pwc-archive/papers-with-abstracts huggingface dataset:
On the Possibilities and Limitations of Multi-hop Reasoning Under Linguistic Imperfections
Authors: Daniel Khashabi, Erfan Sadeqi Azer, Tushar Khot, Ashish Sabharwal, Dan Roth
Date: 2019-01-08 00:00:00

Systems for language understanding have become remarkably strong at overcoming linguistic imperfections in tasks involving phrase matching or simple reasoning. Yet, their accuracy drops dramatically as the number of reasoning steps increases. We present the first formal framework to study such empirical observations. It allows one to quantify the amount and effect of ambiguity, redundancy, incompleteness, and inaccuracy that the use of language introduces when representing a hidden conceptual space. The idea is to consider two interrelated spaces: a conceptual meaning space that is unambiguous and complete but hidden, and a linguistic space that captures a noisy grounding of the meaning space in the words of a language---the level at which all systems, whether neural or symbolic, operate. Applying this framework to a special class of multi-hop reasoning, namely the connectivity problem in graphs of relationships between concepts, we derive rigorous intuitions and impossibility results even under this simplified setting. For instance, if a query requires a moderately large (logarithmic) number of hops in the meaning graph, no reasoning system operating over a noisy graph grounded in language is likely to correctly answer it. This highlights a fundamental barrier that extends to a broader class of reasoning problems and systems, and suggests an alternative path forward: focusing on aligning the two spaces via richer representations, before investing in reasoning with many hops.
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification
Authors: Stephanie Ger, Yegna Subramanian Jambunath, Diego Klabjan
Date: 2019-01-08 00:00:00
Tasks: Anomaly Detection, Classification, Decoder, General Classification, Outlier Detection

Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic data. We introduce a novel GAN with Autoencoder (GAN-AE) architecture to generate synthetic samples for variable length, multi-feature sequence datasets. In this model, we develop a GAN architecture with an additional autoencoder component, where recurrent neural networks (RNNs) are used for each component of the model in order to generate synthetic data to improve classification accuracy for a highly imbalanced medical device dataset. In addition to the medical device dataset, we also evaluate the GAN-AE performance on two additional datasets and demonstrate the application of GAN-AE to a sequence-to-sequence task where both synthetic sequence inputs and sequence outputs must be generated. To evaluate the quality of the synthetic data, we train encoder-decoder models both with and without the synthetic data and compare the classification model performance. We show that a model trained with GAN-AE generated synthetic data outperforms models trained with synthetic data generated both with standard oversampling techniques such as SMOTE and Autoencoders as well as with state of the art GAN-based models.
Combining nonparametric spatial context priors with nonparametric shape priors for dendritic spine segmentation in 2-photon microscopy images
Authors: Ertunc Erdil, Ali Ozgur Argunsah, Tolga Tasdizen, Devrim Unay, Mujdat Cetin
Date: 2019-01-08 00:00:00
Tasks: Segmentation

Data driven segmentation is an important initial step of shape prior-based
segmentation methods since it is assumed that the data term brings a curve to a
plausible level so that shape and data terms can then work together to produce
better segmentations. When purely data driven segmentation produces poor
results, the final segmentation is generally affected adversely. One challenge
faced by many existing data terms is due to the fact that they consider only
pixel intensities to decide whether to assign a pixel to the foreground or to
the background region. When the distributions of the foreground and background
pixel intensities have significant overlap, such data terms become ineffective,
as they produce uncertain results for many pixels in a test image. In such
cases, using prior information about the spatial context of the object to be
segmented together with the data term can bring a curve to a plausible stage,
which would then serve as a good initial point to launch shape-based
segmentation. In this paper, we propose a new segmentation approach that
combines nonparametric context priors with a learned-intensity-based data term
and nonparametric shape priors. We perform experiments for dendritic spine
segmentation in both 2D and 3D 2-photon microscopy images. The experimental
results demonstrate that using spatial context priors leads to significant
improvements.
Multi-stream CNN based Video Semantic Segmentation for Automated Driving
Authors: Ganesh Sistu, Sumanth Chennupati, Senthil Yogamani
Date: 2019-01-08 00:00:00
Tasks: Decoder, Semantic Segmentation, Video Semantic Segmentation

