pwc-archive/papers-with-abstracts huggingface dataset:
Semi-supervised learning for structured regression on partially observed attributed graphs
Authors: Jelena Stojanovic, Milos Jovanovic, Djordje Gligorijevic, Zoran Obradovic
Date: 2018-03-28 00:00:00
Tasks: Imputation, Missing Labels, Missing Values, regression

Conditional probabilistic graphical models provide a powerful framework for
structured regression in spatio-temporal datasets with complex correlation
patterns. However, in real-life applications a large fraction of observations
is often missing, which can severely limit the representational power of these
models. In this paper we propose a Marginalized Gaussian Conditional Random
Fields (m-GCRF) structured regression model for dealing with missing labels in
partially observed temporal attributed graphs. This method is aimed at learning
with both labeled and unlabeled parts and effectively predicting future values
in a graph. The method is even capable of learning from nodes for which the
response variable is never observed in history, which poses problems for many
state-of-the-art models that can handle missing data. The proposed model is
characterized for various missingness mechanisms on 500 synthetic graphs. The
benefits of the new method are also demonstrated on a challenging application
for predicting precipitation based on partial observations of climate variables
in a temporal graph that spans the entire continental US. We also show that the
method can be useful for optimizing the costs of data collection in climate
applications via active reduction of the number of weather stations to
consider. In experiments on these real-world and synthetic datasets we show
that the proposed model is consistently more accurate than alternative
semi-supervised structured models, as well as models that either use imputation
to deal with missing values or simply ignore them altogether.
End-to-End Multi-Task Learning with Attention
Authors: Shikun Liu, Edward Johns, Andrew J. Davison
Date: 2018-03-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: image-classification, Multi-Task Learning

We propose a novel multi-task learning architecture, which allows learning of
task-specific feature-level attention. Our design, the Multi-Task Attention
Network (MTAN), consists of a single shared network containing a global feature
pool, together with a soft-attention module for each task. These modules allow
for learning of task-specific features from the global features, whilst
simultaneously allowing for features to be shared across different tasks. The
architecture can be trained end-to-end and can be built upon any feed-forward
neural network, is simple to implement, and is parameter efficient. We evaluate
our approach on a variety of datasets, across both image-to-image predictions
and image classification tasks. We show that our architecture is
state-of-the-art in multi-task learning compared to existing methods, and is
also less sensitive to various weighting schemes in the multi-task loss
function. Code is available at https://github.com/lorenmt/mtan.
Deep Attention Model for Triage of Emergency Department Patients
Authors: Djordje Gligorijevic, Jelena Stojanovic, Wayne Satz, Ivan Stojkovic, Kathrin Schreyer, Daniel Del Portal, Zoran Obradovic
Date: 2018-03-28 00:00:00
Tasks: Binary Classification, Decision Making, Deep Attention, General Classification, Multi-class Classification

Optimization of patient throughput and wait time in emergency departments
(ED) is an important task for hospital systems. For that reason, Emergency
Severity Index (ESI) system for patient triage was introduced to help guide
manual estimation of acuity levels, which is used by nurses to rank the
patients and organize hospital resources. However, despite improvements that it
brought to managing medical resources, such triage system greatly depends on
nurse's subjective judgment and is thus prone to human errors. Here, we propose
a novel deep model based on the word attention mechanism designed for
predicting a number of resources an ED patient would need. Our approach
incorporates routinely available continuous and nominal (structured) data with
medical text (unstructured) data, including patient's chief complaint, past
medical history, medication list, and nurse assessment collected for 338,500 ED
visits over three years in a large urban hospital. Using both structured and
unstructured data, the proposed approach achieves the AUC of $\sim 88\%$ for
the task of identifying resource intensive patients (binary classification),
and the accuracy of $\sim 44\%$ for predicting exact category of number of
resources (multi-class classification task), giving an estimated lift over
nurses' performance by 16\% in accuracy. Furthermore, the attention mechanism
of the proposed model provides interpretability by assigning attention scores
for nurses' notes which is crucial for decision making and implementation of
such approaches in the real systems working on human health.
Synergy: A HW/SW Framework for High Throughput CNNs on Embedded Heterogeneous SoC
Authors: Guanwen Zhong, Akshat Dubey, Tan Cheng, Tulika Mitra
Date: 2018-03-28 00:00:00

