pwc-archive/papers-with-abstracts huggingface dataset:
SphereFace: Deep Hypersphere Embedding for Face Recognition
Authors: Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, Le Song
Date: 2017-04-26 00:00:00
Proceeding: CVPR 2017 7
Tasks: Face Identification, Face Recognition, Face Verification

This paper addresses deep face recognition (FR) problem under open-set
protocol, where ideal face features are expected to have smaller maximal
intra-class distance than minimal inter-class distance under a suitably chosen
metric space. However, few existing algorithms can effectively achieve this
criterion. To this end, we propose the angular softmax (A-Softmax) loss that
enables convolutional neural networks (CNNs) to learn angularly discriminative
features. Geometrically, A-Softmax loss can be viewed as imposing
discriminative constraints on a hypersphere manifold, which intrinsically
matches the prior that faces also lie on a manifold. Moreover, the size of
angular margin can be quantitatively adjusted by a parameter $m$. We further
derive specific $m$ to approximate the ideal feature criterion. Extensive
analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF)
and MegaFace Challenge show the superiority of A-Softmax loss in FR tasks. The
code has also been made publicly available.
Learning Spherical Convolution for Fast Features from 360° Imagery
Authors: Yu-Chuan Su, Kristen Grauman
Date: 2017-08-02 00:00:00
Proceeding: NeurIPS 2017 12

While 360{\deg} cameras offer tremendous new possibilities in vision,
graphics, and augmented reality, the spherical images they produce make core
feature extraction non-trivial. Convolutional neural networks (CNNs) trained on
images from perspective cameras yield "flat" filters, yet 360{\deg} images
cannot be projected to a single plane without significant distortion. A naive
solution that repeatedly projects the viewing sphere to all tangent planes is
accurate, but much too computationally intensive for real problems. We propose
to learn a spherical convolutional network that translates a planar CNN to
process 360{\deg} imagery directly in its equirectangular projection. Our
approach learns to reproduce the flat filter outputs on 360{\deg} data,
sensitive to the varying distortion effects across the viewing sphere. The key
benefits are 1) efficient feature extraction for 360{\deg} images and video,
and 2) the ability to leverage powerful pre-trained networks researchers have
carefully honed (together with massive labeled image training sets) for
perspective images. We validate our approach compared to several alternative
methods in terms of both raw CNN output accuracy as well as applying a
state-of-the-art "flat" object detector to 360{\deg} data. Our method yields
the most accurate results while saving orders of magnitude in computation
versus the existing exact reprojection solution.
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
Authors: Balázs Hidasi, Alexandros Karatzoglou
Date: 2017-06-12 00:00:00
Proceeding: ICLR 2018 1
Tasks: Collaborative Filtering, Data Augmentation, Session-Based Recommendations

RNNs have been shown to be excellent models for sequential data and in
particular for data that is generated by users in an session-based manner. The
use of RNNs provides impressive performance benefits over classical methods in
session-based recommendations. In this work we introduce novel ranking loss
functions tailored to RNNs in the recommendation setting. The improved
performance of these losses over alternatives, along with further tricks and
refinements described in this work, allow for an overall improvement of up to
35% in terms of MRR and Recall@20 over previous session-based RNN solutions and
up to 53% over classical collaborative filtering approaches. Unlike data
augmentation-based improvements, our method does not increase training times
significantly. We further demonstrate the performance gain of the RNN over
baselines in an online A/B test.
Evaluating approaches for supervised semantic labeling
Authors: Natalia Ruemmele, Yuriy Tyshetskiy, Alex Collins
Date: 2018-01-29 00:00:00
Tasks: BIG-bench Machine Learning, Data Integration, Feature Engineering, Multi-class Classification

