pwc-archive/papers-with-abstracts huggingface dataset:
Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches
Authors: Sherif Abdulatif, Qian Wei, Fady Aziz, Bernhard Kleiner, Urs Schneider
Date: 2017-11-25 00:00:00
Tasks: Classification, Gait Recognition, General Classification

Radar sensors can be used for analyzing the induced frequency shifts due to
micro-motions in both range and velocity dimensions identified as micro-Doppler
($\boldsymbol{\mu}$-D) and micro-Range ($\boldsymbol{\mu}$-R), respectively.
Different moving targets will have unique $\boldsymbol{\mu}$-D and
$\boldsymbol{\mu}$-R signatures that can be used for target classification.
Such classification can be used in numerous fields, such as gait recognition,
safety and surveillance. In this paper, a 25 GHz FMCW Single-Input
Single-Output (SISO) radar is used in industrial safety for real-time
human-robot identification. Due to the real-time constraint, joint
Range-Doppler (R-D) maps are directly analyzed for our classification problem.
Furthermore, a comparison between the conventional classical learning
approaches with handcrafted extracted features, ensemble classifiers and deep
learning approaches is presented. For ensemble classifiers, restructured range
and velocity profiles are passed directly to ensemble trees, such as gradient
boosting and random forest without feature extraction. Finally, a Deep
Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed
into the constructed network. DCNN shows a superior performance of 99\%
accuracy in identifying humans from robots on a single R-D map.
Deep Neural Networks for Multiple Speaker Detection and Localization
Authors: Weipeng He, Petr Motlicek, Jean-Marc Odobez
Date: 2017-11-30 00:00:00
Tasks: Sound Source Localization

We propose to use neural networks for simultaneous detection and localization
of multiple sound sources in human-robot interaction. In contrast to
conventional signal processing techniques, neural network-based sound source
localization methods require fewer strong assumptions about the environment.
Previous neural network-based methods have been focusing on localizing a single
sound source, which do not extend to multiple sources in terms of detection and
localization. In this paper, we thus propose a likelihood-based encoding of the
network output, which naturally allows the detection of an arbitrary number of
sources. In addition, we investigate the use of sub-band cross-correlation
information as features for better localization in sound mixtures, as well as
three different network architectures based on different motivations.
Experiments on real data recorded from a robot show that our proposed methods
significantly outperform the popular spatial spectrum-based approaches.
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
Authors: Tal Ben-Nun, Torsten Hoefler
Date: 2018-02-26 00:00:00
Tasks: Deep Learning, Neural Architecture Search, Stochastic Optimization

Deep Neural Networks (DNNs) are becoming an important tool in modern
computing applications. Accelerating their training is a major challenge and
techniques range from distributed algorithms to low-level circuit design. In
this survey, we describe the problem from a theoretical perspective, followed
by approaches for its parallelization. We present trends in DNN architectures
and the resulting implications on parallelization strategies. We then review
and model the different types of concurrency in DNNs: from the single operator,
through parallelism in network inference and training, to distributed deep
learning. We discuss asynchronous stochastic optimization, distributed system
architectures, communication schemes, and neural architecture search. Based on
those approaches, we extrapolate potential directions for parallelism in deep
learning.
Kernel Cross-Correlator
Authors: Chen Wang, Le Zhang, Lihua Xie, Junsong Yuan
Date: 2017-09-12 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, object-detection, Object Detection, Visual Tracking

