pwc-archive/papers-with-abstracts huggingface dataset:
Reducing variability in along-tract analysis with diffusion profile realignment
Authors: Samuel St-Jean, Maxime Chamberland, Max A. Viergever, Alexander Leemans
Date: 2019-02-04 00:00:00
Proceeding: arXiv 2019 2
Tasks: Anatomy, Specificity

Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction
of the brain's white matter structures through tractography. Analyzing dMRI
measures along the trajectory of white matter bundles can provide a more
specific investigation than considering a region of interest or tract-averaged
measurements. However, performing group analyses with this along-tract strategy
requires correspondence between points of tract pathways across subjects. This
is usually achieved by creating a new common space where the representative
streamlines from every subject are resampled to the same number of points. If
the underlying anatomy of some subjects was altered due to, e.g. disease or
developmental changes, such information might be lost by resampling to a fixed
number of points. In this work, we propose to address the issue of possible
misalignment, which might be present even after resampling, by realigning the
representative streamline of each subject in this 1D space with a new method,
coined diffusion profile realignment (DPR). Experiments on synthetic datasets
show that DPR reduces the coefficient of variation for the mean diffusivity,
fractional anisotropy and apparent fiber density when compared to the unaligned
case. Using 100 in vivo datasets from the HCP, we simulated changes in mean
diffusivity, fractional anisotropy and apparent fiber density. Pairwise
Student's t-tests between these altered subjects and the original subjects
indicate that regional changes are identified after realignment with the DPR
algorithm, while preserving differences previously detected in the unaligned
case. This new correction strategy contributes to revealing effects of interest
which might be hidden by misalignment and has the potential to improve the
specificity in longitudinal population studies beyond the traditional region of
interest based analysis and along-tract analysis workflows.
KLUCB Approach to Copeland Bandits
Authors: Nischal Agrawal, Prasanna Chaporkar
Date: 2019-02-07 00:00:00
Tasks: Information Retrieval, Reinforcement Learning, Retrieval, Thompson Sampling

Multi-armed bandit(MAB) problem is a reinforcement learning framework where
an agent tries to maximise her profit by proper selection of actions through
absolute feedback for each action. The dueling bandits problem is a variation
of MAB problem in which an agent chooses a pair of actions and receives
relative feedback for the chosen action pair. The dueling bandits problem is
well suited for modelling a setting in which it is not possible to provide
quantitative feedback for each action, but qualitative feedback for each action
is preferred as in the case of human feedback. The dueling bandits have been
successfully applied in applications such as online rank elicitation,
information retrieval, search engine improvement and clinical online
recommendation. We propose a new method called Sup-KLUCB for K-armed dueling
bandit problem specifically Copeland bandit problem by converting it into a
standard MAB problem. Instead of using MAB algorithm independently for each
action in a pair as in Sparring and in Self-Sparring algorithms, we combine a
pair of action and use it as one action. Previous UCB algorithms such as
Relative Upper Confidence Bound(RUCB) can be applied only in case of Condorcet
dueling bandits, whereas this algorithm applies to general Copeland dueling
bandits, including Condorcet dueling bandits as a special case. Our empirical
results outperform state of the art Double Thompson Sampling(DTS) in case of
Copeland dueling bandits.
FDDB-360: Face Detection in 360-degree Fisheye Images
Authors: Jianglin Fu, Saeed Ranjbar Alvar, Ivan V. Bajic, Rodney G. Vaughan
Date: 2019-02-07 00:00:00
Tasks: Face Detection

360-degree cameras offer the possibility to cover a large area, for example
an entire room, without using multiple distributed vision sensors. However,
geometric distortions introduced by their lenses make computer vision problems
more challenging. In this paper we address face detection in 360-degree fisheye
images. We show how a face detector trained on regular images can be re-trained
for this purpose, and we also provide a 360-degree fisheye-like version of the
popular FDDB face detection dataset, which we call FDDB-360.
Hybrid Models with Deep and Invertible Features
Authors: Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, Balaji Lakshminarayanan
Date: 2019-02-07 00:00:00
Tasks: Probabilistic Deep Learning

We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i.e. a normalizing flow). An attractive property of our model is that both p(features), the density of the features, and p(targets | features), the predictive distribution, can be computed exactly in a single feed-forward pass. We show that our hybrid model, despite the invertibility constraints, achieves similar accuracy to purely predictive models. Moreover the generative component remains a good model of the input features despite the hybrid optimization objective. This offers additional capabilities such as detection of out-of-distribution inputs and enabling semi-supervised learning. The availability of the exact joint density p(targets, features) also allows us to compute many quantities readily, making our hybrid model a useful building block for downstream applications of probabilistic deep learning.
Neural Inverse Knitting: From Images to Manufacturing Instructions
Authors: Alexandre Kaspar, Tae-Hyun Oh, Liane Makatura, Petr Kellnhofer, Jacqueline Aslarus, Wojciech Matusik
Date: 2019-02-07 00:00:00