Relational data sources are still one of the most popular ways to store
enterprise or Web data, however, the issue with relational schema is the lack
of a well-defined semantic description. A common ontology provides a way to
represent the meaning of a relational schema and can facilitate the integration
of heterogeneous data sources within a domain. Semantic labeling is achieved by
mapping attributes from the data sources to the classes and properties in the
ontology. We formulate this problem as a multi-class classification problem
where previously labeled data sources are used to learn rules for labeling new
data sources. The majority of existing approaches for semantic labeling have
focused on data integration challenges such as naming conflicts and semantic
heterogeneity. In addition, machine learning approaches typically have issues
around class imbalance, lack of labeled instances and relative importance of
attributes. To address these issues, we develop a new machine learning model
with engineered features as well as two deep learning models which do not
require extensive feature engineering. We evaluate our new approaches with the
state-of-the-art.
Not All Ops Are Created Equal!
Authors: Liangzhen Lai, Naveen Suda, Vikas Chandra
Date: 2018-01-12 00:00:00
Tasks: All

Efficient and compact neural network models are essential for enabling the
deployment on mobile and embedded devices. In this work, we point out that
typical design metrics for gauging the efficiency of neural network
architectures -- total number of operations and parameters -- are not
sufficient. These metrics may not accurately correlate with the actual
deployment metrics such as energy and memory footprint. We show that throughput
and energy varies by up to 5X across different neural network operation types
on an off-the-shelf Arm Cortex-M7 microcontroller. Furthermore, we show that
the memory required for activation data also need to be considered, apart from
the model parameters, for network architecture exploration studies.
Denoising Arterial Spin Labeling Cerebral Blood Flow Images Using Deep Learning
Authors: Danfeng Xie, Li Bai, Ze Wang
Date: 2018-01-29 00:00:00
Tasks: Denoising

Arterial spin labeling perfusion MRI is a noninvasive technique for measuring
quantitative cerebral blood flow (CBF), but the measurement is subject to a low
signal-to-noise-ratio(SNR). Various post-processing methods have been proposed
to denoise ASL MRI but only provide moderate improvement. Deep learning (DL) is
an emerging technique that can learn the most representative signal from data
without prior modeling which can be highly complex and analytically
indescribable. The purpose of this study was to assess whether the record
breaking performance of DL can be translated into ASL MRI denoising. We used
convolutional neural network (CNN) to build the DL ASL denosing model (DL-ASL)
to inherently consider the inter-voxel correlations. To better guide DL-ASL
training, we incorporated prior knowledge about ASL MRI: the structural
similarity between ASL CBF map and grey matter probability map. A relatively
large sample data were used to train the model which was subsequently applied
to a new set of data for testing. Experimental results showed that DL-ASL
achieved state-of-the-art denoising performance for ASL MRI as compared to
current routine methods in terms of higher SNR, keeping CBF quantification
quality while shorten the acquisition time by 75%, and automatic partial volume
correction.
Deep Learning based Retinal OCT Segmentation
Authors: Mike Pekala, Neil Joshi, David E. Freund, Neil M. Bressler, Delia Cabrera DeBuc, Philippe M. Burlina
Date: 2018-01-29 00:00:00
Tasks: Deep Learning, regression, Segmentation, Semantic Segmentation

Our objective is to evaluate the efficacy of methods that use deep learning
(DL) for the automatic fine-grained segmentation of optical coherence
tomography (OCT) images of the retina. OCT images from 10 patients with mild
non-proliferative diabetic retinopathy were used from a public (U. of Miami)
dataset. For each patient, five images were available: one image of the fovea
center, two images of the perifovea, and two images of the parafovea. For each
image, two expert graders each manually annotated five retinal surfaces (i.e.
boundaries between pairs of retinal layers). The first grader's annotations
were used as ground truth and the second grader's annotations to compute
inter-operator agreement. The proposed automated approach segments images using
fully convolutional networks (FCNs) together with Gaussian process (GP)-based
regression as a post-processing step to improve the quality of the estimates.
Using 10-fold cross validation, the performance of the algorithms is determined
by computing the per-pixel unsigned error (distance) between the automated
estimates and the ground truth annotations generated by the first manual
grader. We compare the proposed method against five state of the art automatic
segmentation techniques. The results show that the proposed methods compare
favorably with state of the art techniques, resulting in the smallest mean
unsigned error values and associated standard deviations, and performance is
comparable with human annotation of retinal layers from OCT when there is only
mild retinopathy. The results suggest that semantic segmentation using FCNs,
coupled with regression-based post-processing, can effectively solve the OCT
segmentation problem on par with human capabilities with mild retinopathy.
Towards a Quantum World Wide Web
Authors: Diederik Aerts, Jonito Aerts Arguelles, Lester Beltran, Lyneth Beltran, Isaac Distrito, Massimiliano Sassoli de Bianchi, Sandro Sozzo, Tomas Veloz
Date: 2017-03-20 00:00:00
Tasks: Decision Making

