pwc-archive/papers-with-abstracts huggingface dataset:
Sparse Representations for Object and Ego-motion Estimation in Dynamic Scenes
Authors: Hirak J. Kashyap, Charless Fowlkes, Jeffrey L. Krichmar
Date: 2019-03-09 00:00:00
Tasks: Depth And Camera Motion, Monocular Visual Odometry, Motion Estimation, Object, Prediction, Trajectory Prediction, Visual Odometry

Dynamic scenes that contain both object motion and egomotion are a challenge
for monocular visual odometry (VO). Another issue with monocular VO is the
scale ambiguity, i.e. these methods cannot estimate scene depth and camera
motion in real scale. Here, we propose a learning based approach to predict
camera motion parameters directly from optic flow, by marginalizing depthmap
variations and outliers. This is achieved by learning a sparse overcomplete
basis set of egomotion in an autoencoder network, which is able to eliminate
irrelevant components of optic flow for the task of camera parameter or
motionfield estimation. The model is trained using a sparsity regularizer and a
supervised egomotion loss, and achieves the state-of-the-art performances on
trajectory prediction and camera rotation prediction tasks on KITTI and Virtual
KITTI datasets, respectively. The sparse latent space egomotion representation
learned by the model is robust and requires only 5% of the hidden layer neurons
to maintain the best trajectory prediction accuracy on KITTI dataset.
Additionally, in presence of depth information, the proposed method
demonstrates faithful object velocity prediction for wide range of object sizes
and speeds by global compensation of predicted egomotion and a divisive
normalization procedure.
Learning Quantum Graphical Models using Constrained Gradient Descent on the Stiefel Manifold
Authors: Sandesh Adhikary, Siddarth Srinivasan, Byron Boots
Date: 2019-03-09 00:00:00

Quantum graphical models (QGMs) extend the classical framework for reasoning
about uncertainty by incorporating the quantum mechanical view of probability.
Prior work on QGMs has focused on hidden quantum Markov models (HQMMs), which
can be formulated using quantum analogues of the sum rule and Bayes rule used
in classical graphical models. Despite the focus on developing the QGM
framework, there has been little progress in learning these models from data.
The existing state-of-the-art approach randomly initializes parameters and
iteratively finds unitary transformations that increase the likelihood of the
data. While this algorithm demonstrated theoretical strengths of HQMMs over
HMMs, it is slow and can only handle a small number of hidden states. In this
paper, we tackle the learning problem by solving a constrained optimization
problem on the Stiefel manifold using a well-known retraction-based algorithm.
We demonstrate that this approach is not only faster and yields better
solutions on several datasets, but also scales to larger models that were
prohibitively slow to train via the earlier method.
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
Authors: Weizhi Ma, Min Zhang, Yue Cao, Woojeong, Jin, Chenyang Wang, Yiqun Liu, Shaoping Ma, Xiang Ren
Date: 2019-03-09 00:00:00
Tasks: Explainable Recommendation, Knowledge Graphs, Recommendation Systems

Explainability and effectiveness are two key aspects for building recommender
systems. Prior efforts mostly focus on incorporating side information to
achieve better recommendation performance. However, these methods have some
weaknesses: (1) prediction of neural network-based embedding methods are hard
to explain and debug; (2) symbolic, graph-based approaches (e.g., meta
path-based models) require manual efforts and domain knowledge to define
patterns and rules, and ignore the item association types (e.g. substitutable
and complementary). In this paper, we propose a novel joint learning framework
to integrate \textit{induction of explainable rules from knowledge graph} with
\textit{construction of a rule-guided neural recommendation model}. The
framework encourages two modules to complement each other in generating
effective and explainable recommendation: 1) inductive rules, mined from
item-centric knowledge graphs, summarize common multi-hop relational patterns
for inferring different item associations and provide human-readable
explanation for model prediction; 2) recommendation module can be augmented by
induced rules and thus have better generalization ability dealing with the
cold-start issue. Extensive experiments\footnote{Code and data can be found at:
\url{https://github.com/THUIR/RuleRec}} show that our proposed method has
achieved significant improvements in item recommendation over baselines on
real-world datasets. Our model demonstrates robust performance over "noisy"
item knowledge graphs, generated by linking item names to related entities.
Deep Learning-Based Constellation Optimization for Physical Network Coding in Two-Way Relay Networks
Authors: Toshiki Matsumine, Toshiaki Koike-Akino, Ye Wang
Date: 2019-03-09 00:00:00