Convolutional Neural Networks (CNN) have been widely deployed in diverse
application domains. There has been significant progress in accelerating both
their training and inference using high-performance GPUs, FPGAs, and custom
ASICs for datacenter-scale environments. The recent proliferation of mobile and
IoT devices have necessitated real-time, energy-efficient deep neural network
inference on embedded-class, resource-constrained platforms. In this context,
we present {\em Synergy}, an automated, hardware-software co-designed,
pipelined, high-throughput CNN inference framework on embedded heterogeneous
system-on-chip (SoC) architectures (Xilinx Zynq). {\em Synergy} leverages,
through multi-threading, all the available on-chip resources, which includes
the dual-core ARM processor along with the FPGA and the NEON SIMD engines as
accelerators. Moreover, {\em Synergy} provides a unified abstraction of the
heterogeneous accelerators (FPGA and NEON) and can adapt to different network
configurations at runtime without changing the underlying hardware accelerator
architecture by balancing workload across accelerators through work-stealing.
{\em Synergy} achieves 7.3X speedup, averaged across seven CNN models, over a
well-optimized software-only solution. {\em Synergy} demonstrates substantially
better throughput and energy-efficiency compared to the contemporary CNN
implementations on the same SoC architecture.
Weakly-Supervised Action Segmentation with Iterative Soft Boundary Assignment
Authors: Li Ding, Chenliang Xu
Date: 2018-03-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Segmentation

In this work, we address the task of weakly-supervised human action
segmentation in long, untrimmed videos. Recent methods have relied on expensive
learning models, such as Recurrent Neural Networks (RNN) and Hidden Markov
Models (HMM). However, these methods suffer from expensive computational cost,
thus are unable to be deployed in large scale. To overcome the limitations, the
keys to our design are efficiency and scalability. We propose a novel action
modeling framework, which consists of a new temporal convolutional network,
named Temporal Convolutional Feature Pyramid Network (TCFPN), for predicting
frame-wise action labels, and a novel training strategy for weakly-supervised
sequence modeling, named Iterative Soft Boundary Assignment (ISBA), to align
action sequences and update the network in an iterative fashion. The proposed
framework is evaluated on two benchmark datasets, Breakfast and Hollywood
Extended, with four different evaluation metrics. Extensive experimental
results show that our methods achieve competitive or superior performance to
state-of-the-art methods.
Improving confidence while predicting trends in temporal disease networks
Authors: Djordje Gligorijevic, Jelena Stojanovic, Zoran Obradovic
Date: 2018-03-28 00:00:00
Tasks: Decision Making

For highly sensitive real-world predictive analytic applications such as
healthcare and medicine, having good prediction accuracy alone is often not
enough. These kinds of applications require a decision making process which
uses uncertainty estimation as input whenever possible. Quality of uncertainty
estimation is a subject of over or under confident prediction, which is often
not addressed in many models. In this paper we show several extensions to the
Gaussian Conditional Random Fields model, which aim to provide higher quality
uncertainty estimation. These extensions are applied to the temporal disease
graph built from the State Inpatient Database (SID) of California, acquired
from the HCUP. Our experiments demonstrate benefits of using graph information
in modeling temporal disease properties as well as improvements in uncertainty
estimation provided by given extensions of the Gaussian Conditional Random
Fields method.
Fraternal Dropout
Authors: Konrad Zolna, Devansh Arpit, Dendi Suhubdy, Yoshua Bengio
Date: 2017-10-31 00:00:00
Proceeding: ICLR 2018 1
Tasks: Image Captioning, Language Modeling, Language Modelling

Recurrent neural networks (RNNs) are important class of architectures among
neural networks useful for language modeling and sequential prediction.
However, optimizing RNNs is known to be harder compared to feed-forward neural
networks. A number of techniques have been proposed in literature to address
this problem. In this paper we propose a simple technique called fraternal
dropout that takes advantage of dropout to achieve this goal. Specifically, we
propose to train two identical copies of an RNN (that share parameters) with
different dropout masks while minimizing the difference between their
(pre-softmax) predictions. In this way our regularization encourages the
representations of RNNs to be invariant to dropout mask, thus being robust. We
show that our regularization term is upper bounded by the expectation-linear
dropout objective which has been shown to address the gap due to the difference
between the train and inference phases of dropout. We evaluate our model and
achieve state-of-the-art results in sequence modeling tasks on two benchmark
datasets - Penn Treebank and Wikitext-2. We also show that our approach leads
to performance improvement by a significant margin in image captioning
(Microsoft COCO) and semi-supervised (CIFAR-10) tasks.
Bayesian model and dimension reduction for uncertainty propagation: applications in random media
Authors: Constantin Grigo, Phaedon-Stelios Koutsourelakis
Date: 2017-11-07 00:00:00
Tasks: Dimensionality Reduction, Variational Inference

