pwc-archive/papers-with-abstracts huggingface dataset:
Approximate Dynamic Programming with Neural Networks in Linear Discrete Action Spaces
Authors: Wouter van Heeswijk, Han La Poutré
Date: 2019-02-26 00:00:00

Real-world problems of operations research are typically high-dimensional and
combinatorial. Linear programs are generally used to formulate and efficiently
solve these large decision problems. However, in multi-period decision
problems, we must often compute expected downstream values corresponding to
current decisions. When applying stochastic methods to approximate these
values, linear programs become restrictive for designing value function
approximations (VFAs). In particular, the manual design of a polynomial VFA is
challenging.
  This paper presents an integrated approach for complex optimization problems,
focusing on applications in the domain of operations research. It develops a
hybrid solution method that combines linear programming and neural networks as
part of approximate dynamic programming.
  Our proposed solution method embeds neural network VFAs into linear decision
problems, combining the nonlinear expressive power of neural networks with the
efficiency of solving linear programs. As a proof of concept, we perform
numerical experiments on a transportation problem. The neural network VFAs
consistently outperform polynomial VFAs, with limited design and tuning effort.
Multi-Scale Quasi-RNN for Next Item Recommendation
Authors: Chaoyue He, Yong liu, Qingyu Guo, Chunyan Miao
Date: 2019-02-26 00:00:00
Tasks: Recommendation Systems

How to better utilize sequential information has been extensively studied in
the setting of recommender systems. To this end, architectural inductive biases
such as Markov-Chains, Recurrent models, Convolutional networks and many others
have demonstrated reasonable success on this task. This paper proposes a new
neural architecture, multi-scale Quasi-RNN for next item Recommendation
(QR-Rec) task. Our model provides the best of both worlds by exploiting
multi-scale convolutional features as the compositional gating functions of a
recurrent cell. The model is implemented in a multi-scale fashion, i.e.,
convolutional filters of various widths are implemented to capture different
union-level features of input sequences which influence the compositional
encoder. The key idea aims to capture the recurrent relations between different
kinds of local features, which has never been studied previously in the context
of recommendation. Through extensive experiments, we demonstrate that our model
achieves state-of-the-art performance on 15 well-established datasets,
outperforming strong competitors such as FPMC, Fossil and Caser absolutely by
0.57%-7.16% and relatively by 1.44%-17.65% in terms of MAP, Recall@10 and
NDCG@10.
Adaptive Gradient Methods with Dynamic Bound of Learning Rate
Authors: Liangchen Luo, Yuanhao Xiong, Yan Liu, Xu sun
Date: 2019-02-26 00:00:00
Proceeding: ICLR 2019 5

Adaptive optimization methods such as AdaGrad, RMSprop and Adam have been
proposed to achieve a rapid training process with an element-wise scaling term
on learning rates. Though prevailing, they are observed to generalize poorly
compared with SGD or even fail to converge due to unstable and extreme learning
rates. Recent work has put forward some algorithms such as AMSGrad to tackle
this issue but they failed to achieve considerable improvement over existing
methods. In our paper, we demonstrate that extreme learning rates can lead to
poor performance. We provide new variants of Adam and AMSGrad, called AdaBound
and AMSBound respectively, which employ dynamic bounds on learning rates to
achieve a gradual and smooth transition from adaptive methods to SGD and give a
theoretical proof of convergence. We further conduct experiments on various
popular tasks and models, which is often insufficient in previous work.
Experimental results show that new variants can eliminate the generalization
gap between adaptive methods and SGD and maintain higher learning speed early
in training at the same time. Moreover, they can bring significant improvement
over their prototypes, especially on complex deep networks. The implementation
of the algorithm can be found at https://github.com/Luolc/AdaBound .
Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks
Authors: Maximilian Pöpperl, Raghavendra Gulagundi, Senthil Yogamani, Stefan Milz
Date: 2019-02-26 00:00:00
Tasks: Data Augmentation