This paper studies a new application of deep learning (DL) for optimizing
constellations in two-way relaying with physical-layer network coding (PNC),
where deep neural network (DNN)-based modulation and demodulation are employed
at each terminal and relay node. We train DNNs such that the cross entropy loss
is directly minimized, and thus it maximizes the likelihood, rather than
considering the Euclidean distance of the constellations. The proposed scheme
can be extended to higher level constellations with slight modification of the
DNN structure. Simulation results demonstrate a significant performance gain in
terms of the achievable sum rate over conventional relaying schemes.
Furthermore, since our DNN demodulator directly outputs bit-wise probabilities,
it is straightforward to concatenate with soft-decision channel decoding.
Adaptive Power System Emergency Control using Deep Reinforcement Learning
Authors: Qiuhua Huang, Renke Huang, Weituo Hao, Jie Tan, Rui Fan, Zhenyu Huang
Date: 2019-03-09 00:00:00
Tasks: Benchmarking, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Power system emergency control is generally regarded as the last safety net
for grid security and resiliency. Existing emergency control schemes are
usually designed off-line based on either the conceived "worst" case scenario
or a few typical operation scenarios. These schemes are facing significant
adaptiveness and robustness issues as increasing uncertainties and variations
occur in modern electrical grids. To address these challenges, for the first
time, this paper developed novel adaptive emergency control schemes using deep
reinforcement learning (DRL), by leveraging the high-dimensional feature
extraction and non-linear generalization capabilities of DRL for complex power
systems. Furthermore, an open-source platform named RLGC has been designed for
the first time to assist the development and benchmarking of DRL algorithms for
power system control. Details of the platform and DRL-based emergency control
schemes for generator dynamic braking and under-voltage load shedding are
presented. Extensive case studies performed in both two-area four-machine
system and IEEE 39-Bus system have demonstrated the excellent performance and
robustness of the proposed schemes.
Learning to Modulate for Non-coherent MIMO
Authors: Ye Wang, Toshiaki Koike-Akino
Date: 2019-03-09 00:00:00

The deep learning trend has recently impacted a variety of fields, including
communication systems, where various approaches have explored the application
of neural networks in place of traditional designs. Neural networks flexibly
allow for data/simulation-driven optimization, but are often employed as black
boxes detached from direct application of domain knowledge. Our work considers
learning-based approaches addressing modulation and signal detection design for
the non-coherent MIMO channel. We demonstrate that simulation-driven
optimization can be performed while entirely avoiding neural networks, yet
still perform comparably. Additionally, we show the feasibility of MIMO
communications over extremely short coherence windows (i.e., channel
coefficient stability period), with as few as two time slots.
Linear Bandits with Feature Feedback
Authors: Urvashi Oswal, Aniruddha Bhargava, Robert Nowak
Date: 2019-03-09 00:00:00