Well-established methods for the solution of stochastic partial differential
equations (SPDEs) typically struggle in problems with high-dimensional
inputs/outputs. Such difficulties are only amplified in large-scale
applications where even a few tens of full-order model runs are impracticable.
While dimensionality reduction can alleviate some of these issues, it is not
known which and how many features of the (high-dimensional) input are actually
predictive of the (high-dimensional) output. In this paper, we advocate a
Bayesian formulation that is capable of performing simultaneous dimension and
model-order reduction. It consists of a component that encodes the
high-dimensional input into a low-dimensional set of feature functions by
employing sparsity-enforcing priors and a decoding component that makes use of
the solution of a coarse-grained model in order to reconstruct that of the
full-order model. Both components are represented with latent variables in a
probabilistic graphical model and are simultaneously trained using Stochastic
Variational Inference methods. The model is capable of quantifying the
predictive uncertainty due to the information loss that unavoidably takes place
in any model-order/dimension reduction as well as the uncertainty arising from
finite-sized training datasets. We demonstrate its capabilities in the context
of random media where fine-scale fluctuations can give rise to random inputs
with tens of thousands of variables. With a few tens of full-order model
simulations, the proposed model is capable of identifying salient physical
features and produce sharp predictions under different boundary conditions of
the full output which itself consists of thousands of components.
Pose2Seg: Detection Free Human Instance Segmentation
Authors: Song-Hai Zhang, Rui-Long Li, Xin Dong, Paul L. Rosin, Zixi Cai, Han Xi, Dingcheng Yang, Hao-Zhi Huang, Shi-Min Hu
Date: 2018-03-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: 2D Human Pose Estimation, Human Instance Segmentation, Instance Segmentation, Keypoint Detection, object-detection, Object Detection, Pose-Based Human Instance Segmentation, Pose Estimation, Segmentation, Semantic Segmentation

The standard approach to image instance segmentation is to perform the object
detection first, and then segment the object from the detection bounding-box.
More recently, deep learning methods like Mask R-CNN perform them jointly.
However, little research takes into account the uniqueness of the "human"
category, which can be well defined by the pose skeleton. Moreover, the human
pose skeleton can be used to better distinguish instances with heavy occlusion
than using bounding-boxes. In this paper, we present a brand new pose-based
instance segmentation framework for humans which separates instances based on
human pose, rather than proposal region detection. We demonstrate that our
pose-based framework can achieve better accuracy than the state-of-art
detection-based approach on the human instance segmentation problem, and can
moreover better handle occlusion. Furthermore, there are few public datasets
containing many heavily occluded humans along with comprehensive annotations,
which makes this a challenging problem seldom noticed by researchers.
Therefore, in this paper we introduce a new benchmark "Occluded Human
(OCHuman)", which focuses on occluded humans with comprehensive annotations
including bounding-box, human pose and instance masks. This dataset contains
8110 detailed annotated human instances within 4731 images. With an average
0.67 MaxIoU for each person, OCHuman is the most complex and challenging
dataset related to human instance segmentation. Through this dataset, we want
to emphasize occlusion as a challenging problem for researchers to study.
Motion Guided LIDAR-camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles
Authors: Juan Castorena, Gint Puskorius, Gaurav Pandey
Date: 2018-03-28 00:00:00
Tasks: Autonomous Vehicles, Depth Estimation, Super-Resolution

This work proposes a novel motion guided method for target-less self-calibration of a LiDAR and camera and use the re-projection of LiDAR points onto the image reference frame for real-time depth upsampling. The calibration parameters are estimated by optimizing an objective function that penalizes distances between 2D and re-projected 3D motion vectors obtained from time-synchronized image and point cloud sequences. For upsampling, a simple, yet effective and time efficient formulation that minimizes depth gradients subject to an equality constraint involving the LiDAR measurements is proposed. Validation is performed on recorded real data from urban environments and demonstrations that our two methods are effective and suitable to mobile robotics and autonomous vehicle applications imposing real-time requirements is shown.
Learning for Disparity Estimation through Feature Constancy
Authors: Zhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, Jianfeng Zhang
Date: 2017-12-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Disparity Estimation, Stereo Matching, Stereo Matching Hand

Stereo matching algorithms usually consist of four steps, including matching
cost calculation, matching cost aggregation, disparity calculation, and
disparity refinement. Existing CNN-based methods only adopt CNN to solve parts
of the four steps, or use different networks to deal with different steps,
making them difficult to obtain the overall optimal solution. In this paper, we
propose a network architecture to incorporate all steps of stereo matching. The
network consists of three parts. The first part calculates the multi-scale
shared features. The second part performs matching cost calculation, matching
cost aggregation and disparity calculation to estimate the initial disparity
using shared features. The initial disparity and the shared features are used
to calculate the feature constancy that measures correctness of the
correspondence between two input images. The initial disparity and the feature
constancy are then fed to a sub-network to refine the initial disparity. The
proposed method has been evaluated on the Scene Flow and KITTI datasets. It
achieves the state-of-the-art performance on the KITTI 2012 and KITTI 2015
benchmarks while maintaining a very fast running time.
Learning Compositional Visual Concepts with Mutual Consistency
Authors: Yunye Gong, Srikrishna Karanam, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, Peter C. Doerschuk
Date: 2017-11-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Data Augmentation, Face Verification, Image Generation

