pwc-archive/papers-with-abstracts huggingface dataset:
PBGen: Partial Binarization of Deconvolution-Based Generators for Edge Intelligence
Authors: Jinglan Liu, Jiaxin Zhang, Yukun Ding, Xiaowei Xu, Meng Jiang, Yiyu Shi
Date: 2018-02-26 00:00:00
Tasks: Binarization

This work explores the binarization of the deconvolution-based generator in a GAN for memory saving and speedup of image construction. Our study suggests that different from convolutional neural networks (including the discriminator) where all layers can be binarized, only some of the layers in the generator can be binarized without significant performance loss. Supported by theoretical analysis and verified by experiments, a direct metric based on the dimension of deconvolution operations is established, which can be used to quickly decide which layers in the generator can be binarized. Our results also indicate that both the generator and the discriminator should be binarized simultaneously for balanced competition and better performance. Experimental results based on CelebA suggest that directly applying state-of-the-art binarization techniques to all the layers of the generator will lead to 2.83$\times$ performance loss measured by sliced Wasserstein distance compared with the original generator, while applying them to selected layers only can yield up to 25.81$\times$ saving in memory consumption, and 1.96$\times$ and 1.32$\times$ speedup in inference and training respectively with little performance loss.
Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions
Authors: Li Yao, Jordan Prosky, Eric Poblenz, Ben Covington, Kevin Lyman
Date: 2018-03-21 00:00:00
Tasks: Diagnostic, Medical Diagnosis

Diagnostic imaging often requires the simultaneous identification of a
multitude of findings of varied size and appearance. Beyond global indication
of said findings, the prediction and display of localization information
improves trust in and understanding of results when augmenting clinical
workflow. Medical training data rarely includes more than global image-level
labels as segmentations are time-consuming and expensive to collect. We
introduce an approach to managing these practical constraints by applying a
novel architecture which learns at multiple resolutions while generating
saliency maps with weak supervision. Further, we parameterize the Log-Sum-Exp
pooling function with a learnable lower-bounded adaptation (LSE-LBA) to build
in a sharpness prior and better handle localizing abnormalities of different
sizes using only image-level labels. Applying this approach to interpreting
chest x-rays, we set the state of the art on 9 abnormalities in the NIH's CXR14
dataset while generating saliency maps with the highest resolution to date.
Robust Depth Estimation from Auto Bracketed Images
Authors: Sunghoon Im, Hae-Gon Jeon, In So Kweon
Date: 2018-03-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth Estimation, Stereo Matching, Stereo Matching Hand

As demand for advanced photographic applications on hand-held devices grows,
these electronics require the capture of high quality depth. However, under
low-light conditions, most devices still suffer from low imaging quality and
inaccurate depth acquisition. To address the problem, we present a robust depth
estimation method from a short burst shot with varied intensity (i.e., Auto
Bracketing) or strong noise (i.e., High ISO). We introduce a geometric
transformation between flow and depth tailored for burst images, enabling our
learning-based multi-view stereo matching to be performed effectively. We then
describe our depth estimation pipeline that incorporates the geometric
transformation into our residual-flow network. It allows our framework to
produce an accurate depth map even with a bracketed image sequence. We
demonstrate that our method outperforms state-of-the-art methods for various
datasets captured by a smartphone and a DSLR camera. Moreover, we show that the
estimated depth is applicable for image quality enhancement and photographic
editing.
Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study
Authors: Lorin Crawford, Seth R. Flaxman, Daniel E. Runcie, Mike West
Date: 2018-01-22 00:00:00
Tasks: regression, Variable Selection

The central aim in this paper is to address variable selection questions in
nonlinear and nonparametric regression. Motivated by statistical genetics,
where nonlinear interactions are of particular interest, we introduce a novel
and interpretable way to summarize the relative importance of predictor
variables. Methodologically, we develop the "RelATive cEntrality" (RATE)
measure to prioritize candidate genetic variants that are not just marginally
important, but whose associations also stem from significant covarying
relationships with other variants in the data. We illustrate RATE through
Bayesian Gaussian process regression, but the methodological innovations apply
to other "black box" methods. It is known that nonlinear models often exhibit
greater predictive accuracy than linear models, particularly for phenotypes
generated by complex genetic architectures. With detailed simulations and two
real data association mapping studies, we show that applying RATE enables an
explanation for this improved performance.
On the Applicability of Registration Uncertainty
Authors: Jie Luo, Alireza Sedghi, Karteek Popuri, Dana Cobzas, Miaomiao Zhang, Frank Preiswerk, Matthew Toews, Alexandra Golby, Masashi Sugiyama, William M. Wells III, Sarah Frisken
Date: 2018-03-14 00:00:00
Tasks: Image Registration

Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data. If surgeons receive inaccurately calculated registration uncertainty and misplace unwarranted confidence in the alignment solutions, severe consequences may result. For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters. The majority of existing research focuses on trying out different summary statistics as well as a means to exploit them. Distinctively, in this paper, we study two rarely examined topics: (1) whether those summary statistics of the transformation distribution most informatively represent the registration uncertainty; (2) Does utilizing the registration uncertainty always be beneficial. We show that there are two types of uncertainties: the transformation uncertainty, Ut, and label uncertainty Ul. The conventional way of using Ut to quantify Ul is inappropriate and can be misleading. By a real data experiment, we also share a potentially critical finding that making use of the registration uncertainty may not always be an improvement.
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
Authors: Bowen Cheng, Yunchao Wei, Honghui Shi, Rogerio Feris, JinJun Xiong, Thomas Huang
Date: 2018-03-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Classification, General Classification, Multi-Task Learning

Recent region-based object detectors are usually built with separate
classification and localization branches on top of shared feature extraction
networks. In this paper, we analyze failure cases of state-of-the-art detectors
and observe that most hard false positives result from classification instead
of localization. We conjecture that: (1) Shared feature representation is not
optimal due to the mismatched goals of feature learning for classification and
localization; (2) multi-task learning helps, yet optimization of the multi-task
loss may result in sub-optimal for individual tasks; (3) large receptive field
for different scales leads to redundant context information for small
objects.We demonstrate the potential of detector classification power by a
simple, effective, and widely-applicable Decoupled Classification Refinement
(DCR) network. DCR samples hard false positives from the base classifier in
Faster RCNN and trains a RCNN-styled strong classifier. Experiments show new
state-of-the-art results on PASCAL VOC and COCO without any bells and whistles.
A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation
Authors: Jie Luo, Matt Toews, Ines Machado, Sarah Frisken, Miaomiao Zhang, Frank Preiswerk, Alireza Sedghi, Hongyi Ding, Steve Pieper, Polina Golland, Alexandra Golby, Masashi Sugiyama, William M. Wells III
Date: 2018-03-20 00:00:00

A reliable Ultrasound (US)-to-US registration method to compensate for brain
shift would substantially improve Image-Guided Neurological Surgery. Developing
such a registration method is very challenging, due to factors such as missing
correspondence in images, the complexity of brain pathology and the demand for
fast computation. We propose a novel feature-driven active framework. Here,
landmarks and their displacement are first estimated from a pair of US images
using corresponding local image features. Subsequently, a Gaussian Process (GP)
model is used to interpolate a dense deformation field from the sparse
landmarks. Kernels of the GP are estimated by using variograms and a discrete
grid search method. If necessary, the user can actively add new landmarks based
on the image context and visualization of the uncertainty measure provided by
the GP to further improve the result. We retrospectively demonstrate our
registration framework as a robust and accurate brain shift compensation
solution on clinical data acquired during neurosurgery.
Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products
Authors: Ângelo Cardoso, Fabio Daolio, Saúl Vargas
Date: 2018-03-20 00:00:00
Tasks: Recommendation Systems

In this paper, we describe a solution to tackle a common set of challenges in
e-commerce, which arise from the fact that new products are continually being
added to the catalogue. The challenges involve properly personalising the
customer experience, forecasting demand and planning the product range. We
argue that the foundational piece to solve all of these problems is having
consistent and detailed information about each product, information that is
rarely available or consistent given the multitude of suppliers and types of
products. We describe in detail the architecture and methodology implemented at
ASOS, one of the world's largest fashion e-commerce retailers, to tackle this
problem. We then show how this quantitative understanding of the products can
be leveraged to improve recommendations in a hybrid recommender system
approach.
Neural Voice Cloning with a Few Samples
Authors: Sercan O. Arik, Jitong Chen, Kainan Peng, Wei Ping, Yanqi Zhou
Date: 2018-02-14 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Speech Synthesis, Voice Cloning

Voice cloning is a highly desired feature for personalized speech interfaces.
Neural network based speech synthesis has been shown to generate high quality
speech for a large number of speakers. In this paper, we introduce a neural
voice cloning system that takes a few audio samples as input. We study two
approaches: speaker adaptation and speaker encoding. Speaker adaptation is
based on fine-tuning a multi-speaker generative model with a few cloning
samples. Speaker encoding is based on training a separate model to directly
infer a new speaker embedding from cloning audios and to be used with a
multi-speaker generative model. In terms of naturalness of the speech and its
similarity to original speaker, both approaches can achieve good performance,
even with very few cloning audios. While speaker adaptation can achieve better
naturalness and similarity, the cloning time or required memory for the speaker
encoding approach is significantly less, making it favorable for low-resource
deployment.
Entity Resolution and Federated Learning get a Federated Resolution
Authors: Richard Nock, Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Giorgio Patrini, Guillaume Smith, Brian Thorne
Date: 2018-03-11 00:00:00
Tasks: Entity Resolution, Federated Learning, Question Answering