Cross-correlator plays a significant role in many visual perception tasks,
such as object detection and tracking. Beyond the linear cross-correlator, this
paper proposes a kernel cross-correlator (KCC) that breaks traditional
limitations. First, by introducing the kernel trick, the KCC extends the linear
cross-correlation to non-linear space, which is more robust to signal noises
and distortions. Second, the connection to the existing works shows that KCC
provides a unified solution for correlation filters. Third, KCC is applicable
to any kernel function and is not limited to circulant structure on training
data, thus it is able to predict affine transformations with customized
properties. Last, by leveraging the fast Fourier transform (FFT), KCC
eliminates direct calculation of kernel vectors, thus achieves better
performance yet still with a reasonable computational cost. Comprehensive
experiments on visual tracking and human activity recognition using wearable
devices demonstrate its robustness, flexibility, and efficiency. The source
codes of both experiments are released at https://github.com/wang-chen/KCC
AI4AI: Quantitative Methods for Classifying Host Species from Avian Influenza DNA Sequence
Authors: Woo Yong Choi, Kyu Ye Song, Chan Woo Lee
Date: 2018-02-26 00:00:00
Tasks: Classification, Deep Learning, General Classification

Avian Influenza breakouts cause millions of dollars in damage each year
globally, especially in Asian countries such as China and South Korea. The
impact magnitude of a breakout directly correlates to time required to fully
understand the influenza virus, particularly the interspecies pathogenicity.
The procedure requires laboratory tests that require resources typically
lacking in a breakout emergency. In this study, we propose new quantitative
methods utilizing machine learning and deep learning to correctly classify host
species given raw DNA sequence data of the influenza virus, and provide
probabilities for each classification. The best deep learning models achieve
top-1 classification accuracy of 47%, and top-3 classification accuracy of 82%,
on a dataset of 11 host species classes.
Deep Feed-forward Sequential Memory Networks for Speech Synthesis
Authors: Mengxiao Bi, Heng Lu, Shiliang Zhang, Ming Lei, Zhijie Yan
Date: 2018-02-26 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Synthesis, text-to-speech, Text to Speech

The Bidirectional LSTM (BLSTM) RNN based speech synthesis system is among the
best parametric Text-to-Speech (TTS) systems in terms of the naturalness of
generated speech, especially the naturalness in prosody. However, the model
complexity and inference cost of BLSTM prevents its usage in many runtime
applications. Meanwhile, Deep Feed-forward Sequential Memory Networks (DFSMN)
has shown its consistent out-performance over BLSTM in both word error rate
(WER) and the runtime computation cost in speech recognition tasks. Since
speech synthesis also requires to model long-term dependencies compared to
speech recognition, in this paper, we investigate the Deep-FSMN (DFSMN) in
speech synthesis. Both objective and subjective experiments show that, compared
with BLSTM TTS method, the DFSMN system can generate synthesized speech with
comparable speech quality while drastically reduce model complexity and speech
generation time.
Language Distribution Prediction based on Batch Markov Monte Carlo Simulation with Migration
Authors: XingYu Fu, ZiYi Yang, XiuWen Duan
Date: 2018-02-26 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Language spreading is a complex mechanism that involves issues like culture,
economics, migration, population etc. In this paper, we propose a set of
methods to model the dynamics of the spreading system. To model the randomness
of language spreading, we propose the Batch Markov Monte Carlo Simulation with
Migration(BMMCSM) algorithm, in which each agent is treated as a language
stack. The agent learns languages and migrates based on the proposed Batch
Markov Property according to the transition matrix T and migration matrix M.
Since population plays a crucial role in language spreading, we also introduce
the Mortality and Fertility Mechanism, which controls the birth and death of
the simulated agents, into the BMMCSM algorithm. The simulation results of
BMMCSM show that the numerical and geographic distribution of languages varies
across the time. The change of distribution fits the world cultural and
economic development trend. Next, when we construct Matrix T, there are some
entries of T can be directly calculated from historical statistics while some
entries of T is unknown. Thus, the key to the success of the BMMCSM lies in the
accurate estimation of transition matrix T by estimating the unknown entries of
T under the supervision of the known entries. To achieve this, we first
construct a 20 by 20 by 5 factor tensor X to characterize each entry of T. Then
we train a Random Forest Regressor on the known entries of T and use the
trained regressor to predict the unknown entries. The reason why we choose
Random Forest(RF) is that, compared to Single Decision Tree, it conquers the
problem of over fitting and the Shapiro test also suggests that the residual of
RF subjects to the Normal distribution.
Noisy Natural Gradient as Variational Inference
Authors: Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
Date: 2017-12-06 00:00:00
Proceeding: ICML 2018 7
Tasks: Active Learning, Efficient Exploration, Reinforcement Learning, Reinforcement Learning (RL), Variational Inference

