pwc-archive/papers-with-abstracts huggingface dataset:
RGCNN: Regularized Graph CNN for Point Cloud Segmentation
Authors: Gusi Te, Wei Hu, Zongming Guo, Amin Zheng
Date: 2018-06-08 00:00:00
Tasks: Point Cloud Classification, Point Cloud Segmentation, Quantization

Point cloud, an efficient 3D object representation, has become popular with
the development of depth sensing and 3D laser scanning techniques. It has
attracted attention in various applications such as 3D tele-presence,
navigation for unmanned vehicles and heritage reconstruction. The understanding
of point clouds, such as point cloud segmentation, is crucial in exploiting the
informative value of point clouds for such applications. Due to the
irregularity of the data format, previous deep learning works often convert
point clouds to regular 3D voxel grids or collections of images before feeding
them into neural networks, which leads to voluminous data and quantization
artifacts. In this paper, we instead propose a regularized graph convolutional
neural network (RGCNN) that directly consumes point clouds. Leveraging on
spectral graph theory, we treat features of points in a point cloud as signals
on graph, and define the convolution over graph by Chebyshev polynomial
approximation. In particular, we update the graph Laplacian matrix that
describes the connectivity of features in each layer according to the
corresponding learned features, which adaptively captures the structure of
dynamic graphs. Further, we deploy a graph-signal smoothness prior in the loss
function, thus regularizing the learning process. Experimental results on the
ShapeNet part dataset show that the proposed approach significantly reduces the
computational complexity while achieving competitive performance with the state
of the art. Also, experiments show RGCNN is much more robust to both noise and
point cloud density in comparison with other methods. We further apply RGCNN to
point cloud classification and achieve competitive results on ModelNet40
dataset.
Programmatically Interpretable Reinforcement Learning
Authors: Abhinav Verma, Vijayaraghavan Murali, Rishabh Singh, Pushmeet Kohli, Swarat Chaudhuri
Date: 2018-04-06 00:00:00
Proceeding: ICML 2018 7
Tasks: Car Racing, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a reinforcement learning framework, called Programmatically
Interpretable Reinforcement Learning (PIRL), that is designed to generate
interpretable and verifiable agent policies. Unlike the popular Deep
Reinforcement Learning (DRL) paradigm, which represents policies by neural
networks, PIRL represents policies using a high-level, domain-specific
programming language. Such programmatic policies have the benefits of being
more easily interpreted than neural networks, and being amenable to
verification by symbolic methods. We propose a new method, called Neurally
Directed Program Search (NDPS), for solving the challenging nonsmooth
optimization problem of finding a programmatic policy with maximal reward. NDPS
works by first learning a neural policy network using DRL, and then performing
a local search over programmatic policies that seeks to minimize a distance
from this neural "oracle". We evaluate NDPS on the task of learning to drive a
simulated car in the TORCS car-racing environment. We demonstrate that NDPS is
able to discover human-readable policies that pass some significant performance
bars. We also show that PIRL policies can have smoother trajectories, and can
be more easily transferred to environments not encountered during training,
than corresponding policies discovered by DRL.
SupportNet: solving catastrophic forgetting in class incremental learning with support data
Authors: Yu Li, Zhongxiao Li, Lizhong Ding, Yijie Pan, Chao Huang, Yuhui Hu, Wei Chen, Xin Gao
Date: 2018-06-08 00:00:00
Tasks: class-incremental learning, Class Incremental Learning, Deep Learning, Incremental Learning

A plain well-trained deep learning model often does not have the ability to
learn new knowledge without forgetting the previously learned knowledge, which
is known as catastrophic forgetting. Here we propose a novel method,
SupportNet, to efficiently and effectively solve the catastrophic forgetting
problem in the class incremental learning scenario. SupportNet combines the
strength of deep learning and support vector machine (SVM), where SVM is used
to identify the support data from the old data, which are fed to the deep
learning model together with the new data for further training so that the
model can review the essential information of the old data when learning the
new information. Two powerful consolidation regularizers are applied to
stabilize the learned representation and ensure the robustness of the learned
model. We validate our method with comprehensive experiments on various tasks,
which show that SupportNet drastically outperforms the state-of-the-art
incremental learning methods and even reaches similar performance as the deep
learning model trained from scratch on both old and new data. Our program is
accessible at: https://github.com/lykaust15/SupportNet
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network
Authors: Qiangqiang Yuan, Qiang Zhang, Jie Li, Huanfeng Shen, Liangpei Zhang
Date: 2018-06-01 00:00:00
Tasks: Denoising, Hyperspectral Image Denoising, Image Denoising

