representations based on the final task loss. However,
the end-to-end approach ignores the useful domain knowledge encoded in explicit
intermediate-level supervision. We hypothesize that using intermediate
representations as auxiliary supervision at lower levels of deep networks may
be a good way of combining the advantages of end-to-end training and more
traditional pipeline approaches. We present experiments on conversational
speech recognition where we use lower-level tasks, such as phoneme recognition,
in a multitask training approach with an encoder-decoder model for direct
character transcription. We compare multiple types of lower-level tasks and
analyze the effects of the auxiliary tasks. Our results on the Switchboard
corpus show that this approach improves recognition accuracy over a standard
encoder-decoder model on the Eval2000 test set.
Recurrent Environment Simulators
Authors: Silvia Chiappa, Sébastien Racaniere, Daan Wierstra, Shakir Mohamed
Date: 2017-04-07 00:00:00
Tasks: Atari Games, Car Racing

Models that can simulate how environments change in response to actions can
be used by agents to plan and act efficiently. We improve on previous
environment simulators from high-dimensional pixel observations by introducing
recurrent neural networks that are able to make temporally and spatially
coherent predictions for hundreds of time-steps into the future. We present an
in-depth analysis of the factors affecting performance, providing the most
extensive attempt to advance the understanding of the properties of these
models. We address the issue of computationally inefficiency with a model that
does not need to generate a high-dimensional image at each time-step. We show
that our approach can be used to improve exploration and is adaptable to many
diverse environments, namely 10 Atari games, a 3D car racing environment, and
complex 3D mazes.
Redefining Context Windows for Word Embedding Models: An Experimental Study
Authors: Pierre Lison, Andrey Kutuzov
Date: 2017-04-19 00:00:00
Proceeding: WS 2017 5

Distributional semantic models learn vector representations of words through
the contexts they occur in. Although the choice of context (which often takes
the form of a sliding window) has a direct influence on the resulting
embeddings, the exact role of this model component is still not fully
understood. This paper presents a systematic analysis of context windows based
on a set of four distinct hyper-parameters. We train continuous Skip-Gram
models on two English-language corpora for various combinations of these
hyper-parameters, and evaluate them on both lexical similarity and analogy
tasks. Notable experimental results are the positive impact of cross-sentential
contexts and the surprisingly good performance of right-context windows.
Accurate Single Stage Detector Using Recurrent Rolling Convolution
Authors: Jimmy Ren, Xiaohao Chen, Jianbo Liu, Wenxiu Sun, Jiahao Pang, Qiong Yan, Yu-Wing Tai, Li Xu
Date: 2017-04-19 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Pedestrian Detection

Most of the recent successful methods in accurate object detection and
localization used some variants of R-CNN style two stage Convolutional Neural
Networks (CNN) where plausible regions were proposed in the first stage then
followed by a second stage for decision refinement. Despite the simplicity of
training and the efficiency in deployment, the single stage detection methods
have not been as competitive when evaluated in benchmarks consider mAP for high
IoU thresholds. In this paper, we proposed a novel single stage end-to-end
trainable object detection network to overcome this limitation. We achieved
this by introducing Recurrent Rolling Convolution (RRC) architecture over
multi-scale feature maps to construct object classifiers and bounding box
regressors which are "deep in context". We evaluated our method in the
challenging KITTI dataset which measures methods under IoU threshold of 0.7. We
showed that with RRC, a single reduced VGG-16 based model already significantly
outperformed all the previously published results. At the time this paper was
written our models ranked the first in KITTI car detection (the hard level),
the first in cyclist detection and the second in pedestrian detection. These
results were not reached by the previous single stage methods. The code is
publicly available.
Derivation of the Asymptotic Eigenvalue Distribution for Causal 2D-AR Models under Upscaling
Authors: David Vázquez-Padín, Fernando Pérez-González, Pedro Comesaña-Alfaro
Date: 2017-04-19 00:00:00

This technical report describes the derivation of the asymptotic eigenvalue
distribution for causal 2D-AR models under an upscaling scenario. Specifically,
it tackles the analytical derivation of the asymptotic eigenvalue distribution
of the sample autocorrelation matrix corresponding to genuine and upscaled
images. It also includes the pseudocode of the derived approaches for
resampling detection and resampling factor estimation that are based on this
analysis.