This paper explores a new form of the linear bandit problem in which the
algorithm receives the usual stochastic rewards as well as stochastic feedback
about which features are relevant to the rewards, the latter feedback being the
novel aspect. The focus of this paper is the development of new theory and
algorithms for linear bandits with feature feedback. We show that linear
bandits with feature feedback can achieve regret over time horizon $T$ that
scales like $k\sqrt{T}$, without prior knowledge of which features are relevant
nor the number $k$ of relevant features. In comparison, the regret of
traditional linear bandits is $d\sqrt{T}$, where $d$ is the total number of
(relevant and irrelevant) features, so the improvement can be dramatic if $k\ll
d$. The computational complexity of the new algorithm is proportional to $k$
rather than $d$, making it much more suitable for real-world applications
compared to traditional linear bandits. We demonstrate the performance of the
new algorithm with synthetic and real human-labeled data.
NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
Authors: Matthew Hoffman, Pavel Sountsov, Joshua V. Dillon, Ian Langmore, Dustin Tran, Srinivas Vasudevan
Date: 2019-03-09 00:00:00
Tasks: Variational Inference

Hamiltonian Monte Carlo is a powerful algorithm for sampling from
difficult-to-normalize posterior distributions. However, when the geometry of
the posterior is unfavorable, it may take many expensive evaluations of the
target distribution and its gradient to converge and mix. We propose neural
transport (NeuTra) HMC, a technique for learning to correct this sort of
unfavorable geometry using inverse autoregressive flows (IAF), a powerful
neural variational inference technique. The IAF is trained to minimize the KL
divergence from an isotropic Gaussian to the warped posterior, and then HMC
sampling is performed in the warped space. We evaluate NeuTra HMC on a variety
of synthetic and real problems, and find that it significantly outperforms
vanilla HMC both in time to reach the stationary distribution and asymptotic
effective-sample-size rates.
Skew-Fit: State-Covering Self-Supervised Reinforcement Learning
Authors: Vitchyr H. Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, Sergey Levine
Date: 2019-03-08 00:00:00
Proceeding: ICML 2020 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), valid

Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills. Defining each skill with a manually-designed reward function limits this repertoire and imposes a manual engineering burden. Self-supervised agents that set their own goals can automate this process, but designing appropriate goal setting objectives can be difficult, and often involves heuristic design decisions. In this paper, we propose a formal exploration objective for goal-reaching policies that maximizes state coverage. We show that this objective is equivalent to maximizing goal reaching performance together with the entropy of the goal distribution, where goals correspond to full state observations. To instantiate this principle, we present an algorithm called Skew-Fit for learning a maximum-entropy goal distributions. We prove that, under regularity conditions, Skew-Fit converges to a uniform distribution over the set of valid states, even when we do not know this set beforehand. Our experiments show that combining Skew-Fit for learning goal distributions with existing goal-reaching methods outperforms a variety of prior methods on open-sourced visual goal-reaching tasks. Moreover, we demonstrate that Skew-Fit enables a real-world robot to learn to open a door, entirely from scratch, from pixels, and without any manually-designed reward function.
Image Privacy Prediction Using Deep Neural Networks
Authors: Ashwini Tonge, Cornelia Caragea
Date: 2019-03-08 00:00:00
Tasks: Object Recognition, Prediction, TAG

Images today are increasingly shared online on social networking sites such
as Facebook, Flickr, Foursquare, and Instagram. Despite that current social
networking sites allow users to change their privacy preferences, this is often
a cumbersome task for the vast majority of users on the Web, who face
difficulties in assigning and managing privacy settings. Thus, automatically
predicting images' privacy to warn users about private or sensitive content
before uploading these images on social networking sites has become a necessity
in our current interconnected world.
  In this paper, we explore learning models to automatically predict
appropriate images' privacy as private or public using carefully identified
image-specific features. We study deep visual semantic features that are
derived from various layers of Convolutional Neural Networks (CNNs) as well as
textual features such as user tags and deep tags generated from deep CNNs.
Particularly, we extract deep (visual and tag) features from four pre-trained
CNN architectures for object recognition, i.e., AlexNet, GoogLeNet, VGG-16, and
ResNet, and compare their performance for image privacy prediction. Results of
our experiments on a Flickr dataset of over thirty thousand images show that
the learning models trained on features extracted from ResNet outperform the
state-of-the-art models for image privacy prediction. We further investigate
the combination of user tags and deep tags derived from CNN architectures using
two settings: (1) SVM on the bag-of-tags features; and (2) text-based CNN. Our
results show that even though the models trained on the visual features perform
better than those trained on the tag features, the combination of deep visual
features with image tags shows improvements in performance over the individual
feature sets.
Everything old is new again: A multi-view learning approach to learning using privileged information and distillation
Authors: Weiran Wang
Date: 2019-03-08 00:00:00
Tasks: MULTI-VIEW LEARNING, Transfer Learning

