pwc-archive/papers-with-abstracts huggingface dataset:
Parsing R-CNN for Instance-Level Human Analysis
Authors: Lu Yang, Qing Song, Zhihui Wang, Ming Jiang
Date: 2018-11-30 00:00:00
Proceeding: CVPR 2019 6
Tasks: Human Parsing, Human Part Segmentation, Multi-Human Parsing, Pose Estimation

Instance-level human analysis is common in real-life scenarios and has
multiple manifestations, such as human part segmentation, dense pose
estimation, human-object interactions, etc. Models need to distinguish
different human instances in the image panel and learn rich features to
represent the details of each instance. In this paper, we present an end-to-end
pipeline for solving the instance-level human analysis, named Parsing R-CNN. It
processes a set of human instances simultaneously through comprehensive
considering the characteristics of region-based approach and the appearance of
a human, thus allowing representing the details of instances. Parsing R-CNN is
very flexible and efficient, which is applicable to many issues in human
instance analysis. Our approach outperforms all state-of-the-art methods on
CIHP (Crowd Instance-level Human Parsing), MHP v2.0 (Multi-Human Parsing) and
DensePose-COCO datasets. Based on the proposed Parsing R-CNN, we reach the 1st
place in the COCO 2018 Challenge DensePose Estimation task. Code and models are
public available.
Active Learning in Recommendation Systems with Multi-level User Preferences
Authors: Yuheng Bu, Kevin Small
Date: 2018-11-30 00:00:00
Tasks: Active Learning, Recommendation Systems

While recommendation systems generally observe user behavior passively, there
has been an increased interest in directly querying users to learn their
specific preferences. In such settings, considering queries at different levels
of granularity to optimize user information acquisition is crucial to
efficiently providing a good user experience. In this work, we study the active
learning problem with multi-level user preferences within the collective matrix
factorization (CMF) framework. CMF jointly captures multi-level user
preferences with respect to items and relations between items (e.g., book
genre, cuisine type), generally resulting in improved predictions. Motivated by
finite-sample analysis of the CMF model, we propose a theoretically optimal
active learning strategy based on the Fisher information matrix and use this to
derive a realizable approximation algorithm for practical recommendations.
Experiments are conducted using both the Yelp dataset directly and an
illustrative synthetic dataset in the three settings of personalized active
learning, cold-start recommendations, and noisy data -- demonstrating strong
improvements over several widely used active learning methods.
Time Aggregation and Model Interpretation for Deep Multivariate Longitudinal Patient Outcome Forecasting Systems in Chronic Ambulatory Care
Authors: Beau Norgeot, Dmytro Lituiev, Benjamin S. Glicksberg, Atul J. Butte
Date: 2018-11-30 00:00:00

Clinical data for ambulatory care, which accounts for 90% of the nations
healthcare spending, is characterized by relatively small sample sizes of
longitudinal data, unequal spacing between visits for each patient, with
unequal numbers of data points collected across patients. While deep learning
has become state-of-the-art for sequence modeling, it is unknown which methods
of time aggregation may be best suited for these challenging temporal use
cases. Additionally, deep models are often considered uninterpretable by
physicians which may prevent the clinical adoption, even of well performing
models. We show that time-distributed-dense layers combined with GRUs produce
the most generalizable models. Furthermore, we provide a framework for the
clinical interpretation of the models.
Restricted Boltzmann Machine with Multivalued Hidden Variables: a model suppressing over-fitting
Authors: Yuuki Yokoyama, Tomu Katsumata, Muneki Yasuda
Date: 2018-11-30 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Generalization is one of the most important issues in machine learning problems. In this study, we consider generalization in restricted Boltzmann machines (RBMs). We propose an RBM with multivalued hidden variables, which is a simple extension of conventional RBMs. We demonstrate that the proposed model is better than the conventional model via numerical experiments for contrastive divergence learning with artificial data and a classification problem with MNIST.
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
Authors: Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi
Date: 2018-11-30 00:00:00
Tasks: BIG-bench Machine Learning, Mortality Prediction

