pwc-archive/papers-with-abstracts huggingface dataset:
Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars
Authors: Ana I. Maqueda, Antonio Loquercio, Guillermo Gallego, Narciso Garcia, Davide Scaramuzza
Date: 2018-04-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Classification, Event-based vision, Motion Estimation, Robust classification, Self-Driving Cars, Transfer Learning

Event cameras are bio-inspired vision sensors that naturally capture the
dynamics of a scene, filtering out redundant information. This paper presents a
deep neural network approach that unlocks the potential of event cameras on a
challenging motion-estimation task: prediction of a vehicle's steering angle.
To make the best out of this sensor-algorithm combination, we adapt
state-of-the-art convolutional architectures to the output of event sensors and
extensively evaluate the performance of our approach on a publicly available
large scale event-camera dataset (~1000 km). We present qualitative and
quantitative explanations of why event cameras allow robust steering prediction
even in cases where traditional cameras fail, e.g. challenging illumination
conditions and fast motion. Finally, we demonstrate the advantages of
leveraging transfer learning from traditional to event-based vision, and show
that our approach outperforms state-of-the-art algorithms based on standard
cameras.
How Far Can You Get By Combining Change Detection Algorithms?
Authors: Simone Bianco, Gianluigi Ciocca, Raimondo Schettini
Date: 2015-05-12 00:00:00
Tasks: Change Detection, Unity

Given the existence of many change detection algorithms, each with its own
peculiarities and strengths, we propose a combination strategy, that we termed
IUTIS (In Unity There Is Strength), based on a genetic Programming framework.
This combination strategy is aimed at leveraging the strengths of the
algorithms and compensate for their weakness. In this paper we show our
findings in applying the proposed strategy in two different scenarios. The
first scenario is purely performance-based. The second scenario performance and
efficiency must be balanced. Results demonstrate that starting from simple
algorithms we can achieve comparable results with respect to more complex
state-of-the-art change detection algorithms, while keeping the computational
complexity affordable for real-time applications.
Investigating Capsule Networks with Dynamic Routing for Text Classification
Authors: Wei Zhao, Jianbo Ye, Min Yang, Zeyang Lei, Suofei Zhang, Zhou Zhao
Date: 2018-03-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, General Classification, Multi-Label Text Classification, Sentiment Analysis, Subjectivity Analysis, Text Classification

In this study, we explore capsule networks with dynamic routing for text
classification. We propose three strategies to stabilize the dynamic routing
process to alleviate the disturbance of some noise capsules which may contain
"background" information or have not been successfully trained. A series of
experiments are conducted with capsule networks on six text classification
benchmarks. Capsule networks achieve state of the art on 4 out of 6 datasets,
which shows the effectiveness of capsule networks for text classification. We
additionally show that capsule networks exhibit significant improvement when
transfer single-label to multi-label text classification over strong baseline
methods. To the best of our knowledge, this is the first work that capsule
networks have been empirically investigated for text modeling.
Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior
Authors: Anjany Sekuboyina, Markus Rempfler, Jan Kukačka, Giles Tetteh, Alexander Valentinitsch, Jan S. Kirschke, Bjoern H. Menze
Date: 2018-04-04 00:00:00

Robust localisation and identification of vertebrae is essential for
automated spine analysis. The contribution of this work to the task is
two-fold: (1) Inspired by the human expert, we hypothesise that a sagittal and
coronal reformation of the spine contain sufficient information for labelling
the vertebrae. Thereby, we propose a butterfly-shaped network architecture
(termed Btrfly Net) that efficiently combines the information across
reformations. (2) Underpinning the Btrfly net, we present an energy-based
adversarial training regime that encodes local spine structure as an anatomical
prior into the network, thereby enabling it to achieve state-of-art performance
in all standard metrics on a benchmark dataset of 302 scans without any
post-processing during inference.
A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation
Authors: Guillermo Gallego, Henri Rebecq, Davide Scaramuzza
Date: 2018-04-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Optical Flow Estimation

