pwc-archive/papers-with-abstracts huggingface dataset:
Multi-level Multimodal Common Semantic Space for Image-Phrase Grounding
Authors: Hassan Akbari, Svebor Karaman, Surabhi Bhargava, Brian Chen, Carl Vondrick, Shih-Fu Chang
Date: 2018-11-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Language Modeling, Language Modelling, Phrase Grounding, Sentence, Sentence Embeddings

We address the problem of phrase grounding by lear ing a multi-level common semantic space shared by the textual and visual modalities. We exploit multiple levels of feature maps of a Deep Convolutional Neural Network, as well as contextualized word and sentence embeddings extracted from a character-based language model. Following dedicated non-linear mappings for visual features at each level, word, and sentence embeddings, we obtain multiple instantiations of our common semantic space in which comparisons between any target text and the visual content is performed with cosine similarity. We guide the model by a multi-level multimodal attention mechanism which outputs attended visual features at each level. The best level is chosen to be compared with text content for maximizing the pertinence scores of image-sentence pairs of the ground truth. Experiments conducted on three publicly available datasets show significant performance gains (20%-60% relative) over the state-of-the-art in phrase localization and set a new performance record on those datasets. We provide a detailed ablation study to show the contribution of each element of our approach and release our code on GitHub.
Experience Replay for Continual Learning
Authors: David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy P. Lillicrap, Greg Wayne
Date: 2018-11-28 00:00:00
Proceeding: ICLR 2019 5
Tasks: Continual Learning, Reinforcement Learning

Continual learning is the problem of learning new tasks or knowledge while protecting old knowledge and ideally generalizing from old experience to learn new tasks faster. Neural networks trained by stochastic gradient descent often degrade on old tasks when trained successively on new tasks with different data distributions. This phenomenon, referred to as catastrophic forgetting, is considered a major hurdle to learning with non-stationary data or sequences of new tasks, and prevents networks from continually accumulating knowledge and skills. We examine this issue in the context of reinforcement learning, in a setting where an agent is exposed to tasks in a sequence. Unlike most other work, we do not provide an explicit indication to the model of task boundaries, which is the most general circumstance for a learning agent exposed to continuous experience. While various methods to counteract catastrophic forgetting have recently been proposed, we explore a straightforward, general, and seemingly overlooked solution - that of using experience replay buffers for all past events - with a mixture of on- and off-policy learning, leveraging behavioral cloning. We show that this strategy can still learn new tasks quickly yet can substantially reduce catastrophic forgetting in both Atari and DMLab domains, even matching the performance of methods that require task identities. When buffer storage is constrained, we confirm that a simple mechanism for randomly discarding data allows a limited size buffer to perform almost as well as an unbounded one.
Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview
Authors: Michael Kläs
Date: 2018-11-28 00:00:00
Tasks: BIG-bench Machine Learning

Quantifying and managing uncertainties that occur when data-driven models
such as those provided by AI and machine learning methods are applied is
crucial. This whitepaper provides a brief motivation and first overview of the
state of the art in identifying and quantifying sources of uncertainty for
data-driven components as well as means for analyzing their impact.
Racial categories in machine learning
Authors: Sebastian Benthall, Bruce D. Haynes
Date: 2018-11-28 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Controversies around race and machine learning have sparked debate among
computer scientists over how to design machine learning systems that guarantee
fairness. These debates rarely engage with how racial identity is embedded in
our social experience, making for sociological and psychological complexity.
This complexity challenges the paradigm of considering fairness to be a formal
property of supervised learning with respect to protected personal attributes.
Racial identity is not simply a personal subjective quality. For people labeled
"Black" it is an ascribed political category that has consequences for social
differentiation embedded in systemic patterns of social inequality achieved
through both social and spatial segregation. In the United States, racial
classification can best be understood as a system of inherently unequal status
categories that places whites as the most privileged category while signifying
the Negro/black category as stigmatized. Social stigma is reinforced through
the unequal distribution of societal rewards and goods along racial lines that
is reinforced by state, corporate, and civic institutions and practices. This
creates a dilemma for society and designers: be blind to racial group
disparities and thereby reify racialized social inequality by no longer
measuring systemic inequality, or be conscious of racial categories in a way
that itself reifies race. We propose a third option. By preceding group
fairness interventions with unsupervised learning to dynamically detect
patterns of segregation, machine learning systems can mitigate the root cause
of social disparities, social segregation and stratification, without further
anchoring status categories of disadvantage.
Robust Face Detection via Learning Small Faces on Hard Images
Authors: Zhishuai Zhang, Wei Shen, Siyuan Qiao, Yan Wang, Bo wang, Alan Yuille
Date: 2018-11-28 00:00:00
Tasks: Face Detection