Variational Bayesian neural nets combine the flexibility of deep learning
with Bayesian uncertainty estimation. Unfortunately, there is a tradeoff
between cheap but simple variational families (e.g.~fully factorized) or
expensive and complicated inference procedures. We show that natural gradient
ascent with adaptive weight noise implicitly fits a variational posterior to
maximize the evidence lower bound (ELBO). This insight allows us to train
full-covariance, fully factorized, or matrix-variate Gaussian variational
posteriors using noisy versions of natural gradient, Adam, and K-FAC,
respectively, making it possible to scale up to modern-size ConvNets. On
standard regression benchmarks, our noisy K-FAC algorithm makes better
predictions and matches Hamiltonian Monte Carlo's predictive variances better
than existing methods. Its improved uncertainty estimates lead to more
efficient exploration in active learning, and intrinsic motivation for
reinforcement learning.
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Authors: Naveed Akhtar, Ajmal Mian
Date: 2018-01-02 00:00:00
Tasks: Deep Learning, Self-Driving Cars

Deep learning is at the heart of the current rise of machine learning and
artificial intelligence. In the field of Computer Vision, it has become the
workhorse for applications ranging from self-driving cars to surveillance and
security. Whereas deep neural networks have demonstrated phenomenal success
(often beyond human capabilities) in solving complex problems, recent studies
show that they are vulnerable to adversarial attacks in the form of subtle
perturbations to inputs that lead a model to predict incorrect outputs. For
images, such perturbations are often too small to be perceptible, yet they
completely fool the deep learning models. Adversarial attacks pose a serious
threat to the success of deep learning in practice. This fact has lead to a
large influx of contributions in this direction. This article presents the
first comprehensive survey on adversarial attacks on deep learning in Computer
Vision. We review the works that design adversarial attacks, analyze the
existence of such attacks and propose defenses against them. To emphasize that
adversarial attacks are possible in practical conditions, we separately review
the contributions that evaluate adversarial attacks in the real-world
scenarios. Finally, we draw on the literature to provide a broader outlook of
the research direction.
Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning
Authors: Lei Tai, Jingwei Zhang, Ming Liu, Wolfram Burgard
Date: 2017-10-06 00:00:00
Tasks: Autonomous Vehicles, Imitation Learning, Navigate

We present an approach for mobile robots to learn to navigate in dynamic
environments with pedestrians via raw depth inputs, in a socially compliant
manner. To achieve this, we adopt a generative adversarial imitation learning
(GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our
approach overcomes the disadvantages of previous methods, as they heavily
depend on the full knowledge of the location and velocity information of nearby
pedestrians, which not only requires specific sensors, but also the extraction
of such state information from raw sensory input could consume much computation
time. In this paper, our proposed GAIL-based model performs directly on raw
depth inputs and plans in real-time. Experiments show that our GAIL-based
approach greatly improves the safety and efficiency of the behavior of mobile
robots from pure behavior cloning. The real-world deployment also shows that
our method is capable of guiding autonomous vehicles to navigate in a socially
compliant manner directly through raw depth inputs. In addition, we release a
simulation plugin for modeling pedestrian behaviors based on the social force
model.
Information Theoretic Optimal Learning of Gaussian Graphical Models
Authors: Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov
Date: 2017-03-15 00:00:00
Tasks: Graph Reconstruction