We present a unifying framework to solve several computer vision problems
with event cameras: motion, depth and optical flow estimation. The main idea of
our framework is to find the point trajectories on the image plane that are
best aligned with the event data by maximizing an objective function: the
contrast of an image of warped events. Our method implicitly handles data
association between the events, and therefore, does not rely on additional
appearance information about the scene. In addition to accurately recovering
the motion parameters of the problem, our framework produces motion-corrected
edge-like images with high dynamic range that can be used for further scene
analysis. The proposed method is not only simple, but more importantly, it is,
to the best of our knowledge, the first method that can be successfully applied
to such a diverse set of important vision tasks with event cameras.
Gaussian Process Uncertainty in Age Estimation as a Measure of Brain Abnormality
Authors: Benjamin Gutierrez Becker, Tassilo Klein, Christian Wachinger
Date: 2018-04-04 00:00:00
Tasks: Age Estimation, regression

Multivariate regression models for age estimation are a powerful tool for
assessing abnormal brain morphology associated to neuropathology. Age
prediction models are built on cohorts of healthy subjects and are built to
reflect normal aging patterns. The application of these multivariate models to
diseased subjects usually results in high prediction errors, under the
hypothesis that neuropathology presents a similar degenerative pattern as that
of accelerated aging. In this work, we propose an alternative to the idea that
pathology follows a similar trajectory than normal aging. Instead, we propose
the use of metrics which measure deviations from the mean aging trajectory. We
propose to measure these deviations using two different metrics: uncertainty in
a Gaussian process regression model and a newly proposed age weighted
uncertainty measure. Consequently, our approach assumes that pathologic brain
patterns are different to those of normal aging. We present results for
subjects with autism, mild cognitive impairment and Alzheimer's disease to
highlight the versatility of the approach to different diseases and age ranges.
We evaluate volume, thickness, and VBM features for quantifying brain
morphology. Our evaluations are performed on a large number of images obtained
from a variety of publicly available neuroimaging databases. Across all
features, our uncertainty based measurements yield a better separation between
diseased subjects and healthy individuals than the prediction error. Finally,
we illustrate differences in the disease pattern to normal aging, supporting
the application of uncertainty as a measure of neuropathology.
Generative Adversarial Positive-Unlabelled Learning
Authors: Ming Hou, Brahim Chaib-Draa, Chao Li, Qibin Zhao
Date: 2017-11-21 00:00:00

In this work, we consider the task of classifying binary positive-unlabeled
(PU) data. The existing discriminative learning based PU models attempt to seek
an optimal reweighting strategy for U data, so that a decent decision boundary
can be found. However, given limited P data, the conventional PU models tend to
suffer from overfitting when adapted to very flexible deep neural networks. In
contrast, we are the first to innovate a totally new paradigm to attack the
binary PU task, from perspective of generative learning by leveraging the
powerful generative adversarial networks (GAN). Our generative
positive-unlabeled (GenPU) framework incorporates an array of discriminators
and generators that are endowed with different roles in simultaneously
producing positive and negative realistic samples. We provide theoretical
analysis to justify that, at equilibrium, GenPU is capable of recovering both
positive and negative data distributions. Moreover, we show GenPU is
generalizable and closely related to the semi-supervised classification. Given
rather limited P data, experiments on both synthetic and real-world dataset
demonstrate the effectiveness of our proposed framework. With infinite
realistic and diverse sample streams generated from GenPU, a very flexible
classifier can then be trained using deep neural networks.
Toward Incorporation of Relevant Documents in word2vec
Authors: Navid Rekabsaz, Bhaskar Mitra, Mihai Lupu, Allan Hanbury
Date: 2017-07-20 00:00:00
Tasks: Information Retrieval, Retrieval, Word Embeddings