Recent anchor-based deep face detectors have achieved promising performance,
but they are still struggling to detect hard faces, such as small, blurred and
partially occluded faces. A reason is that they treat all images and faces
equally, without putting more effort on hard ones; however, many training
images only contain easy faces, which are less helpful to achieve better
performance on hard images. In this paper, we propose that the robustness of a
face detector against hard faces can be improved by learning small faces on
hard images. Our intuitions are (1) hard images are the images which contain at
least one hard face, thus they facilitate training robust face detectors; (2)
most hard faces are small faces and other types of hard faces can be easily
converted to small faces by shrinking. We build an anchor-based deep face
detector, which only output a single feature map with small anchors, to
specifically learn small faces and train it by a novel hard image mining
strategy. Extensive experiments have been conducted on WIDER FACE, FDDB, Pascal
Faces, and AFW datasets to show the effectiveness of our method. Our method
achieves APs of 95.7, 94.9 and 89.7 on easy, medium and hard WIDER FACE val
dataset respectively, which surpass the previous state-of-the-arts, especially
on the hard subset. Code and model are available at
https://github.com/bairdzhang/smallhardface.
A Structure-aware Online Learning Algorithm for Markov Decision Processes
Authors: Arghyadip Roy, Vivek Borkar, Abhay Karandikar, Prasanna Chaporkar
Date: 2018-11-28 00:00:00
Tasks: Management, Reinforcement Learning, Reinforcement Learning (RL)

To overcome the curse of dimensionality and curse of modeling in Dynamic
Programming (DP) methods for solving classical Markov Decision Process (MDP)
problems, Reinforcement Learning (RL) algorithms are popular. In this paper, we
consider an infinite-horizon average reward MDP problem and prove the
optimality of the threshold policy under certain conditions. Traditional RL
techniques do not exploit the threshold nature of optimal policy while
learning. In this paper, we propose a new RL algorithm which utilizes the known
threshold structure of the optimal policy while learning by reducing the
feasible policy space. We establish that the proposed algorithm converges to
the optimal policy. It provides a significant improvement in convergence speed
and computational and storage complexity over traditional RL algorithms. The
proposed technique can be applied to a wide variety of optimization problems
that include energy efficient data transmission and management of queues. We
exhibit the improvement in convergence speed of the proposed algorithm over
other RL algorithms through simulations.
WaveletNet: Logarithmic Scale Efficient Convolutional Neural Networks for Edge Devices
Authors: Li Jing, Rumen Dangovski, Marin Soljacic
Date: 2018-11-28 00:00:00
Tasks: General Classification

We present a logarithmic-scale efficient convolutional neural network
architecture for edge devices, named WaveletNet. Our model is based on the
well-known depthwise convolution, and on two new layers, which we introduce in
this work: a wavelet convolution and a depthwise fast wavelet transform. By
breaking the symmetry in channel dimensions and applying a fast algorithm,
WaveletNet shrinks the complexity of convolutional blocks by an O(logD/D)
factor, where D is the number of channels. Experiments on CIFAR-10 and ImageNet
classification show superior and comparable performances of WaveletNet compared
to state-of-the-art models such as MobileNetV2.
Harmonic analysis on directed graphs and applications: from Fourier analysis to wavelets
Authors: Harry Sevi, Gabriel Rilling, Pierre Borgnat
Date: 2018-11-28 00:00:00