Recently, realistic data augmentation using neural networks especially
generative neural networks (GAN) has achieved outstanding results. The
communities main research focus is visual image processing. However, automotive
cars and robots are equipped with a large suite of sensors to achieve a high
redundancy. In addition to others, ultrasonic sensors are often used due to
their low-costs and reliable near field distance measuring capabilities. Hence,
Pattern recognition needs to be applied to ultrasonic signals as well. Machine
Learning requires extensive data sets and those measurements are
time-consuming, expensive and not flexible to hardware and environmental
changes. On the other hand, there exists no method to simulate those signals
deterministically. We present a novel approach for synthetic ultrasonic signal
simulation using conditional GANs (cGANs). For the best of our knowledge, we
present the first realistic data augmentation for automotive ultrasonics. The
performance of cGANs allows us to bring the realistic environment simulation to
a new level. By using setup and environmental parameters as condition, the
proposed approach is flexible to external influences. Due to the low complexity
and time effort for data generation, we outperform other simulation algorithms,
such as finite element method. We verify the outstanding accuracy and realism
of our method by applying a detailed statistical analysis and comparing the
generated data to an extensive amount of measured signals.
Information Gathering in Decentralized POMDPs by Policy Graph Improvement
Authors: Mikko Lauri, Joni Pajarinen, Jan Peters
Date: 2019-02-26 00:00:00
Tasks: Decision Making

Decentralized policies for information gathering are required when multiple
autonomous agents are deployed to collect data about a phenomenon of interest
without the ability to communicate. Decentralized partially observable Markov
decision processes (Dec-POMDPs) are a general, principled model well-suited for
such decentralized multiagent decision-making problems. In this paper, we
investigate Dec-POMDPs for decentralized information gathering problems. An
optimal solution of a Dec-POMDP maximizes the expected sum of rewards over
time. To encourage information gathering, we set the reward as a function of
the agents' state information, for example the negative Shannon entropy. We
prove that if the reward is convex, then the finite-horizon value function of
the corresponding Dec-POMDP is also convex. We propose the first heuristic
algorithm for information gathering Dec-POMDPs, and empirically prove its
effectiveness by solving problems an order of magnitude larger than previous
state-of-the-art.
Capsule Neural Network based Height Classification using Low-Cost Automotive Ultrasonic Sensors
Authors: Maximilian Pöpperl, Raghavendra Gulagundi, Senthil Yogamani, Stefan Milz
Date: 2019-02-26 00:00:00
Tasks: Autonomous Driving, Classification, General Classification

High performance ultrasonic sensor hardware is mainly used in medical
applications. Although, the development in automotive scenarios is towards
autonomous driving, the ultrasonic sensor hardware still stays low-cost and
low-performance, respectively. To overcome the strict hardware limitations, we
propose to use capsule neural networks. By the high classification capability
of this network architecture, we can achieve outstanding results for performing
a detailed height analysis of detected objects. We apply a novel resorting and
reshaping method to feed the neural network with ultrasonic data. This
increases classification performance and computation speed. We tested the
approach under different environmental conditions to verify that the proposed
method is working independent of external parameters that is needed for
autonomous driving.
Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies?
Authors: Céline Hocquette, Stephen H. Muggleton
Date: 2019-02-26 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

World-class human players have been outperformed in a number of complex two
person games (Go, Chess, Checkers) by Deep Reinforcement Learning systems.
However, owing to tractability considerations minimax regret of a learning
system cannot be evaluated in such games. In this paper we consider simple
games (Noughts-and-Crosses and Hexapawn) in which minimax regret can be
efficiently evaluated. We use these games to compare Cumulative Minimax Regret
for variants of both standard and deep reinforcement learning against two
variants of a new Meta-Interpretive Learning system called MIGO. In our
experiments all tested variants of both normal and deep reinforcement learning
have worse performance (higher cumulative minimax regret) than both variants of
MIGO on Noughts-and-Crosses and Hexapawn. Additionally, MIGO's learned rules
are relatively easy to comprehend, and are demonstrated to achieve significant
transfer learning in both directions between Noughts-and-Crosses and Hexapawn.
Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets
Authors: Homayun Afrabandpey, Tomi Peltola, Samuel Kaski
Date: 2019-02-26 00:00:00
Tasks: Decision Making, Sequential Decision Making, Small Data Image Classification