Recent advances in neural word embedding provide significant benefit to
various information retrieval tasks. However as shown by recent studies,
adapting the embedding models for the needs of IR tasks can bring considerable
further improvements. The embedding models in general define the term
relatedness by exploiting the terms' co-occurrences in short-window contexts.
An alternative (and well-studied) approach in IR for related terms to a query
is using local information i.e. a set of top-retrieved documents. In view of
these two methods of term relatedness, in this work, we report our study on
incorporating the local information of the query in the word embeddings. One
main challenge in this direction is that the dense vectors of word embeddings
and their estimation of term-to-term relatedness remain difficult to interpret
and hard to analyze. As an alternative, explicit word representations propose
vectors whose dimensions are easily interpretable, and recent methods show
competitive performance to the dense vectors. We introduce a neural-based
explicit representation, rooted in the conceptual ideas of the word2vec
Skip-Gram model. The method provides interpretable explicit vectors while
keeping the effectiveness of the Skip-Gram model. The evaluation of various
explicit representations on word association collections shows that the newly
proposed method out- performs the state-of-the-art explicit representations
when tasked with ranking highly similar terms. Based on the introduced ex-
plicit representation, we discuss our approaches on integrating local documents
in globally-trained embedding models and discuss the preliminary results.
Uncertainty in Neural Network Word Embedding: Exploration of Threshold for Similarity
Authors: Navid Rekabsaz, Mihai Lupu, Allan Hanbury
Date: 2016-06-20 00:00:00
Tasks: Information Retrieval, Retrieval

Word embedding, specially with its recent developments, promises a
quantification of the similarity between terms. However, it is not clear to
which extent this similarity value can be genuinely meaningful and useful for
subsequent tasks. We explore how the similarity score obtained from the models
is really indicative of term relatedness. We first observe and quantify the
uncertainty factor of the word embedding models regarding to the similarity
value. Based on this factor, we introduce a general threshold on various
dimensions which effectively filters the highly related terms. Our evaluation
on four information retrieval collections supports the effectiveness of our
approach as the results of the introduced threshold are significantly better
than the baseline while being equal to or statistically indistinguishable from
the optimal results.
Interpretable Policies for Reinforcement Learning by Genetic Programming
Authors: Daniel Hein, Steffen Udluft, Thomas A. Runkler
Date: 2017-12-12 00:00:00
Tasks: regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Symbolic Regression

The search for interpretable reinforcement learning policies is of high
academic and industrial interest. Especially for industrial systems, domain
experts are more likely to deploy autonomously learned controllers if they are
understandable and convenient to evaluate. Basic algebraic equations are
supposed to meet these requirements, as long as they are restricted to an
adequate complexity. Here we introduce the genetic programming for
reinforcement learning (GPRL) approach based on model-based batch reinforcement
learning and genetic programming, which autonomously learns policy equations
from pre-existing default state-action trajectory samples. GPRL is compared to
a straight-forward method which utilizes genetic programming for symbolic
regression, yielding policies imitating an existing well-performing, but
non-interpretable policy. Experiments on three reinforcement learning
benchmarks, i.e., mountain car, cart-pole balancing, and industrial benchmark,
demonstrate the superiority of our GPRL approach compared to the symbolic
regression method. GPRL is capable of producing well-performing interpretable
reinforcement learning policies from pre-existing default trajectory data.
A Coordinate-wise Optimization Algorithm for Sparse Inverse Covariance Selection
Authors: Ganzhao Yuan, Haoxian Tan, Wei-Shi Zheng
Date: 2017-11-19 00:00:00

Sparse inverse covariance selection is a fundamental problem for analyzing
dependencies in high dimensional data. However, such a problem is difficult to
solve since it is NP-hard. Existing solutions are primarily based on convex
approximation and iterative hard thresholding, which only lead to sub-optimal
solutions. In this work, we propose a coordinate-wise optimization algorithm to
solve this problem which is guaranteed to converge to a coordinate-wise minimum
point. The algorithm iteratively and greedily selects one variable or swaps two
variables to identify the support set, and then solves a reduced convex
optimization problem over the support set to achieve the greatest descent. As a
side contribution of this paper, we propose a Newton-like algorithm to solve
the reduced convex sub-problem, which is proven to always converge to the
optimal solution with global linear convergence rate and local quadratic
convergence rate. Finally, we demonstrate the efficacy of our method on
synthetic data and real-world data sets. As a result, the proposed method
consistently outperforms existing solutions in terms of accuracy.
NegPSpan: efficient extraction of negative sequential patterns with embedding constraints
Authors: Thomas Guyet, René Quiniou
Date: 2018-04-04 00:00:00