We introduce a novel harmonic analysis for functions defined on the vertices of a strongly connected directed graph of which the random walk operator is the cornerstone. As a first step, we consider the set of eigenvectors of the random walk operator as a non-orthogonal Fourier-type basis for functions over directed graphs. We found a frequency interpretation by linking the variation of the eigenvectors of the random walk operator obtained from their Dirichlet energy to the real part of their associated eigenvalues. From this Fourier basis, we can proceed further and build multi-scale analyses on directed graphs. We propose both a redundant wavelet transform and a decimated wavelet transform by extending the diffusion wavelets framework by Coifman and Maggioni for directed graphs. The development of our harmonic analysis on directed graphs thus leads us to consider both semi-supervised learning problems and signal modeling problems on graphs applied to directed graphs highlighting the efficiency of our framework.
Basis Pursuit Denoise with Nonsmooth Constraints
Authors: Robert Baraldi, Rajiv Kumar, Aleksandr Aravkin
Date: 2018-11-28 00:00:00
Tasks: Denoising, Matrix Completion

Level-set optimization formulations with data-driven constraints minimize a
regularization functional subject to matching observations to a given error
level. These formulations are widely used, particularly for matrix completion
and sparsity promotion in data interpolation and denoising. The misfit level is
typically measured in the l2 norm, or other smooth metrics. In this paper, we
present a new flexible algorithmic framework that targets nonsmooth level-set
constraints, including L1, Linf, and even L0 norms. These constraints give
greater flexibility for modeling deviations in observation and denoising, and
have significant impact on the solution. Measuring error in the L1 and L0 norms
makes the result more robust to large outliers, while matching many
observations exactly. We demonstrate the approach for basis pursuit denoise
(BPDN) problems as well as for extensions of BPDN to matrix factorization, with
applications to interpolation and denoising of 5D seismic data. The new methods
are particularly promising for seismic applications, where the amplitude in the
data varies significantly, and measurement noise in low-amplitude regions can
wreak havoc for standard Gaussian error models.
Large Scale Audio-Visual Video Analytics Platform for Forensic Investigations of Terroristic Attacks
Authors: Alexander Schindler, Martin Boyer, Andrew Lindley, David Schreiber, Thomas Philipp
Date: 2018-11-28 00:00:00
Tasks: Event Detection, object-detection, Object Detection

The forensic investigation of a terrorist attack poses a huge challenge to
the investigative authorities, as several thousand hours of video footage need
to be spotted. To assist law enforcement agencies (LEA) in identifying suspects
and securing evidences, we present a platform which fuses information of
surveillance cameras and video uploads from eyewitnesses. The platform
integrates analytical modules for different input-modalities on a scalable
architecture. Videos are analyzed according their acoustic and visual content.
Specifically, Audio Event Detection is applied to index the content according
to attack-specific acoustic concepts. Audio similarity search is utilized to
identify similar video sequences recorded from different perspectives. Visual
object detection and tracking are used to index the content according to
relevant concepts. The heterogeneous results of the analytical modules are
fused into a distributed index of visual and acoustic concepts to facilitate
rapid start of investigations, following traits and investigating witness
reports.
Multi-step Time Series Forecasting Using Ridge Polynomial Neural Network with Error-Output Feedbacks
Authors: Waddah Waheeb, Rozaida Ghazali
Date: 2018-11-28 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