We elaborate a quantum model for the meaning associated with corpora of
written documents, like the pages forming the World Wide Web. To that end, we
are guided by how physicists constructed quantum theory for microscopic
entities, which unlike classical objects cannot be fully represented in our
spatial theater. We suggest that a similar construction needs to be carried out
by linguists and computational scientists, to capture the full meaning carried
by collections of documental entities. More precisely, we show how to associate
a quantum-like 'entity of meaning' to a 'language entity formed by printed
documents', considering the latter as the collection of traces that are left by
the former, in specific results of search actions that we describe as
measurements. In other words, we offer a perspective where a collection of
documents, like the Web, is described as the space of manifestation of a more
complex entity - the QWeb - which is the object of our modeling, drawing its
inspiration from previous studies on operational-realistic approaches to
quantum physics and quantum modeling of human cognition and decision-making. We
emphasize that a consistent QWeb model needs to account for the observed
correlations between words appearing in printed documents, e.g.,
co-occurrences, as the latter would depend on the 'meaning connections'
existing between the concepts that are associated with these words. In that
respect, we show that both 'context and interference (quantum) effects' are
required to explain the probabilities calculated by counting the relative
number of documents containing certain words and co-ocurrrences of words.
Context encoding enables machine learning-based quantitative photoacoustics
Authors: Thomas Kirchner, Janek Gröhl, Lena Maier-Hein
Date: 2017-06-12 00:00:00
Tasks: BIG-bench Machine Learning

Real-time monitoring of functional tissue parameters, such as local blood
oxygenation, based on optical imaging could provide groundbreaking advances in
the diagnosis and interventional therapy of various diseases. While
photoacoustic (PA) imaging is a novel modality with great potential to measure
optical absorption deep inside tissue, quantification of the measurements
remains a major challenge. In this paper, we introduce the first machine
learning based approach to quantitative PA imaging (qPAI), which relies on
learning the fluence in a voxel to deduce the corresponding optical absorption.
The method encodes relevant information of the measured signal and the
characteristics of the imaging system in voxel-based feature vectors, which
allow the generation of thousands of training samples from a single simulated
PA image. Comprehensive in silico experiments suggest that context encoding
(CE)-qPAI enables highly accurate and robust quantification of the local
fluence and thereby the optical absorption from PA images.
A Comparison of Resampling and Recursive Partitioning Methods in Random Forest for Estimating the Asymptotic Variance Using the Infinitesimal Jackknife
Authors: Cole Brokamp, MB Rao, Patrick Ryan, Roman Jandarov
Date: 2017-06-19 00:00:00
Tasks: Selection bias, Variable Selection