What is the optimal number of independent observations from which a sparse
Gaussian Graphical Model can be correctly recovered? Information-theoretic
arguments provide a lower bound on the minimum number of samples necessary to
perfectly identify the support of any multivariate normal distribution as a
function of model parameters. For a model defined on a sparse graph with $p$
nodes, a maximum degree $d$ and minimum normalized edge strength $\kappa$, this
necessary number of samples scales at least as $d \log p/\kappa^2$. The sample
complexity requirements of existing methods for perfect graph reconstruction
exhibit dependency on additional parameters that do not enter in the lower
bound. The question of whether the lower bound is tight and achievable by a
polynomial time algorithm remains open. In this paper, we constructively answer
this question and propose an algorithm, termed DICE, whose sample complexity
matches the information-theoretic lower bound up to a universal constant
factor. We also propose a related algorithm SLICE that has a slightly higher
sample complexity, but can be implemented as a mixed integer quadratic program
which makes it attractive in practice. Importantly, SLICE retains a critical
advantage of DICE in that its sample complexity only depends on quantities
present in the information theoretic lower bound. We anticipate that this
result will stimulate future search of computationally efficient sample-optimal
algorithms.
Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising
Authors: Di Wu, Xiujun Chen, Xun Yang, Hao Wang, Qing Tan, Xiaoxun Zhang, Jian Xu, Kun Gai
Date: 2018-02-23 00:00:00
Tasks: Marketing, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Real-time bidding (RTB) is an important mechanism in online display
advertising, where a proper bid for each page view plays an essential role for
good marketing results. Budget constrained bidding is a typical scenario in RTB
where the advertisers hope to maximize the total value of the winning
impressions under a pre-set budget constraint. However, the optimal bidding
strategy is hard to be derived due to the complexity and volatility of the
auction environment. To address these challenges, in this paper, we formulate
budget constrained bidding as a Markov Decision Process and propose a
model-free reinforcement learning framework to resolve the optimization
problem. Our analysis shows that the immediate reward from environment is
misleading under a critical resource constraint. Therefore, we innovate a
reward function design methodology for the reinforcement learning problems with
constraints. Based on the new reward design, we employ a deep neural network to
learn the appropriate reward so that the optimal policy can be learned
effectively. Different from the prior model-based work, which suffers from the
scalability problem, our framework is easy to be deployed in large-scale
industrial applications. The experimental evaluations demonstrate the
effectiveness of our framework on large-scale real datasets.
Antifragility for Intelligent Autonomous Systems
Authors: Anusha Mujumdar, Swarup Kumar Mohalik, Ramamurthy Badrinath
Date: 2018-02-26 00:00:00

Antifragile systems grow measurably better in the presence of hazards. This
is in contrast to fragile systems which break down in the presence of hazards,
robust systems that tolerate hazards up to a certain degree, and resilient
systems that -- like self-healing systems -- revert to their earlier expected
behavior after a period of convalescence. The notion of antifragility was
introduced by Taleb for economics systems, but its applicability has been
illustrated in biological and engineering domains as well. In this paper, we
propose an architecture that imparts antifragility to intelligent autonomous
systems, specifically those that are goal-driven and based on AI-planning. We
argue that this architecture allows the system to self-improve by uncovering
new capabilities obtained either through the hazards themselves (opportunistic)
or through deliberation (strategic). An AI planning-based case study of an
autonomous wheeled robot is presented. We show that with the proposed
architecture, the robot develops antifragile behaviour with respect to an oil
spill hazard.
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
Authors: Fangchang Ma, Sertac Karaman
Date: 2017-09-21 00:00:00
Tasks: Depth Estimation, Depth Prediction, Prediction, Simultaneous Localization and Mapping, Super-Resolution