Learning predictive models from small high-dimensional data sets is a key
problem in high-dimensional statistics. Expert knowledge elicitation can help,
and a strong line of work focuses on directly eliciting informative prior
distributions for parameters. This either requires considerable statistical
expertise or is laborious, as the emphasis has been on accuracy and not on
efficiency of the process. Another line of work queries about importance of
features one at a time, assuming them to be independent and hence missing
covariance information. In contrast, we propose eliciting expert knowledge
about pairwise feature similarities, to borrow statistical strength in the
predictions, and using sequential decision making techniques to minimize the
effort of the expert. Empirical results demonstrate improvement in predictive
performance on both simulated and real data, in high-dimensional linear
regression tasks, where we learn the covariance structure with a Gaussian
process, based on sequential elicitation.
Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural Networks
Authors: Michele Tonutti, Emanuele Ruffaldi, Alessandro Cattaneo, Carlo Alberto Avizzano
Date: 2019-02-26 00:00:00
Tasks: Domain Adaptation, Time Series, Time Series Analysis, Transfer Learning

Through deep learning and computer vision techniques, driving manoeuvres can
be predicted accurately a few seconds in advance. Even though adapting a
learned model to new drivers and different vehicles is key for robust
driver-assistance systems, this problem has received little attention so far.
This work proposes to tackle this challenge through domain adaptation, a
technique closely related to transfer learning. A proof of concept for the
application of a Domain-Adversarial Recurrent Neural Network (DA-RNN) to
multi-modal time series driving data is presented, in which domain-invariant
features are learned by maximizing the loss of an auxiliary domain classifier.
Our implementation is evaluated using a leave-one-driver-out approach on
individual drivers from the Brain4Cars dataset, as well as using a new dataset
acquired through driving simulations, yielding an average increase in
performance of 30% and 114% respectively compared to no adaptation. We also
show the importance of fine-tuning sections of the network to optimise the
extraction of domain-independent features. The results demonstrate the
applicability of the approach to driver-assistance systems as well as training
and simulation environments.
Generative Visual Dialogue System via Adaptive Reasoning and Weighted Likelihood Estimation
Authors: Heming Zhang, Shalini Ghosh, Larry Heck, Stephen Walsh, Junting Zhang, Jie Zhang, C. -C. Jay Kuo
Date: 2019-02-26 00:00:00
Tasks: Visual Dialog

The key challenge of generative Visual Dialogue (VD) systems is to respond to human queries with informative answers in natural and contiguous conversation flow. Traditional Maximum Likelihood Estimation (MLE)-based methods only learn from positive responses but ignore the negative responses, and consequently tend to yield safe or generic responses. To address this issue, we propose a novel training scheme in conjunction with weighted likelihood estimation (WLE) method. Furthermore, an adaptive multi-modal reasoning module is designed, to accommodate various dialogue scenarios automatically and select relevant information accordingly. The experimental results on the VisDial benchmark demonstrate the superiority of our proposed algorithm over other state-of-the-art approaches, with an improvement of 5.81% on recall@10.
GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks
Authors: Ziyao Li, Liang Zhang, Guojie Song
Date: 2019-02-26 00:00:00
Tasks: Informativeness

Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes. Low-rank proximities and node features are successfully leveraged in existing GCNs, however, attributes that graph links may carry are commonly ignored, as almost all of these models simplify graph links into binary or scalar values describing node connectedness. In our paper instead, links are reverted to hypostatic relationships between entities with descriptional attributes. We propose GCN-LASE (GCN with Link Attributes and Sampling Estimation), a novel GCN model taking both node and link attributes as inputs. To adequately captures the interactions between link and node attributes, their tensor product is used as neighbor features, based on which we define several graph kernels and further develop according architectures for LASE. Besides, to accelerate the training process, the sum of features in entire neighborhoods are estimated through Monte Carlo method, with novel sampling strategies designed for LASE to minimize the estimation variance. Our experiments show that LASE outperforms strong baselines over various graph datasets, and further experiments corroborate the informativeness of link attributes and our model's ability of adequately leveraging them.
LaSO: Label-Set Operations networks for multi-label few-shot learning
Authors: Amit Alfassy, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, Rogerio Feris, Raja Giryes, Alex M. Bronstein
Date: 2019-02-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Data Augmentation, Few-Shot Learning, General Classification, Retrieval

Example synthesis is one of the leading methods to tackle the problem of
few-shot learning, where only a small number of samples per class are
available. However, current synthesis approaches only address the scenario of a
single category label per image. In this work, we propose a novel technique for
synthesizing samples with multiple labels for the (yet unhandled) multi-label
few-shot classification scenario. We propose to combine pairs of given examples
in feature space, so that the resulting synthesized feature vectors will
correspond to examples whose label sets are obtained through certain set
operations on the label sets of the corresponding input pairs. Thus, our method
is capable of producing a sample containing the intersection, union or
set-difference of labels present in two input samples. As we show, these set
operations generalize to labels unseen during training. This enables performing
augmentation on examples of novel categories, thus, facilitating multi-label
few-shot classifier learning. We conduct numerous experiments showing promising
results for the label-set manipulation capabilities of the proposed approach,
both directly (using the classification and retrieval metrics), and in the
context of performing data augmentation for multi-label few-shot learning. We
propose a benchmark for this new and challenging task and show that our method
compares favorably to all the common baselines.
Recurrent Convolution for Compact and Cost-Adjustable Neural Networks: An Empirical Study
Authors: Zhendong Zhang, Cheolkon Jung
Date: 2019-02-26 00:00:00
Tasks: image-classification, Image Classification, Model Compression, Rolling Shutter Correction

Recurrent convolution (RC) shares the same convolutional kernels and unrolls
them multiple steps, which is originally proposed to model time-space signals.
We argue that RC can be viewed as a model compression strategy for deep
convolutional neural networks. RC reduces the redundancy across layers.
However, the performance of an RC network is not satisfactory if we directly
unroll the same kernels multiple steps. We propose a simple yet effective
variant which improves the RC networks: the batch normalization layers of an RC
module are learned independently (not shared) for different unrolling steps.
Moreover, we verify that RC can perform cost-adjustable inference which is
achieved by varying its unrolling steps. We learn double independent BN layers
for cost-adjustable RC networks, i.e. independent w.r.t both the unrolling
steps of current cell and upstream cell. We provide insights on why the
proposed method works successfully. Experiments on both image classification
and image denoise demonstrate the effectiveness of our method.
Logarithmic Regret for parameter-free Online Logistic Regression
Authors: Joseph De Vilmarest, Olivier Wintenberger
Date: 2019-02-26 00:00:00
Tasks: regression

We consider online optimization procedures in the context of logistic
regression, focusing on the Extended Kalman Filter (EKF). We introduce a
second-order algorithm close to the EKF, named Semi-Online Step (SOS), for
which we prove a O(log(n)) regret in the adversarial setting, paving the way to
similar results for the EKF. This regret bound on SOS is the first for such
parameter-free algorithm in the adversarial logistic regression. We prove for
the EKF in constant dynamics a O(log(n)) regret in expectation and in the
well-specified logistic regression model.
Semantic Hilbert Space for Text Representation Learning
Authors: Benyou Wang, Qiuchi Li, Massimo Melucci, Dawei Song
Date: 2019-02-26 00:00:00
Tasks: Benchmarking, General Classification, Representation Learning, Semantic Composition, Sentence, text-classification, Text Classification