Mining frequent sequential patterns consists in extracting recurrent
behaviors, modeled as patterns, in a big sequence dataset. Such patterns inform
about which events are frequently observed in sequences, i.e. what does really
happen. Sometimes, knowing that some specific event does not happen is more
informative than extracting a lot of observed events. Negative sequential
patterns (NSP) formulate recurrent behaviors by patterns containing both
observed events and absent events. Few approaches have been proposed to mine
such NSPs. In addition, the syntax and semantics of NSPs differ in the
different methods which makes it difficult to compare them. This article
provides a unified framework for the formulation of the syntax and the
semantics of NSPs. Then, we introduce a new algorithm, NegPSpan, that extracts
NSPs using a PrefixSpan depth-first scheme and enabling maxgap constraints that
other approaches do not take into account. The formal framework allows for
highlighting the differences between the proposed approach wrt to the methods
from the literature, especially wrt the state of the art approach eNSP.
Intensive experiments on synthetic and real datasets show that NegPSpan can
extract meaningful NSPs and that it can process bigger datasets than eNSP
thanks to significantly lower memory requirements and better computation times.
Model-Based Planning with Discrete and Continuous Actions
Authors: Mikael Henaff, William F. Whitney, Yann Lecun
Date: 2017-05-19 00:00:00

Action planning using learned and differentiable forward models of the world
is a general approach which has a number of desirable properties, including
improved sample complexity over model-free RL methods, reuse of learned models
across different tasks, and the ability to perform efficient gradient-based
optimization in continuous action spaces. However, this approach does not apply
straightforwardly when the action space is discrete. In this work, we show that
it is in fact possible to effectively perform planning via backprop in discrete
action spaces, using a simple paramaterization of the actions vectors on the
simplex combined with input noise when training the forward model. Our
experiments show that this approach can match or outperform model-free RL and
discrete planning methods on gridworld navigation tasks in terms of performance
and/or planning time while using limited environment interactions, and can
additionally be used to perform model-based control in a challenging new task
where the action space combines discrete and continuous actions. We furthermore
propose a policy distillation approach which yields a fast policy network which
can be used at inference time, removing the need for an iterative planning
procedure.
Information Maximizing Exploration with a Latent Dynamics Model
Authors: Trevor Barron, Oliver Obst, Heni Ben Amor
Date: 2018-04-04 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, model, Policy Gradient Methods, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

All reinforcement learning algorithms must handle the trade-off between
exploration and exploitation. Many state-of-the-art deep reinforcement learning
methods use noise in the action selection, such as Gaussian noise in policy
gradient methods or $\epsilon$-greedy in Q-learning. While these methods are
appealing due to their simplicity, they do not explore the state space in a
methodical manner. We present an approach that uses a model to derive reward
bonuses as a means of intrinsic motivation to improve model-free reinforcement
learning. A key insight of our approach is that this dynamics model can be
learned in the latent feature space of a value function, representing the
dynamics of the agent and the environment. This method is both theoretically
grounded and computationally advantageous, permitting the efficient use of
Bayesian information-theoretic methods in high-dimensional state spaces. We
evaluate our method on several continuous control tasks, focusing on improving
exploration.
Discriminative Cross-View Binary Representation Learning
Authors: Liu Liu, Hairong Qi
Date: 2018-04-04 00:00:00
Tasks: Image Retrieval, Quantization, Representation Learning, Retrieval

