pwc-archive/papers-with-abstracts huggingface dataset:
Bayesian Mean-parameterized Nonnegative Binary Matrix Factorization
Authors: Alberto Lumbreras, Louis Filstroff, Cédric Févotte
Date: 2018-12-17 00:00:00
Tasks: Dictionary Learning, valid

Binary data matrices can represent many types of data such as social networks, votes, or gene expression. In some cases, the analysis of binary matrices can be tackled with nonnegative matrix factorization (NMF), where the observed data matrix is approximated by the product of two smaller nonnegative matrices. In this context, probabilistic NMF assumes a generative model where the data is usually Bernoulli-distributed. Often, a link function is used to map the factorization to the $[0,1]$ range, ensuring a valid Bernoulli mean parameter. However, link functions have the potential disadvantage to lead to uninterpretable models. Mean-parameterized NMF, on the contrary, overcomes this problem. We propose a unified framework for Bayesian mean-parameterized nonnegative binary matrix factorization models (NBMF). We analyze three models which correspond to three possible constraints that respect the mean-parametrization without the need for link functions. Furthermore, we derive a novel collapsed Gibbs sampler and a collapsed variational algorithm to infer the posterior distribution of the factors. Next, we extend the proposed models to a nonparametric setting where the number of used latent dimensions is automatically driven by the observed data. We analyze the performance of our NBMF methods in multiple datasets for different tasks such as dictionary learning and prediction of missing data. Experiments show that our methods provide similar or superior results than the state of the art, while automatically detecting the number of relevant components.
Fully Convolutional Speech Recognition
Authors: Neil Zeghidour, Qiantong Xu, Vitaliy Liptchinsky, Nicolas Usunier, Gabriel Synnaeve, Ronan Collobert
Date: 2018-12-17 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Current state-of-the-art speech recognition systems build on recurrent neural
networks for acoustic and/or language modeling, and rely on feature extraction
pipelines to extract mel-filterbanks or cepstral coefficients. In this paper we
present an alternative approach based solely on convolutional neural networks,
leveraging recent advances in acoustic models from the raw waveform and
language modeling. This fully convolutional approach is trained end-to-end to
predict characters from the raw waveform, removing the feature extraction step
altogether. An external convolutional language model is used to decode words.
On Wall Street Journal, our model matches the current state-of-the-art. On
Librispeech, we report state-of-the-art performance among end-to-end models,
including Deep Speech 2 trained with 12 times more acoustic data and
significantly more linguistic data.
Taking a Deeper Look at the Inverse Compositional Algorithm
Authors: Zhaoyang Lv, Frank Dellaert, James M. Rehg, Andreas Geiger
Date: 2018-12-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Motion Estimation, regression

In this paper, we provide a modern synthesis of the classic inverse
compositional algorithm for dense image alignment. We first discuss the
assumptions made by this well-established technique, and subsequently propose
to relax these assumptions by incorporating data-driven priors into this model.
More specifically, we unroll a robust version of the inverse compositional
algorithm and replace multiple components of this algorithm using more
expressive models whose parameters we train in an end-to-end fashion from data.
Our experiments on several challenging 3D rigid motion estimation tasks
demonstrate the advantages of combining optimization with learning-based
techniques, outperforming the classic inverse compositional algorithm as well
as data-driven image-to-pose regression approaches.
Winter Road Surface Condition Recognition Using A Pretrained Deep Convolutional Network
Authors: Guangyuan Pan, Liping Fu, Ruifan Yu, Matthew Muresan
Date: 2018-12-17 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

This paper investigates the application of the latest machine learning
technique deep neural networks for classifying road surface conditions (RSC)
based on images from smartphones. Traditional machine learning techniques such
as support vector machine (SVM) and random forests (RF) have been attempted in
literature; however, their classification performance has been less than
desirable due to challenges associated with image noises caused by sunlight
glare and residual salts. A deep learning model based on convolutional neural
network (CNN) is proposed and evaluated for its potential to address these
challenges for improved classification accuracy. In the proposed approach we
introduce the idea of applying an existing CNN model that has been pre-trained
using millions of images with proven high recognition accuracy. The model is
extended with two additional fully-connected layers of neurons for learning the
specific features of the RSC images. The whole model is then trained with a low
learning rate for fine-tuning by using a small set of RSC images. Results show
that the proposed model has the highest classification performance in
comparison to the traditional machine learning techniques. The testing accuracy
with different training dataset sizes is also analyzed, showing the potential
of achieving much higher accuracy with a larger training dataset.
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders
Authors: Ozan Ozdenizci, Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus
Date: 2018-12-17 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Motor Imagery, Representation Learning, Transfer Learning