Consider two data providers, each maintaining records of different feature
sets about common entities. They aim to learn a linear model over the whole set
of features. This problem of federated learning over vertically partitioned
data includes a crucial upstream issue: entity resolution, i.e. finding the
correspondence between the rows of the datasets. It is well known that entity
resolution, just like learning, is mistake-prone in the real world. Despite the
importance of the problem, there has been no formal assessment of how errors in
entity resolution impact learning.
  In this paper, we provide a thorough answer to this question, answering how
optimal classifiers, empirical losses, margins and generalisation abilities are
affected. While our answer spans a wide set of losses --- going beyond proper,
convex, or classification calibrated ---, it brings simple practical arguments
to upgrade entity resolution as a preprocessing step to learning. One of these
suggests that entity resolution should be aimed at controlling or minimizing
the number of matching errors between examples of distinct classes. In our
experiments, we modify a simple token-based entity resolution algorithm so that
it indeed aims at avoiding matching rows belonging to different classes, and
perform experiments in the setting where entity resolution relies on noisy
data, which is very relevant to real world domains. Notably, our approach
covers the case where one peer \textit{does not} have classes, or a noisy
record of classes. Experiments display that using the class information during
entity resolution can buy significant uplift for learning at little expense
from the complexity standpoint.
Nonsmooth Frank-Wolfe using Uniform Affine Approximations
Authors: Edward Cheung, Yuying Li
Date: 2017-10-16 00:00:00

Frank-Wolfe methods (FW) have gained significant interest in the machine
learning community due to its ability to efficiently solve large problems that
admit a sparse structure (e.g. sparse vectors and low-rank matrices). However
the performance of the existing FW method hinges on the quality of the linear
approximation. This typically restricts FW to smooth functions for which the
approximation quality, indicated by a global curvature measure, is reasonably
good.
  In this paper, we propose a modified FW algorithm amenable to nonsmooth
functions by optimizing for approximation quality over all affine
approximations given a neighborhood of interest. We analyze theoretical
properties of the proposed algorithm and demonstrate that it overcomes many
issues associated with existing methods in the context of nonsmooth low-rank
matrix estimation.
Attend and Interact: Higher-Order Object Interactions for Video Understanding
Authors: Chih-Yao Ma, Asim Kadav, Iain Melvin, Zsolt Kira, Ghassan AlRegib, Hans Peter Graf
Date: 2017-11-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Classification, Action Recognition, Object, Relationship Detection, Temporal Action Localization, Video Captioning, Video Classification, Video Description, Video Understanding, Visual Relationship Detection

Human actions often involve complex interactions across several inter-related
objects in the scene. However, existing approaches to fine-grained video
understanding or visual relationship detection often rely on single object
representation or pairwise object relationships. Furthermore, learning
interactions across multiple objects in hundreds of frames for video is
computationally infeasible and performance may suffer since a large
combinatorial space has to be modeled. In this paper, we propose to efficiently
learn higher-order interactions between arbitrary subgroups of objects for
fine-grained video understanding. We demonstrate that modeling object
interactions significantly improves accuracy for both action recognition and
video captioning, while saving more than 3-times the computation over
traditional pairwise relationships. The proposed method is validated on two
large-scale datasets: Kinetics and ActivityNet Captions. Our SINet and
SINet-Caption achieve state-of-the-art performances on both datasets even
though the videos are sampled at a maximum of 1 FPS. To the best of our
knowledge, this is the first work modeling object interactions on open domain
large-scale video datasets, and we additionally model higher-order object
interactions which improves the performance with low computational costs.
Uncertainty Estimation via Stochastic Batch Normalization
Authors: Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry Vetrov
Date: 2018-02-13 00:00:00

In this work, we investigate Batch Normalization technique and propose its
probabilistic interpretation. We propose a probabilistic model and show that
Batch Normalization maximazes the lower bound of its marginalized
log-likelihood. Then, according to the new probabilistic model, we design an
algorithm which acts consistently during train and test. However, inference
becomes computationally inefficient. To reduce memory and computational cost,
we propose Stochastic Batch Normalization -- an efficient approximation of
proper inference procedure. This method provides us with a scalable uncertainty
estimation technique. We demonstrate the performance of Stochastic Batch
Normalization on popular architectures (including deep convolutional
architectures: VGG-like and ResNets) for MNIST and CIFAR-10 datasets.
Learning Robotic Assembly from CAD
Authors: Garrett Thomas, Melissa Chien, Aviv Tamar, Juan Aparicio Ojea, Pieter Abbeel
Date: 2018-03-20 00:00:00
Tasks: Contact-rich Manipulation, Motion Planning, Reinforcement Learning, Reinforcement Learning (RL), State Estimation