Majority of semantic segmentation algorithms operate on a single frame even
in the case of videos. In this work, the goal is to exploit temporal
information within the algorithm model for leveraging motion cues and temporal
consistency. We propose two simple high-level architectures based on Recurrent
FCN (RFCN) and Multi-Stream FCN (MSFCN) networks. In case of RFCN, a recurrent
network namely LSTM is inserted between the encoder and decoder. MSFCN combines
the encoders of different frames into a fused encoder via 1x1 channel-wise
convolution. We use a ResNet50 network as the baseline encoder and construct
three networks namely MSFCN of order 2 & 3 and RFCN of order 2. MSFCN-3
produces the best results with an accuracy improvement of 9% and 15% for
Highway and New York-like city scenarios in the SYNTHIA-CVPR'16 dataset using
mean IoU metric. MSFCN-3 also produced 11% and 6% for SegTrack V2 and DAVIS
datasets over the baseline FCN network. We also designed an efficient version
of MSFCN-2 and RFCN-2 using weight sharing among the two encoders. The
efficient MSFCN-2 provided an improvement of 11% and 5% for KITTI and SYNTHIA
with negligible increase in computational complexity compared to the baseline
version.
An Application of Manifold Learning in Global Shape Descriptors
Authors: Fereshteh S. Bashiri, Reihaneh Rostami, Peggy Peissig, Roshan M. D'souza, Zeyun Yu
Date: 2019-01-08 00:00:00
Tasks: Retrieval

With the rapid expansion of applied 3D computational vision, shape
descriptors have become increasingly important for a wide variety of
applications and objects from molecules to planets. Appropriate shape
descriptors are critical for accurate (and efficient) shape retrieval and 3D
model classification. Several spectral-based shape descriptors have been
introduced by solving various physical equations over a 3D surface model. In
this paper, for the first time, we incorporate a specific group of techniques
in statistics and machine learning, known as manifold learning, to develop a
global shape descriptor in the computer graphics domain. The proposed
descriptor utilizes the Laplacian Eigenmap technique in which the Laplacian
eigenvalue problem is discretized using an exponential weighting scheme. As a
result, our descriptor eliminates the limitations tied to the existing spectral
descriptors, namely dependency on triangular mesh representation and high
intra-class quality of 3D models. We also present a straightforward
normalization method to obtain a scale-invariant descriptor. The extensive
experiments performed in this study show that the present contribution provides
a highly discriminative and robust shape descriptor under the presence of a
high level of noise, random scale variations, and low sampling rate, in
addition to the known isometric-invariance property of the Laplace-Beltrami
operator. The proposed method significantly outperforms state-of-the-art
algorithms on several non-rigid shape retrieval benchmarks.
Grey matter sublayer thickness estimation in themouse cerebellum
Authors: Da Ma, Manuel J. Cardoso, Maria A. Zuluaga, Marc Modat, Nick. Powell, Frances Wiseman, Victor Tybulewicz, Elizabeth Fisher, Mark. F. Lythgoe, Sebastien Ourselin
Date: 2019-01-08 00:00:00

The cerebellar grey matter morphology is an important feature to study
neurodegenerative diseases such as Alzheimer's disease or Down's syndrome. Its
volume or thickness is commonly used as a surrogate imaging biomarker for such
diseases. Most studies about grey matter thickness estimation focused on the
cortex, and little attention has been drawn on the morphology of the
cerebellum. Using ex vivo high-resolution MRI, it is now possible to visualise
the different cell layers in the mouse cerebellum. In this work, we introduce a
framework to extract the Purkinje layer within the grey matter, enabling the
estimation of the thickness of the cerebellar grey matter, the granular layer
and molecular layer from gadolinium-enhanced ex vivo mouse brain MRI.
Application to mouse model of Down's syndrome found reduced cortical and layer
thicknesses in the transchromosomic group.
Presence-absence estimation in audio recordings of tropical frog communities
Authors: Andrés Estrella Terneux, Damián Nicolalde, Daniel Nicolalde, Andrés Merino-Viteri
Date: 2019-01-08 00:00:00
Tasks: General Classification