We adopt a multi-view approach for analyzing two knowledge transfer
settings---learning using privileged information (LUPI) and distillation---in a
common framework. Under reasonable assumptions about the complexities of
hypothesis spaces, and being optimistic about the expected loss achievable by
the student (in distillation) and a transformed teacher predictor (in LUPI), we
show that encouraging agreement between the teacher and the student leads to
reduced search space. As a result, improved convergence rate can be obtained
with regularized empirical risk minimization.
RoPAD: Robust Presentation Attack Detection through Unsupervised Adversarial Invariance
Authors: Ayush Jaiswal, Shuai Xia, Iacopo Masi, Wael Abd-Almageed
Date: 2019-03-08 00:00:00

For enterprise, personal and societal applications, there is now an
increasing demand for automated authentication of identity from images using
computer vision. However, current authentication technologies are still
vulnerable to presentation attacks. We present RoPAD, an end-to-end deep
learning model for presentation attack detection that employs unsupervised
adversarial invariance to ignore visual distractors in images for increased
robustness and reduced overfitting. Experiments show that the proposed
framework exhibits state-of-the-art performance on presentation attack
detection on several benchmark datasets.
Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter
Authors: Zakaria El Mrabet, Youness Arjoune, Hassan El Ghazi, Badr Abou Al Majd, Naima Kaabouch
Date: 2019-03-08 00:00:00
Tasks: valid

Cognitive radio technology addresses the problem of spectrum scarcity by
allowing secondary users to use the vacant spectrum bands without causing
interference to the primary users. However, several attacks could disturb the
normal functioning of the cognitive radio network. Primary user emulation
attacks are one of the most severe attacks in which a malicious user emulates
the primary user signal characteristics to either prevent other legitimate
secondary users from accessing the idle channels or causing harmful
interference to the primary users. There are several proposed approaches to
detect the primary user emulation attackers. However, most of these techniques
assume that the primary user location is fixed, which does not make them valid
when the primary user is mobile. In this paper, we propose a new approach based
on the Kalman filter framework for detecting the primary user emulation attacks
with a non-stationary primary user. Several experiments have been conducted and
the advantages of the proposed approach are demonstrated through the simulation
results.
Learning Self-Game-Play Agents for Combinatorial Optimization Problems
Authors: Ruiyang Xu, Karl Lieberherr
Date: 2019-03-08 00:00:00
Tasks: Atari Games, Board Games, Combinatorial Optimization, Reinforcement Learning, Reinforcement Learning (RL)

Recent progress in reinforcement learning (RL) using self-game-play has shown remarkable performance on several board games (e.g., Chess and Go) as well as video games (e.g., Atari games and Dota2). It is plausible to consider that RL, starting from zero knowledge, might be able to gradually approximate a winning strategy after a certain amount of training. In this paper, we explore neural Monte-Carlo-Tree-Search (neural MCTS), an RL algorithm which has been applied successfully by DeepMind to play Go and Chess at a super-human level. We try to leverage the computational power of neural MCTS to solve a class of combinatorial optimization problems. Following the idea of Hintikka's Game-Theoretical Semantics, we propose the Zermelo Gamification (ZG) to transform specific combinatorial optimization problems into Zermelo games whose winning strategies correspond to the solutions of the original optimization problem. The ZG also provides a specially designed neural MCTS. We use a combinatorial planning problem for which the ground-truth policy is efficiently computable to demonstrate that ZG is promising.
Deep Learning Based Online Power Control for Large Energy Harvesting Networks
Authors: Mohit K. Sharma, Alessio Zappone, Merouane Debbah, Mohamad Assaad
Date: 2019-03-08 00:00:00
Tasks: Deep Learning