Time series forecasting gets much attention due to its impact on many
practical applications. Higher-order neural network with recurrent feedback is
a powerful technique which used successfully for forecasting. It maintains fast
learning and the ability to learn the dynamics of the series over time. For
that, in this paper, we propose a novel model which is called Ridge Polynomial
Neural Network with Error-Output Feedbacks (RPNN-EOFs) that combines the
properties of higher order and error-output feedbacks. The well-known
Mackey-Glass time series is used to test the forecasting capability of
RPNN-EOFS. Simulation results showed that the proposed RPNN-EOFs provides
better understanding for the Mackey-Glass time series with root mean square
error equal to 0.00416. This result is smaller than other models in the
literature. Therefore, we can conclude that the RPNN-EOFs can be applied
successfully for time series forecasting.
Kalman filter demystified: from intuition to probabilistic graphical model to real case in financial markets
Authors: Eric Benhamou
Date: 2018-11-28 00:00:00

In this paper, we revisit the Kalman filter theory. After giving the
intuition on a simplified financial markets example, we revisit the maths
underlying it. We then show that Kalman filter can be presented in a very
different fashion using graphical models. This enables us to establish the
connection between Kalman filter and Hidden Markov Models. We then look at
their application in financial markets and provide various intuitions in terms
of their applicability for complex systems such as financial markets. Although
this paper has been written more like a self contained work connecting Kalman
filter to Hidden Markov Models and hence revisiting well known and establish
results, it contains new results and brings additional contributions to the
field. First, leveraging on the link between Kalman filter and HMM, it gives
new algorithms for inference for extended Kalman filters. Second, it presents
an alternative to the traditional estimation of parameters using EM algorithm
thanks to the usage of CMA-ES optimization. Third, it examines the application
of Kalman filter and its Hidden Markov models version to financial markets,
providing various dynamics assumptions and tests. We conclude by connecting
Kalman filter approach to trend following technical analysis system and showing
their superior performances for trend following detection.
Deep Reinforcement Learning for Time Optimal Velocity Control using Prior Knowledge
Authors: Gabriel Hartmann, Zvi Shiller, Amos Azaria
Date: 2018-11-28 00:00:00
Tasks: Autonomous Navigation, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomous navigation has recently gained great interest in the field of reinforcement learning. However, little attention was given to the time optimal velocity control problem, i.e. controlling a vehicle such that it travels at the maximal speed without becoming dynamically unstable (roll-over or sliding). Time optimal velocity control can be solved numerically using existing methods that are based on optimal control and vehicle dynamics. In this paper, we use deep reinforcement learning to generate the time optimal velocity control. Furthermore, we use the numerical solution to further improve the performance of the reinforcement learner. It is shown that the reinforcement learner outperforms the numerically derived solution, and that the hybrid approach (combining learning with the numerical solution) speeds up the training process.
A Generative Appearance Model for End-to-end Video Object Segmentation
Authors: Joakim Johnander, Martin Danelljan, Emil Brissman, Fahad Shahbaz Khan, Michael Felsberg
Date: 2018-11-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: GPU, One-shot visual object segmentation, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

One of the fundamental challenges in video object segmentation is to find an
effective representation of the target and background appearance. The best
performing approaches resort to extensive fine-tuning of a convolutional neural
network for this purpose. Besides being prohibitively expensive, this strategy
cannot be truly trained end-to-end since the online fine-tuning procedure is
not integrated into the offline training of the network.
  To address these issues, we propose a network architecture that learns a
powerful representation of the target and background appearance in a single
forward pass. The introduced appearance module learns a probabilistic
generative model of target and background feature distributions. Given a new
image, it predicts the posterior class probabilities, providing a highly
discriminative cue, which is processed in later network modules. Both the
learning and prediction stages of our appearance module are fully
differentiable, enabling true end-to-end training of the entire segmentation
pipeline. Comprehensive experiments demonstrate the effectiveness of the
proposed approach on three video object segmentation benchmarks. We close the
gap to approaches based on online fine-tuning on DAVIS17, while operating at 15
FPS on a single GPU. Furthermore, our method outperforms all published
approaches on the large-scale YouTube-VOS dataset.
Escaping Plato's Cave: 3D Shape From Adversarial Rendering
Authors: Philipp Henzler, Niloy Mitra, Tobias Ritschel
Date: 2018-11-28 00:00:00
Proceeding: ICCV 2019 10