Machine learning for healthcare often trains models on de-identified datasets
with randomly-shifted calendar dates, ignoring the fact that data were
generated under hospital operation practices that change over time. These
changing practices induce definitive changes in observed data which confound
evaluations which do not account for dates and limit the generalisability of
date-agnostic models. In this work, we establish the magnitude of this problem
on MIMIC, a public hospital dataset, and showcase a simple solution. We augment
MIMIC with the year in which care was provided and show that a model trained
using standard feature representations will significantly degrade in quality
over time. We find a deterioration of 0.3 AUC when evaluating mortality
prediction on data from 10 years later. We find a similar deterioration of 0.15
AUC for length-of-stay. In contrast, we demonstrate that clinically-oriented
aggregates of raw features significantly mitigate future deterioration. Our
suggested aggregated representations, when retrained yearly, have prediction
quality comparable to year-agnostic models.
Are All Training Examples Created Equal? An Empirical Study
Authors: Kailas Vodrahalli, Ke Li, Jitendra Malik
Date: 2018-11-30 00:00:00
Tasks: Active Learning, All, Diversity

Modern computer vision algorithms often rely on very large training datasets.
However, it is conceivable that a carefully selected subsample of the dataset
is sufficient for training. In this paper, we propose a gradient-based
importance measure that we use to empirically analyze relative importance of
training images in four datasets of varying complexity. We find that in some
cases, a small subsample is indeed sufficient for training. For other datasets,
however, the relative differences in importance are negligible. These results
have important implications for active learning on deep networks. Additionally,
our analysis method can be used as a general tool to better understand
diversity of training examples in datasets.
Eigenvalue Corrected Noisy Natural Gradient
Authors: Juhan Bae, Guodong Zhang, Roger Grosse
Date: 2018-11-30 00:00:00

Variational Bayesian neural networks combine the flexibility of deep learning
with Bayesian uncertainty estimation. However, inference procedures for
flexible variational posteriors are computationally expensive. A recently
proposed method, noisy natural gradient, is a surprisingly simple method to fit
expressive posteriors by adding weight noise to regular natural gradient
updates. Noisy K-FAC is an instance of noisy natural gradient that fits a
matrix-variate Gaussian posterior with minor changes to ordinary K-FAC.
Nevertheless, a matrix-variate Gaussian posterior does not capture an accurate
diagonal variance. In this work, we extend on noisy K-FAC to obtain a more
flexible posterior distribution called eigenvalue corrected matrix-variate
Gaussian. The proposed method computes the full diagonal re-scaling factor in
Kronecker-factored eigenbasis. Empirically, our approach consistently
outperforms existing algorithms (e.g., noisy K-FAC) on regression and
classification tasks.
Deep Multimodal Learning: An Effective Method for Video Classification
Authors: Tianqi Zhao
Date: 2018-11-30 00:00:00
Tasks: Classification, General Classification, Language Modeling, Language Modelling, Time Series, Time Series Analysis, Video Classification