In this paper, we propose a deep learning based approach to design online
power control policies for large EH networks, which are often intractable
stochastic control problems. In the proposed approach, for a given EH network,
the optimal online power control rule is learned by training a deep neural
network (DNN), using the solution of offline policy design problem. Under the
proposed scheme, in a given time slot, the transmit power is obtained by
feeding the current system state to the trained DNN. Our results illustrate
that the DNN based online power control scheme outperforms a Markov decision
process based policy. In general, the proposed deep learning based approach can
be used to find solutions to large intractable stochastic control problems.
Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning
Authors: Xiaobai Ma, Katherine Driggs-Campbell, Mykel J. Kochenderfer
Date: 2019-03-08 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

To improve efficiency and reduce failures in autonomous vehicles, research
has focused on developing robust and safe learning methods that take into
account disturbances in the environment. Existing literature in robust
reinforcement learning poses the learning problem as a two player game between
the autonomous system and disturbances. This paper examines two different
algorithms to solve the game, Robust Adversarial Reinforcement Learning and
Neural Fictitious Self Play, and compares performance on an autonomous driving
scenario. We extend the game formulation to a semi-competitive setting and
demonstrate that the resulting adversary better captures meaningful
disturbances that lead to better overall performance. The resulting robust
policy exhibits improved driving efficiency while effectively reducing
collision rates compared to baseline control policies produced by traditional
reinforcement learning methods.
Imputation estimators for unnormalized models with missing data
Authors: Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim
Date: 2019-03-08 00:00:00
Tasks: Imputation

Several statistical models are given in the form of unnormalized densities, and calculation of the normalization constant is intractable. We propose estimation methods for such unnormalized models with missing data. The key concept is to combine imputation techniques with estimators for unnormalized models including noise contrastive estimation and score matching. In addition, we derive asymptotic distributions of the proposed estimators and construct confidence intervals. Simulation results with truncated Gaussian graphical models and the application to real data of wind direction reveal that the proposed methods effectively enable statistical inference with unnormalized models from missing data.
Mix and match networks: cross-modal alignment for zero-pair image-to-image translation
Authors: Yaxing Wang, Luis Herranz, Joost Van de Weijer
Date: 2019-03-08 00:00:00
Tasks: cross-modal alignment, Decoder, Image-to-Image Translation, Segmentation, Semantic Segmentation, Translation

This paper addresses the problem of inferring unseen cross-modal image-to-image translations between multiple modalities. We assume that only some of the pairwise translations have been seen (i.e. trained) and infer the remaining unseen translations (where training pairs are not available). We propose mix and match networks, an approach where multiple encoders and decoders are aligned in such a way that the desired translation can be obtained by simply cascading the source encoder and the target decoder, even when they have not interacted during the training stage (i.e. unseen). The main challenge lies in the alignment of the latent representations at the bottlenecks of encoder-decoder pairs. We propose an architecture with several tools to encourage alignment, including autoencoders and robust side information and latent consistency losses. We show the benefits of our approach in terms of effectiveness and scalability compared with other pairwise image-to-image translation approaches. We also propose zero-pair cross-modal image translation, a challenging setting where the objective is inferring semantic segmentation from depth (and vice-versa) without explicit segmentation-depth pairs, and only from two (disjoint) segmentation-RGB and depth-RGB training sets. We observe that a certain part of the shared information between unseen modalities might not be reachable, so we further propose a variant that leverages pseudo-pairs which allows us to exploit this shared information between the unseen modalities.