Motivated by the recent potential of mass customization brought by whole-garment knitting machines, we introduce the new problem of automatic machine instruction generation using a single image of the desired physical product, which we apply to machine knitting. We propose to tackle this problem by directly learning to synthesize regular machine instructions from real images. We create a cured dataset of real samples with their instruction counterpart and propose to use synthetic images to augment it in a novel way. We theoretically motivate our data mixing framework and show empirical results suggesting that making real images look more synthetic is beneficial in our problem setup.
Reversible GANs for Memory-efficient Image-to-Image Translation
Authors: Tycho F. A. van der Ouderaa, Daniel E. Worrall
Date: 2019-02-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image-to-Image Translation, Translation

The Pix2pix and CycleGAN losses have vastly improved the qualitative and
quantitative visual quality of results in image-to-image translation tasks. We
extend this framework by exploring approximately invertible architectures which
are well suited to these losses. These architectures are approximately
invertible by design and thus partially satisfy cycle-consistency before
training even begins. Furthermore, since invertible architectures have constant
memory complexity in depth, these models can be built arbitrarily deep. We are
able to demonstrate superior quantitative output on the Cityscapes and Maps
datasets at near constant memory budget.
Metaoptimization on a Distributed System for Deep Reinforcement Learning
Authors: Greg Heinrich, Iuri Frosio
Date: 2019-02-07 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Training intelligent agents through reinforcement learning is a notoriously
unstable procedure. Massive parallelization on GPUs and distributed systems has
been exploited to generate a large amount of training experiences and
consequently reduce instabilities, but the success of training remains strongly
influenced by the choice of the hyperparameters. To overcome this issue, we
introduce HyperTrick, a new metaoptimization algorithm, and show its effective
application to tune hyperparameters in the case of deep reinforcement learning,
while learning to play different Atari games on a distributed system. Our
analysis provides evidence of the interaction between the identification of the
optimal hyperparameters and the learned policy, that is typical of the case of
metaoptimization for deep reinforcement learning. When compared with
state-of-the-art metaoptimization algorithms, HyperTrick is characterized by a
simpler implementation and it allows learning similar policies, while making a
more effective use of the computational resources in a distributed system.
Variational Recurrent Neural Networks for Graph Classification
Authors: Edouard Pineau, Nathan de Lara
Date: 2019-02-07 00:00:00
Tasks: Classification, General Classification, Graph Classification

We address the problem of graph classification based only on structural information. Inspired by natural language processing techniques (NLP), our model sequentially embeds information to estimate class membership probabilities. Besides, we experiment with NLP-like variational regularization techniques, making the model predict the next node in the sequence as it reads it. We experimentally show that our model achieves state-of-the-art classification results on several standard molecular datasets. Finally, we perform a qualitative analysis and give some insights on whether the node prediction helps the model better classify graphs.
Sparse Regression and Adaptive Feature Generation for the Discovery of Dynamical Systems
Authors: Chinmay S. Kulkarni
Date: 2019-02-07 00:00:00
Tasks: regression

We study the performance of sparse regression methods and propose new
techniques to distill the governing equations of dynamical systems from data.
We first look at the generic methodology of learning interpretable equation
forms from data, proposed by Brunton et al., followed by performance of LASSO
for this purpose. We then propose a new algorithm that uses the dual of LASSO
optimization for higher accuracy and stability. In the second part, we propose
a novel algorithm that learns the candidate function library in a completely
data-driven manner to distill the governing equations of the dynamical system.
This is achieved via sequentially thresholded ridge regression (STRidge) over a
orthogonal polynomial space. The performance of the three discussed methods is
illustrated by looking the Lorenz 63 system and the quadratic Lorenz system.
Momentum Schemes with Stochastic Variance Reduction for Nonconvex Composite Optimization
Authors: Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh
Date: 2019-02-07 00:00:00

Two new stochastic variance-reduced algorithms named SARAH and SPIDER have been recently proposed, and SPIDER has been shown to achieve a near-optimal gradient oracle complexity for nonconvex optimization. However, the theoretical advantage of SPIDER does not lead to substantial improvement of practical performance over SVRG. To address this issue, momentum technique can be a good candidate to improve the performance of SPIDER. However, existing momentum schemes used in variance-reduced algorithms are designed specifically for convex optimization, and are not applicable to nonconvex scenarios. In this paper, we develop novel momentum schemes with flexible coefficient settings to accelerate SPIDER for nonconvex and nonsmooth composite optimization, and show that the resulting algorithms achieve the near-optimal gradient oracle complexity for achieving a generalized first-order stationary condition. Furthermore, we generalize our algorithm to online nonconvex and nonsmooth optimization, and establish an oracle complexity result that matches the state-of-the-art. Our extensive experiments demonstrate the superior performance of our proposed algorithm over other stochastic variance-reduced algorithms.
Virtual Training for a Real Application: Accurate Object-Robot Relative Localization without Calibration
Authors: Vianney Loing, Renaud Marlet, Mathieu Aubry
Date: 2019-02-07 00:00:00