Videos have become ubiquitous on the Internet. And video analysis can provide
lots of information for detecting and recognizing objects as well as help
people understand human actions and interactions with the real world. However,
facing data as huge as TB level, effective methods should be applied. Recurrent
neural network (RNN) architecture has wildly been used on many sequential
learning problems such as Language Model, Time-Series Analysis, etc. In this
paper, we propose some variations of RNN such as stacked bidirectional LSTM/GRU
network with attention mechanism to categorize large-scale video data. We also
explore different multimodal fusion methods. Our model combines both visual and
audio information on both video and frame level and received great result.
Ensemble methods are also applied. Because of its multimodal characteristics,
we decide to call this method Deep Multimodal Learning(DML). Our DML-based
model was trained on Google Cloud and our own server and was tested in a
well-known video classification competition on Kaggle held by Google.
An Introduction to Deep Reinforcement Learning
Authors: Vincent Francois-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau
Date: 2018-11-30 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning is the combination of reinforcement learning (RL)
and deep learning. This field of research has been able to solve a wide range
of complex decision-making tasks that were previously out of reach for a
machine. Thus, deep RL opens up many new applications in domains such as
healthcare, robotics, smart grids, finance, and many more. This manuscript
provides an introduction to deep reinforcement learning models, algorithms and
techniques. Particular focus is on the aspects related to generalization and
how deep RL can be used for practical applications. We assume the reader is
familiar with basic machine learning concepts.
Deep Multi-Agent Reinforcement Learning with Relevance Graphs
Authors: Aleksandra Malysheva, Tegg Taekyong Sung, Chae-Bong Sohn, Daniel Kudenko, Aleksei Shpilman
Date: 2018-11-30 00:00:00
Tasks: Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Over recent years, deep reinforcement learning has shown strong successes in
complex single-agent tasks, and more recently this approach has also been
applied to multi-agent domains. In this paper, we propose a novel approach,
called MAGnet, to multi-agent reinforcement learning (MARL) that utilizes a
relevance graph representation of the environment obtained by a self-attention
mechanism, and a message-generation technique inspired by the NerveNet
architecture. We applied our MAGnet approach to the Pommerman game and the
results show that it significantly outperforms state-of-the-art MARL solutions,
including DQN, MADDPG, and MCTS.
How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology
Authors: Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey, Peter Krafft, Esteban Moro, Alex `Sandy' Pentland
Date: 2018-11-30 00:00:00
Tasks: BIG-bench Machine Learning, Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

In this empirical paper, we investigate how learning agents can be arranged
in more efficient communication topologies for improved learning. This is an
important problem because a common technique to improve speed and robustness of
learning in deep reinforcement learning and many other machine learning
algorithms is to run multiple learning agents in parallel. The standard
communication architecture typically involves all agents intermittently
communicating with each other (fully connected topology) or with a centralized
server (star topology). Unfortunately, optimizing the topology of communication
over the space of all possible graphs is a hard problem, so we borrow results
from the networked optimization and collective intelligence literatures which
suggest that certain families of network topologies can lead to strong
improvements over fully-connected networks. We start by introducing alternative
network topologies to DRL benchmark tasks under the Evolution Strategies
paradigm which we call Network Evolution Strategies. We explore the relative
performance of the four main graph families and observe that one such family
(Erdos-Renyi random graphs) empirically outperforms all other families,
including the de facto fully-connected communication topologies. Additionally,
the use of alternative network topologies has a multiplicative performance
effect: we observe that when 1000 learning agents are arranged in a carefully
designed communication topology, they can compete with 3000 agents arranged in
the de facto fully-connected topology. Overall, our work suggests that
distributed machine learning algorithms would learn more efficiently if the
communication topology between learning agents was optimized.
A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation
Authors: Yao Cui, Zhehan Yi, Jiajun Duan, Di Shi, Zhiwei Wang
Date: 2018-11-29 00:00:00
Tasks: Point Tracking

This paper proposes a resilient-backpropagation-neural-network-(Rprop-NN)
based algorithm for Photovoltaic (PV) maximum power point tracking (MPPT). A
supervision mechanism is proposed to calibrate the Rprop-NN-MPPT reference and
limit short-circuit current caused by incorrect prediction. Conventional MPPT
algorithms (e.g., perturb and observe (P&O), hill climbing, and incremental
conductance (Inc-Cond) etc.) are trial-and-error-based, which may result in
steady-state oscillations and loss of tracking direction under fast-changing
ambient environment. In addition, partial shading is also a challenge due to
the difficulty of finding the global maximum power point on a multi-peak
characteristic curve. As an attempt to address the aforementioned issues, a
novel Rprop-NN MPPT algorithm is developed and elaborated in this work.
Multiple case studies are carried out to verify the effectiveness of the
proposed algorithm.
A Neural-Network-Based Optimal Control of Ultra-Capacitors with System Uncertainties
Authors: Jiajun Duan, Zhehan Yi, Di Shi, Hao Xu, Zhiwei Wang
Date: 2018-11-29 00:00:00
Tasks: Point Tracking