Compositionality of semantic concepts in image synthesis and analysis is
appealing as it can help in decomposing known and generatively recomposing
unknown data. For instance, we may learn concepts of changing illumination,
geometry or albedo of a scene, and try to recombine them to generate physically
meaningful, but unseen data for training and testing. In practice however we
often do not have samples from the joint concept space available: We may have
data on illumination change in one data set and on geometric change in another
one without complete overlap. We pose the following question: How can we learn
two or more concepts jointly from different data sets with mutual consistency
where we do not have samples from the full joint space? We present a novel
answer in this paper based on cyclic consistency over multiple concepts,
represented individually by generative adversarial networks (GANs). Our method,
ConceptGAN, can be understood as a drop in for data augmentation to improve
resilience for real world applications. Qualitative and quantitative
evaluations demonstrate its efficacy in generating semantically meaningful
images, as well as one shot face verification as an example application.
MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting
Authors: Bernardo Pérez Orozco, Gabriele Abbati, Stephen Roberts
Date: 2018-03-26 00:00:00
Tasks: Astronomy, regression, Time Series, Time Series Analysis, Time Series Forecasting

Time series forecasting is ubiquitous in the modern world. Applications range
from health care to astronomy, and include climate modelling, financial trading
and monitoring of critical engineering equipment. To offer value over this
range of activities, models must not only provide accurate forecasts, but also
quantify and adjust their uncertainty over time. In this work, we directly
tackle this task with a novel, fully end-to-end deep learning method for time
series forecasting. By recasting time series forecasting as an ordinal
regression task, we develop a principled methodology to assess long-term
predictive uncertainty and describe rich multimodal, non-Gaussian behaviour,
which arises regularly in applied settings.
  Notably, our framework is a wholly general-purpose approach that requires
little to no user intervention to be used. We showcase this key feature in a
large-scale benchmark test with 45 datasets drawn from both, a wide range of
real-world application domains, as well as a comprehensive list of synthetic
maps. This wide comparison encompasses state-of-the-art methods in both the
Machine Learning and Statistics modelling literature, such as the Gaussian
Process. We find that our approach does not only provide excellent predictive
forecasts, shadowing true future values, but also allows us to infer valuable
information, such as the predictive distribution of the occurrence of critical
events of interest, accurately and reliably even over long time horizons.
Learning Intelligent Dialogs for Bounding Box Annotation
Authors: Ksenia Konyushkova, Jasper Uijlings, Christoph Lampert, Vittorio Ferrari
Date: 2017-12-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Reinforcement Learning

We introduce Intelligent Annotation Dialogs for bounding box annotation. We
train an agent to automatically choose a sequence of actions for a human
annotator to produce a bounding box in a minimal amount of time. Specifically,
we consider two actions: box verification, where the annotator verifies a box
generated by an object detector, and manual box drawing. We explore two kinds
of agents, one based on predicting the probability that a box will be
positively verified, and the other based on reinforcement learning. We
demonstrate that (1) our agents are able to learn efficient annotation
strategies in several scenarios, automatically adapting to the image
difficulty, the desired quality of the boxes, and the detector strength; (2) in
all scenarios the resulting annotation dialogs speed up annotation compared to
manual box drawing alone and box verification alone, while also outperforming
any fixed combination of verification and drawing in most scenarios; (3) in a
realistic scenario where the detector is iteratively re-trained, our agents
evolve a series of strategies that reflect the shifting trade-off between
verification and drawing as the detector grows stronger.
Smooth Neighbors on Teacher Graphs for Semi-supervised Learning
Authors: Yucen Luo, Jun Zhu, Mengxi Li, Yong Ren, Bo Zhang
Date: 2017-11-01 00:00:00
Proceeding: CVPR 2018 6

The recently proposed self-ensembling methods have achieved promising results
in deep semi-supervised learning, which penalize inconsistent predictions of
unlabeled data under different perturbations. However, they only consider
adding perturbations to each single data point, while ignoring the connections
between data samples. In this paper, we propose a novel method, called Smooth
Neighbors on Teacher Graphs (SNTG). In SNTG, a graph is constructed based on
the predictions of the teacher model, i.e., the implicit self-ensemble of
models. Then the graph serves as a similarity measure with respect to which the
representations of "similar" neighboring points are learned to be smooth on the
low-dimensional manifold. We achieve state-of-the-art results on
semi-supervised learning benchmarks. The error rates are 9.89%, 3.99% for
CIFAR-10 with 4000 labels, SVHN with 500 labels, respectively. In particular,
the improvements are significant when the labels are fewer. For the
non-augmented MNIST with only 20 labels, the error rate is reduced from
previous 4.81% to 1.36%. Our method also shows robustness to noisy labels.