The infinitesimal jackknife (IJ) has recently been applied to the random
forest to estimate its prediction variance. These theorems were verified under
a traditional random forest framework which uses classification and regression
trees (CART) and bootstrap resampling. However, random forests using
conditional inference (CI) trees and subsampling have been found to be not
prone to variable selection bias. Here, we conduct simulation experiments using
a novel approach to explore the applicability of the IJ to random forests using
variations on the resampling method and base learner. Test data points were
simulated and each trained using random forest on one hundred simulated
training data sets using different combinations of resampling and base
learners. Using CI trees instead of traditional CART trees as well as using
subsampling instead of bootstrap sampling resulted in a much more accurate
estimation of prediction variance when using the IJ. The random forest
variations here have been incorporated into an open source software package for
the R programming language.
Object-based reasoning in VQA
Authors: Mikyas T. Desta, Larry Chen, Tomasz Kornuta
Date: 2018-01-29 00:00:00
Tasks: Object, object-detection, Object Detection, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) is a novel problem domain where multi-modal
inputs must be processed in order to solve the task given in the form of a
natural language. As the solutions inherently require to combine visual and
natural language processing with abstract reasoning, the problem is considered
as AI-complete. Recent advances indicate that using high-level, abstract facts
extracted from the inputs might facilitate reasoning. Following that direction
we decided to develop a solution combining state-of-the-art object detection
and reasoning modules. The results, achieved on the well-balanced CLEVR
dataset, confirm the promises and show significant, few percent improvements of
accuracy on the complex "counting" task.
Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings
Authors: Hanan Aldarmaki, Mahesh Mohan, Mona Diab
Date: 2017-12-19 00:00:00
Proceeding: TACL 2018 1
Tasks: Word Embeddings

Most existing methods for automatic bilingual dictionary induction rely on
prior alignments between the source and target languages, such as parallel
corpora or seed dictionaries. For many language pairs, such supervised
alignments are not readily available. We propose an unsupervised approach for
learning a bilingual dictionary for a pair of languages given their
independently-learned monolingual word embeddings. The proposed method exploits
local and global structures in monolingual vector spaces to align them such
that similar words are mapped to each other. We show empirically that the
performance of bilingual correspondents learned using our proposed unsupervised
method is comparable to that of using supervised bilingual correspondents from
a seed dictionary.
Matrix Completion for Structured Observations
Authors: Denali Molitor, Deanna Needell
Date: 2018-01-29 00:00:00
Tasks: Matrix Completion

The need to predict or fill-in missing data, often referred to as matrix
completion, is a common challenge in today's data-driven world. Previous
strategies typically assume that no structural difference between observed and
missing entries exists. Unfortunately, this assumption is woefully unrealistic
in many applications. For example, in the classic Netflix challenge, in which
one hopes to predict user-movie ratings for unseen films, the fact that the
viewer has not watched a given movie may indicate a lack of interest in that
movie, thus suggesting a lower rating than otherwise expected. We propose
adjusting the standard nuclear norm minimization strategy for matrix completion
to account for such structural differences between observed and unobserved
entries by regularizing the values of the unobserved entries. We show that the
proposed method outperforms nuclear norm minimization in certain settings.
Depth Adaptive Deep Neural Network for Semantic Segmentation
Authors: Byeongkeun Kang, Yeejin Lee, Truong Q. Nguyen
Date: 2017-08-05 00:00:00
Tasks: Hand Segmentation, Segmentation, Semantic Segmentation

In this work, we present the depth-adaptive deep neural network using a depth
map for semantic segmentation. Typical deep neural networks receive inputs at
the predetermined locations regardless of the distance from the camera. This
fixed receptive field presents a challenge to generalize the features of
objects at various distances in neural networks. Specifically, the
predetermined receptive fields are too small at a short distance, and vice
versa. To overcome this challenge, we develop a neural network which is able to
adapt the receptive field not only for each layer but also for each neuron at
the spatial location. To adjust the receptive field, we propose the
depth-adaptive multiscale (DaM) convolution layer consisting of the adaptive
perception neuron and the in-layer multiscale neuron. The adaptive perception
neuron is to adjust the receptive field at each spatial location using the
corresponding depth information. The in-layer multiscale neuron is to apply the
different size of the receptive field at each feature space to learn features
at multiple scales. The proposed DaM convolution is applied to two fully
convolutional neural networks. We demonstrate the effectiveness of the proposed
neural networks on the publicly available RGB-D dataset for semantic
segmentation and the novel hand segmentation dataset for hand-object
interaction. The experimental results show that the proposed method outperforms
the state-of-the-art methods without any additional layers or
pre/post-processing.
Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing
Authors: David-Alexandre Beaupré, Guillaume-Alexandre Bilodeau, Nicolas Saunier
Date: 2018-01-29 00:00:00
Tasks: Multiple Object Tracking, object-detection, Object Detection, Object Tracking, Optical Flow Estimation