One non-invasive way to study frog communities is by analyzing long-term
samples of acoustic material containing calls. This immense task has been
optimized by the development of Machine Learning tools to extract ecological
information. We explored a likelihood-ratio audio detector based on Gaussian
mixture model classification of 10 frog species, and applied it to estimate
presence-absence in audio recordings from an actual amphibian monitoring
performed at Yasun\'i National Park in the Ecuadorian Amazonia. A modified
filter-bank was used to extract 20 cepstral features that model the spectral
content of frog calls. Experiments were carried out to investigate the
hyperparameters and the minimum frog-call time needed to train an accurate GMM
classifier. With 64 Gaussians and 12 seconds of training time, the classifier
achieved an average weighted error rate of 0.9% on the 10-fold cross-validation
for nine species classification, as compared to 3% with MFCC and 1.8% with PLP
features. For testing, 10 GMMs were trained using all the available
training-validation dataset to study 23.5 hours in 141, 10-minute long samples
of unidentified real-world audio recorded at two frog communities in 2001 with
analog equipment. To evaluate automatic presence-absence estimation, we
characterized the audio samples with 10 binary variables each corresponding to
a frog species, and manually labeled a sub-set of 18 samples using headphones.
A recall of 87.5% and precision of 100% with average accuracy of 96.66%
suggests good generalization ability of the algorithm, and provides evidence of
the validity of this approach to study real-world audio recorded in a tropical
acoustic environment. Finally, we applied the algorithm to the available
corpus, and show its potentiality to gain insights into the temporal
reproductive behavior of frogs.
Choosing the Right Word: Using Bidirectional LSTM Tagger for Writing Support Systems
Authors: Victor Makarenkov, Lior Rokach, Bracha Shapira
Date: 2019-01-08 00:00:00
Tasks: Grammatical Error Correction, Machine Translation, Translation

Scientific writing is difficult. It is even harder for those for whom English
is a second language (ESL learners). Scholars around the world spend a
significant amount of time and resources proofreading their work before
submitting it for review or publication.
  In this paper we present a novel machine learning based application for
proper word choice task. Proper word choice is a generalization the lexical
substitution (LS) and grammatical error correction (GEC) tasks. We demonstrate
and evaluate the usefulness of applying bidirectional Long Short Term Memory
(LSTM) tagger, for this task. While state-of-the-art grammatical error
correction uses error-specific classifiers and machine translation methods, we
demonstrate an unsupervised method that is based solely on a high quality text
corpus and does not require manually annotated data. We use a bidirectional
Recurrent Neural Network (RNN) with LSTM for learning the proper word choice
based on a word's sentential context. We demonstrate and evaluate our
application on both a domain-specific (scientific), writing task and a
general-purpose writing task. We show that our domain-specific and
general-purpose models outperform state-of-the-art general context learning. As
an additional contribution of this research, we also share our code,
pre-trained models, and a new ESL learner test set with the research community.
On Relativistic $f$-Divergences
Authors: Alexia Jolicoeur-Martineau
Date: 2019-01-08 00:00:00