Localizing an object accurately with respect to a robot is a key step for
autonomous robotic manipulation. In this work, we propose to tackle this task
knowing only 3D models of the robot and object in the particular case where the
scene is viewed from uncalibrated cameras -- a situation which would be typical
in an uncontrolled environment, e.g., on a construction site. We demonstrate
that this localization can be performed very accurately, with millimetric
errors, without using a single real image for training, a strong advantage
since acquiring representative training data is a long and expensive process.
Our approach relies on a classification Convolutional Neural Network (CNN)
trained using hundreds of thousands of synthetically rendered scenes with
randomized parameters. To evaluate our approach quantitatively and make it
comparable to alternative approaches, we build a new rich dataset of real robot
images with accurately localized blocks.
Aspect Specific Opinion Expression Extraction using Attention based LSTM-CRF Network
Authors: Abhishek Laddha, Arjun Mukherjee
Date: 2019-02-07 00:00:00
Tasks: Sentence, Sentiment Analysis

Opinion phrase extraction is one of the key tasks in fine-grained sentiment
analysis. While opinion expressions could be generic subjective expressions,
aspect specific opinion expressions contain both the aspect as well as the
opinion expression within the original sentence context. In this work, we
formulate the task as an instance of token-level sequence labeling. When
multiple aspects are present in a sentence, detection of opinion phrase
boundary becomes difficult and label of each word depend not only upon the
surrounding words but also with the concerned aspect. We propose a neural
network architecture with bidirectional LSTM (Bi-LSTM) and a novel attention
mechanism. Bi-LSTM layer learns the various sequential pattern among the words
without requiring any hand-crafted features. The attention mechanism captures
the importance of context words on a particular aspect opinion expression when
multiple aspects are present in a sentence via location and content based
memory. A Conditional Random Field (CRF) model is incorporated in the final
layer to explicitly model the dependencies among the output labels.
Experimental results on Hotel dataset from Tripadvisor.com showed that our
approach outperformed several state-of-the-art baselines.
Understanding Chat Messages for Sticker Recommendation in Messaging Apps
Authors: Abhishek Laddha, Mohamed Hanoosh, Debdoot Mukherjee, Parth Patwa, Ankur Narang
Date: 2019-02-07 00:00:00

Stickers are popularly used in messaging apps such as Hike to visually express a nuanced range of thoughts and utterances to convey exaggerated emotions. However, discovering the right sticker from a large and ever expanding pool of stickers while chatting can be cumbersome. In this paper, we describe a system for recommending stickers in real time as the user is typing based on the context of the conversation. We decompose the sticker recommendation (SR) problem into two steps. First, we predict the message that the user is likely to send in the chat. Second, we substitute the predicted message with an appropriate sticker. Majority of Hike's messages are in the form of text which is transliterated from users' native language to the Roman script. This leads to numerous orthographic variations of the same message and makes accurate message prediction challenging. To address this issue, we learn dense representations of chat messages employing character level convolution network in an unsupervised manner. We use them to cluster the messages that have the same meaning. In the subsequent steps, we predict the message cluster instead of the message. Our approach does not depend on human labelled data (except for validation), leading to fully automatic updation and tuning pipeline for the underlying models. We also propose a novel hybrid message prediction model, which can run with low latency on low-end phones that have severe computational limitations. Our described system has been deployed for more than $6$ months and is being used by millions of users along with hundreds of thousands of expressive stickers.
StampNet: unsupervised multi-class object discovery
Authors: Joost Visser, Alessandro Corbetta, Vlado Menkovski, Federico Toschi
Date: 2019-02-07 00:00:00
Tasks: Clustering, Image Clustering, Object, Object Discovery