In this paper, a neural-network (NN)-based online optimal control method
(NN-OPT) is proposed for ultra-capacitors (UCs) energy storage system (ESS) in
hybrid AC/DC microgrids involving multiple distributed generations (e.g.,
Photovoltaic (PV) system, battery storage, diesel generator). Conventional
control strategies usually produce large disturbances to buses during charging
and discharging (C&D) processes of UCs, which significantly degrades the power
quality and system performance, especially under fast C&D modes. Therefore, the
optimal control theory is adopted to optimize the C&D profile as well as to
suppress the disturbances caused by UCs implementation. Specifically, an
NN-based intelligent algorithm is developed to learn the optimal control policy
for bidirectional-converter-interfaced UCs. The inaccuracies of system modeling
are also considered in the control design. Since the designed NN-OPT method is
decentralized that only requires the local measurements, plug & play of UCs can
be easily realized with minimal communication efforts. In addition, the PV
system is under the maximum power point tracking (MPPT) control to extract the
maximum benefit. Both islanded and grid-tied modes are considered during the
controller design. Extensive case studies have been conducted to evaluate the
effectiveness of the proposed method.
The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning
Authors: Jiaming Zeng, Adam Lesnikowski, Jose M. Alvarez
Date: 2018-11-29 00:00:00
Tasks: Active Learning, Deep Learning, Position

One of the main challenges of deep learning tools is their inability to
capture model uncertainty. While Bayesian deep learning can be used to tackle
the problem, Bayesian neural networks often require more time and computational
power to train than deterministic networks. Our work explores whether fully
Bayesian networks are needed to successfully capture model uncertainty. We vary
the number and position of Bayesian layers in a network and compare their
performance on active learning with the MNIST dataset. We found that we can
fully capture the model uncertainty by using only a few Bayesian layers near
the output of the network, combining the advantages of deterministic and
Bayesian networks.
Learning Finite State Representations of Recurrent Policy Networks
Authors: Anurag Koul, Sam Greydanus, Alan Fern
Date: 2018-11-29 00:00:00
Proceeding: ICLR 2019 5
Tasks: Atari Games, Imitation Learning

Recurrent neural networks (RNNs) are an effective representation of control
policies for a wide range of reinforcement and imitation learning problems. RNN
policies, however, are particularly difficult to explain, understand, and
analyze due to their use of continuous-valued memory vectors and observation
features. In this paper, we introduce a new technique, Quantized Bottleneck
Insertion, to learn finite representations of these vectors and features. The
result is a quantized representation of the RNN that can be analyzed to improve
our understanding of memory use and general behavior. We present results of
this approach on synthetic environments and six Atari games. The resulting
finite representations are surprisingly small in some cases, using as few as 3
discrete memory states and 10 observations for a perfect Pong policy. We also
show that these finite policy representations lead to improved
interpretability.
Learning to Skip Ineffectual Recurrent Computations in LSTMs
Authors: Arash Ardakani, Zhengyun Ji, Warren J. Gross
Date: 2018-11-09 00:00:00

Long Short-Term Memory (LSTM) is a special class of recurrent neural network,
which has shown remarkable successes in processing sequential data. The typical
architecture of an LSTM involves a set of states and gates: the states retain
information over arbitrary time intervals and the gates regulate the flow of
information. Due to the recursive nature of LSTMs, they are computationally
intensive to deploy on edge devices with limited hardware resources. To reduce
the computational complexity of LSTMs, we first introduce a method that learns
to retain only the important information in the states by pruning redundant
information. We then show that our method can prune over 90% of information in
the states without incurring any accuracy degradation over a set of temporal
tasks. This observation suggests that a large fraction of the recurrent
computations are ineffectual and can be avoided to speed up the process during
the inference as they involve noncontributory multiplications/accumulations
with zero-valued states. Finally, we introduce a custom hardware accelerator
that can perform the recurrent computations using both sparse and dense states.
Experimental measurements show that performing the computations using the
sparse states speeds up the process and improves energy efficiency by up to
5.2x when compared to implementation results of the accelerator performing the
computations using dense states.
Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale
Authors: Eli Sherman, Hitinder Gurm, Ulysses Balis, Scott Owens, Jenna Wiens
Date: 2018-11-29 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