We introduce adversarial neural networks for representation learning as a
novel approach to transfer learning in brain-computer interfaces (BCIs). The
proposed approach aims to learn subject-invariant representations by
simultaneously training a conditional variational autoencoder (cVAE) and an
adversarial network. We use shallow convolutional architectures to realize the
cVAE, and the learned encoder is transferred to extract subject-invariant
features from unseen BCI users' data for decoding. We demonstrate a
proof-of-concept of our approach based on analyses of electroencephalographic
(EEG) data recorded during a motor imagery BCI experiment.
Bayesian Optimization in AlphaGo
Authors: Yutian Chen, Aja Huang, Ziyu Wang, Ioannis Antonoglou, Julian Schrittwieser, David Silver, Nando de Freitas
Date: 2018-12-17 00:00:00
Tasks: Bayesian Optimisation, Bayesian Optimization

During the development of AlphaGo, its many hyper-parameters were tuned with
Bayesian optimization multiple times. This automatic tuning process resulted in
substantial improvements in playing strength. For example, prior to the match
with Lee Sedol, we tuned the latest AlphaGo agent and this improved its
win-rate from 50% to 66.5% in self-play games. This tuned version was deployed
in the final match. Of course, since we tuned AlphaGo many times during its
development cycle, the compounded contribution was even higher than this
percentage. It is our hope that this brief case study will be of interest to Go
fans, and also provide Bayesian optimization practitioners with some insights
and inspiration.
Convolutional herbal prescription building method from multi-scale facial features
Authors: Huiqiang Liao, Guihua Wen, Yang Hu, Changjun Wang
Date: 2018-12-17 00:00:00

In Traditional Chinese Medicine (TCM), facial features are important basis
for diagnosis and treatment. A doctor of TCM can prescribe according to a
patient's physical indicators such as face, tongue, voice, symptoms, pulse.
Previous works analyze and generate prescription according to symptoms.
However, research work to mine the association between facial features and
prescriptions has not been found for the time being. In this work, we try to
use deep learning methods to mine the relationship between the patient's face
and herbal prescriptions (TCM prescriptions), and propose to construct
convolutional neural networks that generate TCM prescriptions according to the
patient's face image. It is a novel and challenging job. In order to mine
features from different granularities of faces, we design a multi-scale
convolutional neural network based on three-grained face, which mines the
patient's face information from the organs, local regions, and the entire face.
Our experiments show that convolutional neural networks can learn relevant
information from face to prescribe, and the multi-scale convolutional neural
networks based on three-grained face perform better.
A Tutorial on Deep Latent Variable Models of Natural Language
Authors: Yoon Kim, Sam Wiseman, Alexander M. Rush
Date: 2018-12-17 00:00:00
Tasks: Deep Learning, Variational Inference

There has been much recent, exciting work on combining the complementary strengths of latent variable models and deep learning. Latent variable modeling makes it easy to explicitly specify model constraints through conditional independence properties, while deep learning makes it possible to parameterize these conditional likelihoods with powerful function approximators. While these "deep latent variable" models provide a rich, flexible framework for modeling many real-world phenomena, difficulties exist: deep parameterizations of conditional likelihoods usually make posterior inference intractable, and latent variable objectives often complicate backpropagation by introducing points of non-differentiability. This tutorial explores these issues in depth through the lens of variational inference.
Discriminant Patch Representation for RGB-D Face Recognition Using Convolutional Neural Networks
Authors: Nesrine Grati, Achraf Ben-Hamadou, Mohamed Hammami
Date: 2018-12-17 00:00:00
Tasks: Face Recognition

This paper focuses on designing data-driven models to learn a discriminant
representation space for face recognition using RGB-D data. Unlike hand-crafted
representations, learned models can extract and organize the discriminant
information from the data, and can automatically adapt to build new compute
vision applications faster. We proposed an effective way to train Convolutional
Neural Networks to learn face patch discriminant features. The proposed
solution was tested and validated on state-of-the-art RGB-D datasets and showed
competitive and promising results relatively to standard hand-crafted feature
extractors.
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations
Authors: Di Wang, Adam Smith, Jinhui Xu
Date: 2018-12-17 00:00:00