In this work, motivated by recent manufacturing trends, we investigate
autonomous robotic assembly. Industrial assembly tasks require contact-rich
manipulation skills, which are challenging to acquire using classical control
and motion planning approaches. Consequently, robot controllers for assembly
domains are presently engineered to solve a particular task, and cannot easily
handle variations in the product or environment. Reinforcement learning (RL) is
a promising approach for autonomously acquiring robot skills that involve
contact-rich dynamics. However, RL relies on random exploration for learning a
control policy, which requires many robot executions, and often gets trapped in
locally suboptimal solutions. Instead, we posit that prior knowledge, when
available, can improve RL performance. We exploit the fact that in modern
assembly domains, geometric information about the task is readily available via
the CAD design files. We propose to leverage this prior knowledge by guiding RL
along a geometric motion plan, calculated using the CAD data. We show that our
approach effectively improves over traditional control approaches for tracking
the motion plan, and can solve assembly tasks that require high precision, even
without accurate state estimation. In addition, we propose a neural network
architecture that can learn to track the motion plan, and generalize the
assembly controller to changes in the object positions.
Domain Adaptation with Randomized Expectation Maximization
Authors: Twan van Laarhoven, Elena Marchiori
Date: 2018-03-20 00:00:00
Tasks: Domain Adaptation, Transfer Learning

Domain adaptation (DA) is the task of classifying an unlabeled dataset
(target) using a labeled dataset (source) from a related domain. The majority
of successful DA methods try to directly match the distributions of the source
and target data by transforming the feature space. Despite their success, state
of the art methods based on this approach are either involved or unable to
directly scale to data with many features. This article shows that domain
adaptation can be successfully performed by using a very simple randomized
expectation maximization (EM) method. We consider two instances of the method,
which involve logistic regression and support vector machine, respectively. The
underlying assumption of the proposed method is the existence of a good single
linear classifier for both source and target domain. The potential limitations
of this assumption are alleviated by the flexibility of the method, which can
directly incorporate deep features extracted from a pre-trained deep neural
network. The resulting algorithm is strikingly easy to implement and apply. We
test its performance on 36 real-life adaptation tasks over text and image data
with diverse characteristics. The method achieves state-of-the-art results,
competitive with those of involved end-to-end deep transfer-learning methods.
IntPhys: A Framework and Benchmark for Visual Intuitive Physics Reasoning
Authors: Ronan Riochet, Mario Ynocente Castro, Mathieu Bernard, Adam Lerer, Rob Fergus, Véronique Izard, Emmanuel Dupoux
Date: 2018-03-20 00:00:00

In order to reach human performance on complexvisual tasks, artificial systems need to incorporate a sig-nificant amount of understanding of the world in termsof macroscopic objects, movements, forces, etc. Inspiredby work on intuitive physics in infants, we propose anevaluation benchmark which diagnoses how much a givensystem understands about physics by testing whether itcan tell apart well matched videos of possible versusimpossible events constructed with a game engine. Thetest requires systems to compute a physical plausibilityscore over an entire video. It is free of bias and cantest a range of basic physical reasoning concepts. Wethen describe two Deep Neural Networks systems aimedat learning intuitive physics in an unsupervised way,using only physically possible videos. The systems aretrained with a future semantic mask prediction objectiveand tested on the possible versus impossible discrimi-nation task. The analysis of their results compared tohuman data gives novel insights in the potentials andlimitations of next frame prediction architectures.
Online Learning: Sufficient Statistics and the Burkholder Method
Authors: Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
Date: 2018-03-20 00:00:00

We uncover a fairly general principle in online learning: If regret can be
(approximately) expressed as a function of certain "sufficient statistics" for
the data sequence, then there exists a special Burkholder function that 1) can
be used algorithmically to achieve the regret bound and 2) only depends on
these sufficient statistics, not the entire data sequence, so that the online
strategy is only required to keep the sufficient statistics in memory. This
characterization is achieved by bringing the full power of the Burkholder
Method --- originally developed for certifying probabilistic martingale
inequalities --- to bear on the online learning setting.
  To demonstrate the scope and effectiveness of the Burkholder method, we
develop a novel online strategy for matrix prediction that attains a regret
bound corresponding to the variance term in matrix concentration inequalities.
We also present a linear-time/space prediction strategy for parameter free
supervised learning with linear classes and general smooth norms.
A Survey of Deep Learning Techniques for Mobile Robot Applications
Authors: Jahanzaib Shabbir, Tarique Anwer
Date: 2018-03-20 00:00:00
Tasks: Deep Learning, Survey

Advancements in deep learning over the years have attracted research into how
deep artificial neural networks can be used in robotic systems. This research
survey will present a summarization of the current research with a specific
focus on the gains and obstacles for deep learning to be applied to mobile
robotics.