Learning compact representation is vital and challenging for large scale
multimedia data. Cross-view/cross-modal hashing for effective binary
representation learning has received significant attention with exponentially
growing availability of multimedia content. Most existing cross-view hashing
algorithms emphasize the similarities in individual views, which are then
connected via cross-view similarities. In this work, we focus on the
exploitation of the discriminative information from different views, and
propose an end-to-end method to learn semantic-preserving and discriminative
binary representation, dubbed Discriminative Cross-View Hashing (DCVH), in
light of learning multitasking binary representation for various tasks
including cross-view retrieval, image-to-image retrieval, and image
annotation/tagging. The proposed DCVH has the following key components. First,
it uses convolutional neural network (CNN) based nonlinear hashing functions
and multilabel classification for both images and texts simultaneously. Such
hashing functions achieve effective continuous relaxation during training
without explicit quantization loss by using Direct Binary Embedding (DBE)
layers. Second, we propose an effective view alignment via Hamming distance
minimization, which is efficiently accomplished by bit-wise XOR operation.
Extensive experiments on two image-text benchmark datasets demonstrate that
DCVH outperforms state-of-the-art cross-view hashing algorithms as well as
single-view image hashing algorithms. In addition, DCVH can provide competitive
performance for image annotation/tagging.
Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
Authors: Chao Li, Cheng Deng, Ning li, Wei Liu, Xinbo Gao, DaCheng Tao
Date: 2018-04-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Cross-Modal Retrieval, Retrieval

Thanks to the success of deep learning, cross-modal retrieval has made
significant progress recently. However, there still remains a crucial
bottleneck: how to bridge the modality gap to further enhance the retrieval
accuracy. In this paper, we propose a self-supervised adversarial hashing
(\textbf{SSAH}) approach, which lies among the early attempts to incorporate
adversarial learning into cross-modal hashing in a self-supervised fashion. The
primary contribution of this work is that two adversarial networks are
leveraged to maximize the semantic correlation and consistency of the
representations between different modalities. In addition, we harness a
self-supervised semantic network to discover high-level semantic information in
the form of multi-label annotations. Such information guides the feature
learning process and preserves the modality relationships in both the common
semantic space and the Hamming space. Extensive experiments carried out on
three benchmark datasets validate that the proposed SSAH surpasses the
state-of-the-art methods.
Tight Query Complexity Lower Bounds for PCA via Finite Sample Deformed Wigner Law
Authors: Max Simchowitz, Ahmed El Alaoui, Benjamin Recht
Date: 2018-04-04 00:00:00

We prove a \emph{query complexity} lower bound for approximating the top $r$ dimensional eigenspace of a matrix. We consider an oracle model where, given a symmetric matrix $\mathbf{M} \in \mathbb{R}^{d \times d}$, an algorithm $\mathsf{Alg}$ is allowed to make $\mathsf{T}$ exact queries of the form $\mathsf{w}^{(i)} = \mathbf{M} \mathsf{v}^{(i)}$ for $i$ in $\{1,...,\mathsf{T}\}$, where $\mathsf{v}^{(i)}$ is drawn from a distribution which depends arbitrarily on the past queries and measurements $\{\mathsf{v}^{(j)},\mathsf{w}^{(i)}\}_{1 \le j \le i-1}$. We show that for every $\mathtt{gap} \in (0,1/2]$, there exists a distribution over matrices $\mathbf{M}$ for which 1) $\mathrm{gap}_r(\mathbf{M}) = \Omega(\mathtt{gap})$ (where $\mathrm{gap}_r(\mathbf{M})$ is the normalized gap between the $r$ and $r+1$-st largest-magnitude eigenvector of $\mathbf{M}$), and 2) any algorithm $\mathsf{Alg}$ which takes fewer than $\mathrm{const} \times \frac{r \log d}{\sqrt{\mathtt{gap}}}$ queries fails (with overwhelming probability) to identity a matrix $\widehat{\mathsf{V}} \in \mathbb{R}^{d \times r}$ with orthonormal columns for which $\langle \widehat{\mathsf{V}}, \mathbf{M} \widehat{\mathsf{V}}\rangle \ge (1 - \mathrm{const} \times \mathtt{gap})\sum_{i=1}^r \lambda_i(\mathbf{M})$. Our bound requires only that $d$ is a small polynomial in $1/\mathtt{gap}$ and $r$, and matches the upper bounds of Musco and Musco '15. Moreover, it establishes a strict separation between convex optimization and \emph{randomized}, "strict-saddle" non-convex optimization of which PCA is a canonical example: in the former, first-order methods can have dimension-free iteration complexity, whereas in PCA, the iteration complexity of gradient-based methods must necessarily grow with the dimension.