In this paper, we present a new method for detecting road users in an urban
environment which leads to an improvement in multiple object tracking. Our
method takes as an input a foreground image and improves the object detection
and segmentation. This new image can be used as an input to trackers that use
foreground blobs from background subtraction. The first step is to create
foreground images for all the frames in an urban video. Then, starting from the
original blobs of the foreground image, we merge the blobs that are close to
one another and that have similar optical flow. The next step is extracting the
edges of the different objects to detect multiple objects that might be very
close (and be merged in the same blob) and to adjust the size of the original
blobs. At the same time, we use the optical flow to detect occlusion of objects
that are moving in opposite directions. Finally, we make a decision on which
information we keep in order to construct a new foreground image with blobs
that can be used for tracking. The system is validated on four videos of an
urban traffic dataset. Our method improves the recall and precision metrics for
the object detection task compared to the vanilla background subtraction method
and improves the CLEAR MOT metrics in the tracking tasks for most videos.
Geospatial distributions reflect rates of evolution of features of language
Authors: Henri Kauhanen, Deepthi Gopal, Tobias Galla, Ricardo Bermúdez-Otero
Date: 2018-01-29 00:00:00

Quantifying the speed of linguistic change is challenging due to the fact that the historical evolution of languages is sparsely documented. Consequently, traditional methods rely on phylogenetic reconstruction. In this paper, we propose a model-based approach to the problem through the analysis of language change as a stochastic process combining vertical descent, spatial interactions, and mutations in both dimensions. A notion of linguistic temperature emerges naturally from this analysis as a dimensionless measure of the propensity of a linguistic feature to undergo change. We demonstrate how temperatures of linguistic features can be inferred from their present-day geospatial distributions, without recourse to information about their phylogenies. Thus the evolutionary dynamics of language, operating across thousands of years, leaves a measurable geospatial signature. This signature licenses inferences about the historical evolution of languages even in the absence of longitudinal data.
Helping Crisis Responders Find the Informative Needle in the Tweet Haystack
Authors: Leon Derczynski, Kenny Meesters, Kalina Bontcheva, Diana Maynard
Date: 2018-01-29 00:00:00
Tasks: General Classification, Informativeness

Crisis responders are increasingly using social media, data and other digital
sources of information to build a situational understanding of a crisis
situation in order to design an effective response. However with the increased
availability of such data, the challenge of identifying relevant information
from it also increases. This paper presents a successful automatic approach to
handling this problem. Messages are filtered for informativeness based on a
definition of the concept drawn from prior research and crisis response
experts. Informative messages are tagged for actionable data -- for example,
people in need, threats to rescue efforts, changes in environment, and so on.
In all, eight categories of actionability are identified. The two components --
informativeness and actionability classification -- are packaged together as an
openly-available tool called Emina (Emergent Informativeness and
Actionability).
Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation
Authors: Motoya Ohnishi, Li Wang, Gennaro Notomista, Magnus Egerstedt
Date: 2018-01-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics. To extract the dynamic structure of the model, we use a sparse optimization technique. We use the learned model in combination with control barrier certificates which constrain policies (feedback controllers) in order to maintain safety, which refers to avoiding particular undesirable regions of the state space. Under certain conditions, recovery of safety in the sense of Lyapunov stability after violations of safety due to the nonstationarity is guaranteed. In addition, we reformulate an action-value function approximation to make any kernel-based nonlinear function estimation method applicable to our adaptive learning framework. Lastly, solutions to the barrier-certified policy optimization are guaranteed to be globally optimal, ensuring the greedy policy improvement under mild conditions. The resulting framework is validated via simulations of a quadrotor, which has previously been used under stationarity assumptions in the safe learnings literature, and is then tested on a real robot, the brushbot, whose dynamics is unknown, highly complex and nonstationary.