Minimizing a convex risk function is the main step in many basic learning algorithms. We study protocols for convex optimization which provably leak very little about the individual data points that constitute the loss function. Specifically, we consider differentially private algorithms that operate in the local model, where each data record is stored on a separate user device and randomization is performed locally by those devices. We give new protocols for \emph{noninteractive} LDP convex optimization---i.e., protocols that require only a single randomized report from each user to an untrusted aggregator. We study our algorithms' performance with respect to expected loss---either over the data set at hand (empirical risk) or a larger population from which our data set is assumed to be drawn. Our error bounds depend on the form of individuals' contribution to the expected loss. For the case of \emph{generalized linear losses} (such as hinge and logistic losses), we give an LDP algorithm whose sample complexity is only linear in the dimensionality $p$ and quasipolynomial in other terms (the privacy parameters $\epsilon$ and $\delta$, and the desired excess risk $\alpha$). This is the first algorithm for nonsmooth losses with sub-exponential dependence on $p$. For the Euclidean median problem, where the loss is given by the Euclidean distance to a given data point, we give a protocol whose sample complexity grows quasipolynomially in $p$. This is the first protocol with sub-exponential dependence on $p$ for a loss that is not a generalized linear loss . Our result for the hinge loss is based on a technique, dubbed polynomial of inner product approximation, which may be applicable to other problems. Our results for generalized linear losses and the Euclidean median are based on new reductions to the case of hinge loss.
Spartan Networks: Self-Feature-Squeezing Neural Networks for increased robustness in adversarial settings
Authors: François Menet, Paul Berthier, José M. Fernandez, Michel Gagnon
Date: 2018-12-17 00:00:00

Deep learning models are vulnerable to adversarial examples which are input
samples modified in order to maximize the error on the system. We introduce
Spartan Networks, resistant deep neural networks that do not require input
preprocessing nor adversarial training. These networks have an adversarial
layer designed to discard some information of the network, thus forcing the
system to focus on relevant input. This is done using a new activation function
to discard data. The added layer trains the neural network to filter-out
usually-irrelevant parts of its input. Our performance evaluation shows that
Spartan Networks have a slightly lower precision but report a higher robustness
under attack when compared to unprotected models. Results of this study of
Adversarial AI as a new attack vector are based on tests conducted on the MNIST
dataset.
Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events
Authors: Danilo Comminiello, Marco Lella, Simone Scardapane, Aurelio Uncini
Date: 2018-12-17 00:00:00
Tasks: Event Detection, Sound Event Detection

Learning from data in the quaternion domain enables us to exploit internal
dependencies of 4D signals and treating them as a single entity. One of the
models that perfectly suits with quaternion-valued data processing is
represented by 3D acoustic signals in their spherical harmonics decomposition.
In this paper, we address the problem of localizing and detecting sound events
in the spatial sound field by using quaternion-valued data processing. In
particular, we consider the spherical harmonic components of the signals
captured by a first-order ambisonic microphone and process them by using a
quaternion convolutional neural network. Experimental results show that the
proposed approach exploits the correlated nature of the ambisonic signals, thus
improving accuracy results in 3D sound event detection and localization.
Variational Autoencoders Pursue PCA Directions (by Accident)
Authors: Michal Rolinek, Dominik Zietlow, Georg Martius
Date: 2018-12-17 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decoder, Representation Learning

The Variational Autoencoder (VAE) is a powerful architecture capable of
representation learning and generative modeling. When it comes to learning
interpretable (disentangled) representations, VAE and its variants show
unparalleled performance. However, the reasons for this are unclear, since a
very particular alignment of the latent embedding is needed but the design of
the VAE does not encourage it in any explicit way. We address this matter and
offer the following explanation: the diagonal approximation in the encoder
together with the inherent stochasticity force local orthogonality of the
decoder. The local behavior of promoting both reconstruction and orthogonality
matches closely how the PCA embedding is chosen. Alongside providing an
intuitive understanding, we justify the statement with full theoretical
analysis as well as with experiments.
Fully-deformable 3D image registration in two seconds
Authors: Daniel Budelmann, Lars König, Nils Papenberg, Jan Lellmann
Date: 2018-12-17 00:00:00
Tasks: CPU, GPU, Image Registration, Vocal Bursts Valence Prediction

We present a highly parallel method for accurate and efficient variational
deformable 3D image registration on a consumer-grade graphics processing unit
(GPU). We build on recent matrix-free variational approaches and specialize the
concepts to the massively-parallel manycore architecture provided by the GPU.
Compared to a parallel and optimized CPU implementation, this allows us to
achieve an average speedup of 32.53 on 986 real-world CT thorax-abdomen
follow-up scans. At a resolution of approximately $256^3$ voxels, the average
runtime is 1.99 seconds for the full registration. On the publicly available
DIR-lab benchmark, our method ranks third with respect to average landmark
error at an average runtime of 0.32 seconds.
Trichotomic Argumentation Representation
Authors: Merlin Göttlinger, Lutz Schröder
Date: 2018-12-17 00:00:00