This paper provides a more rigorous look at Relativistic Generative
Adversarial Networks (RGANs). We prove that the objective function of the
discriminator is a statistical divergence for any concave function $f$ with
minimal properties ($f(0)=0$, $f'(0) \neq 0$, $\sup_x f(x)>0$). We also devise
a few variants of relativistic $f$-divergences. Wasserstein GAN was originally
justified by the idea that the Wasserstein distance (WD) is most sensible
because it is weak (i.e., it induces a weak topology). We show that the WD is
weaker than $f$-divergences which are weaker than relativistic $f$-divergences.
Given the good performance of RGANs, this suggests that WGAN does not performs
well primarily because of the weak metric, but rather because of regularization
and the use of a relativistic discriminator. We also take a closer look at
estimators of relativistic $f$-divergences. We introduce the minimum-variance
unbiased estimator (MVUE) for Relativistic paired GANs (RpGANs; originally
called RGANs which could bring confusion) and show that it does not perform
better. Furthermore, we show that the estimator of Relativistic average GANs
(RaGANs) is only asymptotically unbiased, but that the finite-sample bias is
small. Removing this bias does not improve performance.
Bilinear Bandits with Low-rank Structure
Authors: Kwang-Sung Jun, Rebecca Willett, Stephen Wright, Robert Nowak
Date: 2019-01-08 00:00:00

We introduce the bilinear bandit problem with low-rank structure in which an action takes the form of a pair of arms from two different entity types, and the reward is a bilinear function of the known feature vectors of the arms. The unknown in the problem is a $d_1$ by $d_2$ matrix $\mathbf{\Theta}^*$ that defines the reward, and has low rank $r \ll \min\{d_1,d_2\}$. Determination of $\mathbf{\Theta}^*$ with this low-rank structure poses a significant challenge in finding the right exploration-exploitation tradeoff. In this work, we propose a new two-stage algorithm called "Explore-Subspace-Then-Refine" (ESTR). The first stage is an explicit subspace exploration, while the second stage is a linear bandit algorithm called "almost-low-dimensional OFUL" (LowOFUL) that exploits and further refines the estimated subspace via a regularization technique. We show that the regret of ESTR is $\widetilde{\mathcal{O}}((d_1+d_2)^{3/2} \sqrt{r T})$ where $\widetilde{\mathcal{O}}$ hides logarithmic factors and $T$ is the time horizon, which improves upon the regret of $\widetilde{\mathcal{O}}(d_1d_2\sqrt{T})$ attained for a na\"ive linear bandit reduction. We conjecture that the regret bound of ESTR is unimprovable up to polylogarithmic factors, and our preliminary experiment shows that ESTR outperforms a na\"ive linear bandit reduction.
Neural Inverse Rendering of an Indoor Scene from a Single Image
Authors: Soumyadip Sengupta, Jinwei Gu, Kihwan Kim, Guilin Liu, David W. Jacobs, Jan Kautz
Date: 2019-01-08 00:00:00
Proceeding: ICCV 2019 10
Tasks: Inverse Rendering, Self-Supervised Learning

Inverse rendering aims to estimate physical attributes of a scene, e.g., reflectance, geometry, and lighting, from image(s). Inverse rendering has been studied primarily for single objects or with methods that solve for only one of the scene attributes. We propose the first learning-based approach that jointly estimates albedo, normals, and lighting of an indoor scene from a single image. Our key contribution is the Residual Appearance Renderer (RAR), which can be trained to synthesize complex appearance effects (e.g., inter-reflection, cast shadows, near-field illumination, and realistic shading), which would be neglected otherwise. This enables us to perform self-supervised learning on real data using a reconstruction loss, based on re-synthesizing the input image from the estimated components. We finetune with real data after pretraining with synthetic data. To this end, we use physically-based rendering to create a large-scale synthetic dataset, which is a significant improvement over prior datasets. Experimental results show that our approach outperforms state-of-the-art methods that estimate one or more scene attributes.
Face Recognition System
Authors: Yang Li, Sangwhan Cha
Date: 2019-01-08 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Face Recognition