We introduce PlatonicGAN to discover the 3D structure of an object class from an unstructured collection of 2D images, i.e., where no relation between photos is known, except that they are showing instances of the same category. The key idea is to train a deep neural network to generate 3D shapes which, when rendered to images, are indistinguishable from ground truth images (for a discriminator) under various camera poses. Discriminating 2D images instead of 3D shapes allows tapping into unstructured 2D photo collections instead of relying on curated (e.g., aligned, annotated, etc.) 3D data sets. To establish constraints between 2D image observation and their 3D interpretation, we suggest a family of rendering layers that are effectively differentiable. This family includes visual hull, absorption-only (akin to x-ray), and emission-absorption. We can successfully reconstruct 3D shapes from unstructured 2D images and extensively evaluate PlatonicGAN on a range of synthetic and real data sets achieving consistent improvements over baseline methods. We further show that PlatonicGAN can be combined with 3D supervision to improve on and in some cases even surpass the quality of 3D-supervised methods.
Automated Algorithm Selection: Survey and Perspectives
Authors: Pascal Kerschke, Holger H. Hoos, Frank Neumann, Heike Trautmann
Date: 2018-11-28 00:00:00
Tasks: Scheduling, Survey

It has long been observed that for practically any computational problem that
has been intensely studied, different instances are best solved using different
algorithms. This is particularly pronounced for computationally hard problems,
where in most cases, no single algorithm defines the state of the art; instead,
there is a set of algorithms with complementary strengths. This performance
complementarity can be exploited in various ways, one of which is based on the
idea of selecting, from a set of given algorithms, for each problem instance to
be solved the one expected to perform best. The task of automatically selecting
an algorithm from a given set is known as the per-instance algorithm selection
problem and has been intensely studied over the past 15 years, leading to major
improvements in the state of the art in solving a growing number of discrete
combinatorial problems, including propositional satisfiability and AI planning.
Per-instance algorithm selection also shows much promise for boosting
performance in solving continuous and mixed discrete/continuous optimisation
problems.
  This survey provides an overview of research in automated algorithm
selection, ranging from early and seminal works to recent and promising
application areas. Different from earlier work, it covers applications to
discrete and continuous problems, and discusses algorithm selection in context
with conceptually related approaches, such as algorithm configuration,
scheduling or portfolio selection. Since informative and cheaply computable
problem instance features provide the basis for effective per-instance
algorithm selection systems, we also provide an overview of such features for
discrete and continuous problems. Finally, we provide perspectives on future
work in the area and discuss a number of open research challenges.
Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks
Authors: Wei Hu, Gusi Te, Ju He, Dong Chen, Zongming Guo
Date: 2018-11-28 00:00:00
Tasks: Face Anti-Spoofing, Face Recognition

Face anti-spoofing plays a crucial role in protecting face recognition
systems from various attacks. Previous model-based and deep learning approaches
achieve satisfactory performance for 2D face spoofs, but remain limited for
more advanced 3D attacks such as vivid masks. In this paper, we address 3D face
anti-spoofing via the proposed Hypergraph Convolutional Neural Networks
(HGCNN). Firstly, we construct a computation-efficient and posture-invariant
face representation with only a few key points on hypergraphs. The hypergraph
representation is then fed into the designed HGCNN with hypergraph convolution
for feature extraction, while the depth auxiliary is also exploited for 3D mask
anti-spoofing. Further, we build a 3D face attack database with color, depth
and infrared light information to overcome the deficiency of 3D face
anti-spoofing data. Experiments show that our method achieves the
state-of-the-art performance over widely used 3D and 2D databases as well as
the proposed one under various tests.