We consider the problem of dense depth prediction from a sparse set of depth
measurements and a single RGB image. Since depth estimation from monocular
images alone is inherently ambiguous and unreliable, to attain a higher level
of robustness and accuracy, we introduce additional sparse depth samples, which
are either acquired with a low-resolution depth sensor or computed via visual
Simultaneous Localization and Mapping (SLAM) algorithms. We propose the use of
a single deep regression network to learn directly from the RGB-D raw data, and
explore the impact of number of depth samples on prediction accuracy. Our
experiments show that, compared to using only RGB images, the addition of 100
spatially random depth samples reduces the prediction root-mean-square error by
50% on the NYU-Depth-v2 indoor dataset. It also boosts the percentage of
reliable prediction from 59% to 92% on the KITTI dataset. We demonstrate two
applications of the proposed algorithm: a plug-in module in SLAM to convert
sparse maps to dense maps, and super-resolution for LiDARs. Software and video
demonstration are publicly available.
Classification and Geometry of General Perceptual Manifolds
Authors: SueYeon Chung, Daniel D. Lee, Haim Sompolinsky
Date: 2017-10-17 00:00:00
Tasks: Classification, General Classification, Object, Object Recognition

Perceptual manifolds arise when a neural population responds to an ensemble
of sensory signals associated with different physical features (e.g.,
orientation, pose, scale, location, and intensity) of the same perceptual
object. Object recognition and discrimination requires classifying the
manifolds in a manner that is insensitive to variability within a manifold. How
neuronal systems give rise to invariant object classification and recognition
is a fundamental problem in brain theory as well as in machine learning. Here
we study the ability of a readout network to classify objects from their
perceptual manifold representations. We develop a statistical mechanical theory
for the linear classification of manifolds with arbitrary geometry revealing a
remarkable relation to the mathematics of conic decomposition. Novel
geometrical measures of manifold radius and manifold dimension are introduced
which can explain the classification capacity for manifolds of various
geometries. The general theory is demonstrated on a number of representative
manifolds, including L2 ellipsoids prototypical of strictly convex manifolds,
L1 balls representing polytopes consisting of finite sample points, and
orientation manifolds which arise from neurons tuned to respond to a continuous
angle variable, such as object orientation. The effects of label sparsity on
the classification capacity of manifolds are elucidated, revealing a scaling
relation between label sparsity and manifold radius. Theoretical predictions
are corroborated by numerical simulations using recently developed algorithms
to compute maximum margin solutions for manifold dichotomies. Our theory and
its extensions provide a powerful and rich framework for applying statistical
mechanics of linear classification to data arising from neuronal responses to
object stimuli, as well as to artificial deep networks trained for object
recognition tasks.
Active Learning with Partial Feedback
Authors: Peiyun Hu, Zachary C. Lipton, Anima Anandkumar, Deva Ramanan
Date: 2018-02-21 00:00:00
Proceeding: ICLR 2019 5
Tasks: Active Learning

While many active learning papers assume that the learner can simply ask for a label and receive it, real annotation often presents a mismatch between the form of a label (say, one among many classes), and the form of an annotation (typically yes/no binary feedback). To annotate examples corpora for multiclass classification, we might need to ask multiple yes/no questions, exploiting a label hierarchy if one is available. To address this more realistic setting, we propose active learning with partial feedback (ALPF), where the learner must actively choose both which example to label and which binary question to ask. At each step, the learner selects an example, asking if it belongs to a chosen (possibly composite) class. Each answer eliminates some classes, leaving the learner with a partial label. The learner may then either ask more questions about the same example (until an exact label is uncovered) or move on immediately, leaving the first example partially labeled. Active learning with partial labels requires (i) a sampling strategy to choose (example, class) pairs, and (ii) learning from partial labels between rounds. Experiments on Tiny ImageNet demonstrate that our most effective method improves 26% (relative) in top-1 classification accuracy compared to i.i.d. baselines and standard active learners given 30% of the annotation budget that would be required (naively) to annotate the dataset. Moreover, ALPF-learners fully annotate TinyImageNet at 42% lower cost. Surprisingly, we observe that accounting for per-example annotation costs can alter the conventional wisdom that active learners should solicit labels for hard examples.