Deep learning is one of the new and important branches in machine learning.
Deep learning refers to a set of algorithms that solve various problems such as
images and texts by using various machine learning algorithms in multi-layer
neural networks. Deep learning can be classified as a neural network from the
general category, but there are many changes in the concrete realization. At
the core of deep learning is feature learning, which is designed to obtain
hierarchical information through hierarchical networks, so as to solve the
important problems that previously required artificial design features. Deep
Learning is a framework that contains several important algorithms. For
different applications (images, voice, text), you need to use different network
models to achieve better results. With the development of deep learning and the
introduction of deep convolutional neural networks, the accuracy and speed of
face recognition have made great strides. However, as we said above, the
results from different networks and models are very different. In this paper,
facial features are extracted by merging and comparing multiple models, and
then a deep neural network is constructed to train and construct the combined
features. In this way, the advantages of multiple models can be combined to
mention the recognition accuracy. After getting a model with high accuracy, we
build a product model. This article compares the pure-client model with the
server-client model, analyzes the pros and cons of the two models, and analyzes
the various commercial products that are required for the server-client model.
Selective metamorphosis for growth modelling with applications to landmarks
Authors: Andreas Bock, Alexis Arnaudon, Colin Cotter
Date: 2019-01-08 00:00:00
Tasks: Anatomy

We present a framework for shape matching in computational anatomy allowing users control of the degree to which the matching is diffeomorphic. This control is given as a function defined over the image and parameterises the template deformation. By modelling localised template deformation we have a mathematical description of growth only in specified parts of an image. The location can either be specified from prior knowledge of the growth location or learned from data. For simplicity, we consider landmark matching and infer the distribution of a finite dimensional parameterisation of the control via Markov chain Monte Carlo. Preliminary numerical results are shown and future paths of investigation are laid out. Well-posedness of this new problem is studied together with an analysis of the associated geodesic equations.
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
Authors: Kumar Shridhar, Felix Laumann, Marcus Liwicki
Date: 2019-01-08 00:00:00
Tasks: Bayesian Inference, General Classification, Generative Adversarial Network, image-classification, Image Classification, Image Super-Resolution, Super-Resolution, Variational Inference

Artificial Neural Networks are connectionist systems that perform a given
task by learning on examples without having prior knowledge about the task.
This is done by finding an optimal point estimate for the weights in every
node. Generally, the network using point estimates as weights perform well with
large datasets, but they fail to express uncertainty in regions with little or
no data, leading to overconfident decisions.
  In this paper, Bayesian Convolutional Neural Network (BayesCNN) using
Variational Inference is proposed, that introduces probability distribution
over the weights. Furthermore, the proposed BayesCNN architecture is applied to
tasks like Image Classification, Image Super-Resolution and Generative
Adversarial Networks. The results are compared to point-estimates based
architectures on MNIST, CIFAR-10 and CIFAR-100 datasets for Image
CLassification task, on BSD300 dataset for Image Super Resolution task and on
CIFAR10 dataset again for Generative Adversarial Network task.
  BayesCNN is based on Bayes by Backprop which derives a variational
approximation to the true posterior. We, therefore, introduce the idea of
applying two convolutional operations, one for the mean and one for the
variance. Our proposed method not only achieves performances equivalent to
frequentist inference in identical architectures but also incorporate a
measurement for uncertainties and regularisation. It further eliminates the use
of dropout in the model. Moreover, we predict how certain the model prediction
is based on the epistemic and aleatoric uncertainties and empirically show how
the uncertainty can decrease, allowing the decisions made by the network to
become more deterministic as the training accuracy increases. Finally, we
propose ways to prune the Bayesian architecture and to make it more
computational and time effective.
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
Authors: Mikael Henaff, Alfredo Canziani, Yann Lecun
Date: 2019-01-08 00:00:00
Proceeding: ICLR 2019 5
Tasks: Rolling Shutter Correction

Learning a policy using only observational data is challenging because the
distribution of states it induces at execution time may differ from the
distribution observed during training. We propose to train a policy by
unrolling a learned model of the environment dynamics over multiple time steps
while explicitly penalizing two costs: the original cost the policy seeks to
optimize, and an uncertainty cost which represents its divergence from the
states it is trained on. We measure this second cost by using the uncertainty
of the dynamics model about its own predictions, using recent ideas from
uncertainty estimation for deep networks. We evaluate our approach using a
large-scale observational dataset of driving behavior recorded from traffic
cameras, and show that we are able to learn effective driving policies from
purely observational data, with no environment interaction.