Capturing the meaning of sentences has long been a challenging task. Current
models tend to apply linear combinations of word features to conduct semantic
composition for bigger-granularity units e.g. phrases, sentences, and
documents. However, the semantic linearity does not always hold in human
language. For instance, the meaning of the phrase `ivory tower' can not be
deduced by linearly combining the meanings of `ivory' and `tower'. To address
this issue, we propose a new framework that models different levels of semantic
units (e.g. sememe, word, sentence, and semantic abstraction) on a single
\textit{Semantic Hilbert Space}, which naturally admits a non-linear semantic
composition by means of a complex-valued vector word representation. An
end-to-end neural network~\footnote{https://github.com/wabyking/qnn} is
proposed to implement the framework in the text classification task, and
evaluation results on six benchmarking text classification datasets demonstrate
the effectiveness, robustness and self-explanation power of the proposed model.
Furthermore, intuitive case studies are conducted to help end users to
understand how the framework works.
Bi-stream Pose Guided Region Ensemble Network for Fingertip Localization from Stereo Images
Authors: Guijin Wang, Cairong Zhang, Xinghao Chen, Xiangyang Ji, Jing-Hao Xue, Hang Wang
Date: 2019-02-26 00:00:00
Tasks: 3D Hand Pose Estimation, Hand Pose Estimation, Missing Values, Pose Estimation

In human-computer interaction, it is important to accurately estimate the
hand pose especially fingertips. However, traditional approaches for fingertip
localization mainly rely on depth images and thus suffer considerably from the
noise and missing values. Instead of depth images, stereo images can also
provide 3D information of hands and promote 3D hand pose estimation. There are
nevertheless limitations on the dataset size, global viewpoints, hand
articulations and hand shapes in the publicly available stereo-based hand pose
datasets. To mitigate these limitations and promote further research on hand
pose estimation from stereo images, we propose a new large-scale binocular hand
pose dataset called THU-Bi-Hand, offering a new perspective for fingertip
localization. In the THU-Bi-Hand dataset, there are 447k pairs of stereo images
of different hand shapes from 10 subjects with accurate 3D location annotations
of the wrist and five fingertips. Captured with minimal restriction on the
range of hand motion, the dataset covers large global viewpoint space and hand
articulation space. To better present the performance of fingertip localization
on THU-Bi-Hand, we propose a novel scheme termed Bi-stream Pose Guided Region
Ensemble Network (Bi-Pose-REN). It extracts more representative feature regions
around joint points in the feature maps under the guidance of the previously
estimated pose. The feature regions are integrated hierarchically according to
the topology of hand joints to regress the refined hand pose. Bi-Pose-REN and
several existing methods are evaluated on THU-Bi-Hand so that benchmarks are
provided for further research. Experimental results show that our new method
has achieved the best performance on THU-Bi-Hand.
The importance of space and time in neuromorphic cognitive agents
Authors: Giacomo Indiveri, Yulia Sandamirskaya
Date: 2019-02-26 00:00:00
Tasks: Autonomous Driving

Artificial neural networks and computational neuroscience models have made
tremendous progress, allowing computers to achieve impressive results in
artificial intelligence (AI) applications, such as image recognition, natural
language processing, or autonomous driving. Despite this remarkable progress,
biological neural systems consume orders of magnitude less energy than today's
artificial neural networks and are much more agile and adaptive. This
efficiency and adaptivity gap is partially explained by the computing substrate
of biological neural processing systems that is fundamentally different from
the way today's computers are built. Biological systems use in-memory computing
elements operating in a massively parallel way rather than time-multiplexed
computing units that are reused in a sequential fashion. Moreover, activity of
biological neurons follows continuous-time dynamics in real, physical time,
instead of operating on discrete temporal cycles abstracted away from
real-time. Here, we present neuromorphic processing devices that emulate the
biological style of processing by using parallel instances of mixed-signal
analog/digital circuits that operate in real time. We argue that this approach
brings significant advantages in efficiency of computation. We show examples of
embodied neuromorphic agents that use such devices to interact with the
environment and exhibit autonomous learning.