Unsupervised object discovery in images involves uncovering recurring
patterns that define objects and discriminates them against the background.
This is more challenging than image clustering as the size and the location of
the objects are not known: this adds additional degrees of freedom and
increases the problem complexity. In this work, we propose StampNet, a novel
autoencoding neural network that localizes shapes (objects) over a simple
background in images and categorizes them simultaneously. StampNet consists of
a discrete latent space that is used to categorize objects and to determine the
location of the objects. The object categories are formed during the training,
resulting in the discovery of a fixed set of objects. We present a set of
experiments that demonstrate that StampNet is able to localize and cluster
multiple overlapping shapes with varying complexity including the digits from
the MNIST dataset. We also present an application of StampNet in the
localization of pedestrians in overhead depth-maps.
Single Network Panoptic Segmentation for Street Scene Understanding
Authors: Daan de Geus, Panagiotis Meletis, Gijs Dubbelman
Date: 2019-02-07 00:00:00
Tasks: Instance Segmentation, Panoptic Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

In this work, we propose a single deep neural network for panoptic
segmentation, for which the goal is to provide each individual pixel of an
input image with a class label, as in semantic segmentation, as well as a
unique identifier for specific objects in an image, following instance
segmentation. Our network makes joint semantic and instance segmentation
predictions and combines these to form an output in the panoptic format. This
has two main benefits: firstly, the entire panoptic prediction is made in one
pass, reducing the required computation time and resources; secondly, by
learning the tasks jointly, information is shared between the two tasks,
thereby improving performance. Our network is evaluated on two street scene
datasets: Cityscapes and Mapillary Vistas. By leveraging information exchange
and improving the merging heuristics, we increase the performance of the single
network, and achieve a score of 23.9 on the Panoptic Quality (PQ) metric on
Mapillary Vistas validation, with an input resolution of 640 x 900 pixels. On
Cityscapes validation, our method achieves a PQ score of 45.9 with an input
resolution of 512 x 1024 pixels. Moreover, our method decreases the prediction
time by a factor of 2 with respect to separate networks.
BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
Authors: Asa Cooper Stickland, Iain Murray
Date: 2019-02-07 00:00:00
Tasks: Multi-Task Learning, Natural Language Inference, Natural Language Understanding, Unsupervised Pre-training

Multi-task learning shares information between related tasks, sometimes reducing the number of parameters required. State-of-the-art results across multiple natural language understanding tasks in the GLUE benchmark have previously used transfer from a single large task: unsupervised pre-training with BERT, where a separate BERT model was fine-tuned for each task. We explore multi-task approaches that share a single BERT model with a small number of additional task-specific parameters. Using new adaptation modules, PALs or `projected attention layers', we match the performance of separately fine-tuned models on the GLUE benchmark with roughly 7 times fewer parameters, and obtain state-of-the-art results on the Recognizing Textual Entailment dataset.
Bayesian Reinforcement Learning via Deep, Sparse Sampling
Authors: Divya Grover, Debabrota Basu, Christos Dimitrakakis
Date: 2019-02-07 00:00:00
Tasks: Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We address the problem of Bayesian reinforcement learning using efficient model-based online planning. We propose an optimism-free Bayes-adaptive algorithm to induce deeper and sparser exploration with a theoretical bound on its performance relative to the Bayes optimal policy, with a lower computational complexity. The main novelty is the use of a candidate policy generator, to generate long-term options in the planning tree (over beliefs), which allows us to create much sparser and deeper trees. Experimental results on different environments show that in comparison to the state-of-the-art, our algorithm is both computationally more efficient, and obtains significantly higher reward in discrete environments.
Bounds for the VC Dimension of 1NN Prototype Sets
Authors: Iain A. D. Gunn, Ludmila I. Kuncheva
Date: 2019-02-07 00:00:00

In Statistical Learning, the Vapnik-Chervonenkis (VC) dimension is an
important combinatorial property of classifiers. To our knowledge, no
theoretical results yet exist for the VC dimension of edited nearest-neighbour
(1NN) classifiers with reference set of fixed size. Related theoretical results
are scattered in the literature and their implications have not been made
explicit. We collect some relevant results and use them to provide explicit
lower and upper bounds for the VC dimension of 1NN classifiers with a prototype
set of fixed size. We discuss the implications of these bounds for the size of
training set needed to learn such a classifier to a given accuracy. Further, we
provide a new lower bound for the two-dimensional case, based on a new
geometrical argument.
Radial and Directional Posteriors for Bayesian Neural Networks
Authors: Changyong Oh, Kamil Adamczewski, Mijung Park
Date: 2019-02-07 00:00:00

We propose a new variational family for Bayesian neural networks. We
decompose the variational posterior into two components, where the radial
component captures the strength of each neuron in terms of its magnitude; while
the directional component captures the statistical dependencies among the
weight parameters. The dependencies learned via the directional density provide
better modeling performance compared to the widely-used Gaussian
mean-field-type variational family. In addition, the strength of input and
output neurons learned via the radial density provides a structured way to
compress neural networks. Indeed, experiments show that our variational family
improves predictive performance and yields compressed networks simultaneously.