The Aristotelian trichotomy distinguishes three aspects of argumentation:
Logos, Ethos, and Pathos. Even rich argumentation representations like the
Argument Interchange Format (AIF) are only concerned with capturing the Logos
aspect. Inference Anchoring Theory (IAT) adds the possibility to represent
ethical requirements on the illocutionary force edges linking locutions to
illocutions, thereby allowing to capture some aspects of ethos. With the recent
extensions AIF+ and Social Argument Interchange Format (S-AIF), which embed
dialogue and speakers into the AIF argumentation representation, the basis for
representing all three aspects identified by Aristotle was formed. In the
present work, we develop the Trichotomic Argument Interchange Format (T-AIF),
building on the idea from S-AIF of adding the speakers to the argumentation
graph. We capture Logos in the usual known from AIF+, Ethos in form of weighted
edges between actors representing trust, and Pathos via weighted edges from
actors to illocutions representing their level of commitment to the
propositions. This extended structured argumentation representation opens up
new possibilities of defining semantic properties on this rich graph in order
to characterize and profile the reasoning patterns of the participating actors.
Traceability of Deep Neural Networks
Authors: Vincent Aravantinos, Frederik Diehl
Date: 2018-12-17 00:00:00
Tasks: Deep Learning

[Context.] The success of deep learning makes its usage more and more tempting in safety-critical applications. However such applications have historical standards (e.g., DO178, ISO26262) which typically do not envision the usage of machine learning. We focus in particular on \emph{requirements traceability} of software artifacts, i.e., code modules, functions, or statements (depending on the desired granularity). [Problem.] Both code and requirements are a problem when dealing with deep neural networks: code constituting the network is not comparable to classical code; furthermore, requirements for applications where neural networks are required are typically very hard to specify: even though high-level requirements can be defined, it is very hard to make such requirements concrete enough, that one can qualify them of low-level requirements. An additional problem is that deep learning is in practice very much based on trial-and-error, which makes the final result hard to explain without the previous iterations. [Proposed solution.] We investigate which artifacts could play a similar role to code or low-level requirements in neural network development and propose various traces which one could possibly consider as a replacement for classical notions. We also propose a form of traceability (and new artifacts) in order to deal with the particular trial-and-error development process for deep learning.
Heuristics for Efficient Sparse Blind Source Separation
Authors: Christophe Kervazo, Jerome Bobin, Cecile Chenot
Date: 2018-12-17 00:00:00
Tasks: blind source separation

Sparse Blind Source Separation (sparse BSS) is a key method to analyze
multichannel data in fields ranging from medical imaging to astrophysics.
However, since it relies on seeking the solution of a non-convex penalized
matrix factorization problem, its performances largely depend on the
optimization strategy. In this context, Proximal Alternating Linearized
Minimization (PALM) has become a standard algorithm which, despite its
theoretical grounding, generally provides poor practical separation results. In
this work, we propose a novel strategy that combines a heuristic approach with
PALM. We show its relevance on realistic astrophysical data.
TechKG: A Large-Scale Chinese Technology-Oriented Knowledge Graph
Authors: Feiliang Ren, Yining Hou, Yan Li, Linfeng Pan, Yi Zhang, Xiaobo Liang, Yongkang Liu, Yu Guo, Rongsheng Zhao, Ruicheng Ming, Huiming Wu
Date: 2018-12-17 00:00:00
Tasks: Knowledge Graphs

Knowledge graph is a kind of valuable knowledge base which would benefit lots
of AI-related applications. Up to now, lots of large-scale knowledge graphs
have been built. However, most of them are non-Chinese and designed for general
purpose. In this work, we introduce TechKG, a large scale Chinese knowledge
graph that is technology-oriented. It is built automatically from massive
technical papers that are published in Chinese academic journals of different
research domains. Some carefully designed heuristic rules are used to extract
high quality entities and relations. Totally, it comprises of over 260 million
triplets that are built upon more than 52 million entities which come from 38
research domains. Our preliminary ex-periments indicate that TechKG has high
adaptability and can be used as a dataset for many diverse AI-related
applications. We released TechKG at: http://www.techkg.cn.
Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation
Authors: Rakshith Shetty, Bernt Schiele, Mario Fritz
Date: 2018-12-17 00:00:00
Tasks: Classification, Data Augmentation, General Classification, image-classification, Image Classification, Scene Understanding, Segmentation, Semantic Segmentation

Importance of visual context in scene understanding tasks is well recognized
in the computer vision community. However, to what extent the computer vision
models for image classification and semantic segmentation are dependent on the
context to make their predictions is unclear. A model overly relying on context
will fail when encountering objects in context distributions different from
training data and hence it is important to identify these dependencies before
we can deploy the models in the real-world. We propose a method to quantify the
sensitivity of black-box vision models to visual context by editing images to
remove selected objects and measuring the response of the target models. We
apply this methodology on two tasks, image classification and semantic
segmentation, and discover undesirable dependency between objects and context,
for example that "sidewalk" segmentation relies heavily on "cars" being present
in the image. We propose an object removal based data augmentation solution to
mitigate this dependency and increase the robustness of classification and
segmentation models to contextual variations. Our experiments show that the
proposed data augmentation helps these models improve the performance in
out-of-context scenarios, while preserving the performance on regular data.