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to
improve the performance of the subsequent HSI interpretation and applications.
In this paper, a novel deep learning-based method for this task is proposed, by
learning a non-linear end-to-end mapping between the noisy and clean HSIs with
a combined spatial-spectral deep convolutional neural network (HSID-CNN). Both
the spatial and spectral information are simultaneously assigned to the
proposed network. In addition, multi-scale feature extraction and multi-level
feature representation are respectively employed to capture both the
multi-scale spatial-spectral feature and fuse the feature representations with
different levels for the final restoration. The simulated and real-data
experiments demonstrate that the proposed HSID-CNN outperforms many of the
mainstream methods in both the quantitative evaluation indexes, visual effects,
and HSI classification accuracy.
Multi-Source Neural Machine Translation with Missing Data
Authors: Yuta Nishimura, Katsuhito Sudoh, Graham Neubig, Satoshi Nakamura
Date: 2018-06-07 00:00:00
Proceeding: WS 2018 7
Tasks: Machine Translation, NMT, Translation

Multi-source translation is an approach to exploit multiple inputs (e.g. in
two different languages) to increase translation accuracy. In this paper, we
examine approaches for multi-source neural machine translation (NMT) using an
incomplete multilingual corpus in which some translations are missing. In
practice, many multilingual corpora are not complete due to the difficulty to
provide translations in all of the relevant languages (for example, in TED
talks, most English talks only have subtitles for a small portion of the
languages that TED supports). Existing studies on multi-source translation did
not explicitly handle such situations. This study focuses on the use of
incomplete multilingual corpora in multi-encoder NMT and mixture of NMT experts
and examines a very simple implementation where missing source translations are
replaced by a special symbol <NULL>. These methods allow us to use incomplete
corpora both at training time and test time. In experiments with real
incomplete multilingual corpora of TED Talks, the multi-source NMT with the
<NULL> tokens achieved higher translation accuracies measured by BLEU than
those by any one-to-one NMT systems.
Causal effects based on distributional distances
Authors: Kwangho Kim, Jisu Kim, Edward H. Kennedy
Date: 2018-06-08 00:00:00
Tasks: counterfactual

Comparing counterfactual distributions can provide more nuanced and valuable measures for causal effects, going beyond typical summary statistics such as averages. In this work, we consider characterizing causal effects via distributional distances, focusing on two kinds of target parameters. The first is the counterfactual outcome density. We propose a doubly robust-style estimator for the counterfactual density and study its rates of convergence and limiting distributions. We analyze asymptotic upper bounds on the $L_q$ and the integrated $L_q$ risks of the proposed estimator, and propose a bootstrap-based confidence band. The second is a novel distributional causal effect defined by the $L_1$ distance between different counterfactual distributions. We study three approaches for estimating the proposed distributional effect: smoothing the counterfactual density, smoothing the $L_1$ distance, and imposing a margin condition. For each approach, we analyze asymptotic properties and error bounds of the proposed estimator, and discuss potential advantages and disadvantages. We go on to present a bootstrap approach for obtaining confidence intervals, and propose a test of no distributional effect. We conclude with a numerical illustration and a real-world example.
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
Authors: Ashwin Kalyan, Stefan Lee, Anitha Kannan, Dhruv Batra
Date: 2018-06-08 00:00:00
Proceeding: ICML 2018 7
Tasks: Diversity, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Question Generation, Question-Generation, Sentence, Structured Prediction