In healthcare, patient risk stratification models are often learned using
time-series data extracted from electronic health records. When extracting data
for a clinical prediction task, several formulations exist, depending on how
one chooses the time of prediction and the prediction horizon. In this paper,
we show how the formulation can greatly impact both model performance and
clinical utility. Leveraging a publicly available ICU dataset, we consider two
clinical prediction tasks: in-hospital mortality, and hypokalemia. Through
these case studies, we demonstrate the necessity of evaluating models using an
outcome-independent reference point, since choosing the time of prediction
relative to the event can result in unrealistic performance. Further, an
outcome-independent scheme outperforms an outcome-dependent scheme on both
tasks (In-Hospital Mortality AUROC .882 vs. .831; Serum Potassium: AUROC .829
vs. .740) when evaluated on test sets that mimic real-world use.
Regression and Classification by Zonal Kriging
Authors: Jean Serra, Jesus Angulo, B Ravi Kiran
Date: 2018-11-29 00:00:00
Tasks: Classification, General Classification, regression

Consider a family $Z=\{\boldsymbol{x_{i}},y_{i}$,$1\leq i\leq N\}$ of $N$
pairs of vectors $\boldsymbol{x_{i}} \in \mathbb{R}^d$ and scalars $y_{i}$ that
we aim to predict for a new sample vector $\mathbf{x}_0$. Kriging models $y$ as
a sum of a deterministic function $m$, a drift which depends on the point
$\boldsymbol{x}$, and a random function $z$ with zero mean. The zonality
hypothesis interprets $y$ as a weighted sum of $d$ random functions of a single
independent variables, each of which is a kriging, with a quadratic form for
the variograms drift. We can therefore construct an unbiased estimator
$y^{*}(\boldsymbol{x_{0}})=\sum_{i}\lambda^{i}z(\boldsymbol{x_{i}})$ de
$y(\boldsymbol{x_{0}})$ with minimal variance
$E[y^{*}(\boldsymbol{x_{0}})-y(\boldsymbol{x_{0}})]^{2}$, with the help of the
known training set points. We give the explicitly closed form for $\lambda^{i}$
without having calculated the inverse of the matrices.
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
Authors: Yingda Xia, Fengze Liu, Dong Yang, Jinzheng Cai, Lequan Yu, Zhuotun Zhu, Daguang Xu, Alan Yuille, Holger Roth
Date: 2018-11-29 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Pseudo Label, Segmentation, Semantic Segmentation, Tumor Segmentation

While making a tremendous impact in various fields, deep neural networks usually require large amounts of labeled data for training which are expensive to collect in many applications, especially in the medical domain. Unlabeled data, on the other hand, is much more abundant. Semi-supervised learning techniques, such as co-training, could provide a powerful tool to leverage unlabeled data. In this paper, we propose a novel framework, uncertainty-aware multi-view co-training (UMCT), to address semi-supervised learning on 3D data, such as volumetric data from medical imaging. In our work, co-training is achieved by exploiting multi-viewpoint consistency of 3D data. We generate different views by rotating or permuting the 3D data and utilize asymmetrical 3D kernels to encourage diversified features in different sub-networks. In addition, we propose an uncertainty-weighted label fusion mechanism to estimate the reliability of each view's prediction with Bayesian deep learning. As one view requires the supervision from other views in co-training, our self-adaptive approach computes a confidence score for the prediction of each unlabeled sample in order to assign a reliable pseudo label. Thus, our approach can take advantage of unlabeled data during training. We show the effectiveness of our proposed semi-supervised method on several public datasets from medical image segmentation tasks (NIH pancreas & LiTS liver tumor dataset). Meanwhile, a fully-supervised method based on our approach achieved state-of-the-art performances on both the LiTS liver tumor segmentation and the Medical Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value of our framework, even when fully supervised training is feasible.