Many structured prediction problems (particularly in vision and language
domains) are ambiguous, with multiple outputs being correct for an input - e.g.
there are many ways of describing an image, multiple ways of translating a
sentence; however, exhaustively annotating the applicability of all possible
outputs is intractable due to exponentially large output spaces (e.g. all
English sentences). In practice, these problems are cast as multi-class
prediction, with the likelihood of only a sparse set of annotations being
maximized - unfortunately penalizing for placing beliefs on plausible but
unannotated outputs. We make and test the following hypothesis - for a given
input, the annotations of its neighbors may serve as an additional supervisory
signal. Specifically, we propose an objective that transfers supervision from
neighboring examples. We first study the properties of our developed method in
a controlled toy setup before reporting results on multi-label classification
and two image-grounded sequence modeling tasks - captioning and question
generation. We evaluate using standard task-specific metrics and measures of
output diversity, finding consistent improvements over standard maximum
likelihood training and other baselines.
Between hard and soft thresholding: optimal iterative thresholding algorithms
Authors: Haoyang Liu, Rina Foygel Barber
Date: 2018-04-24 00:00:00

Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective, and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator, and asks whether it is possible to achieve stronger guarantees than what is possible with hard thresholding. We develop the notion of relative concavity of a thresholding operator, a quantity that characterizes the worst-case convergence performance of any thresholding operator on the target optimization problem. Surprisingly, we find that commonly used thresholding operators, such as hard thresholding and soft thresholding, are suboptimal in terms of worst-case convergence guarantees. Instead, a general class of thresholding operators, lying between hard thresholding and soft thresholding, is shown to be optimal with the strongest possible convergence guarantee among all thresholding operators. Examples of this general class includes $\ell_q$ thresholding with appropriate choices of $q$, and a newly defined {\em reciprocal thresholding} operator. We also investigate the implications of the improved optimization guarantee in the statistical setting of sparse linear regression, and show that this new class of thresholding operators attain the optimal rate for computationally efficient estimators, matching the Lasso.
Program Synthesis Through Reinforcement Learning Guided Tree Search
Authors: Riley Simmons-Edler, Anders Miltner, Sebastian Seung
Date: 2018-06-08 00:00:00
Tasks: Program Synthesis, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Program Synthesis is the task of generating a program from a provided
specification. Traditionally, this has been treated as a search problem by the
programming languages (PL) community and more recently as a supervised learning
problem by the machine learning community. Here, we propose a third approach,
representing the task of synthesizing a given program as a Markov decision
process solvable via reinforcement learning(RL). From observations about the
states of partial programs, we attempt to find a program that is optimal over a
provided reward metric on pairs of programs and states. We instantiate this
approach on a subset of the RISC-V assembly language operating on floating
point numbers, and as an optimization inspired by search-based techniques from
the PL community, we combine RL with a priority search tree. We evaluate this
instantiation and demonstrate the effectiveness of our combined method compared
to a variety of baselines, including a pure RL ablation and a state of the art
Markov chain Monte Carlo search method on this task.
Reference Model of Multi-Entity Bayesian Networks for Predictive Situation Awareness
Authors: Cheol Young Park, Kathryn Blackmond Laskey
Date: 2018-06-06 00:00:00

During the past quarter-century, situation awareness (SAW) has become a
critical research theme, because of its importance. Since the concept of SAW
was first introduced during World War I, various versions of SAW have been
researched and introduced. Predictive Situation Awareness (PSAW) focuses on the
ability to predict aspects of a temporally evolving situation over time. PSAW
requires a formal representation and a reasoning method using such a
representation. A Multi-Entity Bayesian Network (MEBN) is a knowledge
representation formalism combining Bayesian Networks (BN) with First-Order
Logic (FOL). MEBN can be used to represent uncertain situations (supported by
BN) as well as complex situations (supported by FOL). Also, efficient reasoning
algorithms for MEBN have been developed. MEBN can be a formal representation to
support PSAW and has been used for several PSAW systems. Although several MEBN
applications for PSAW exist, very little work can be found in the literature
that attempts to generalize a MEBN model to support PSAW. In this research, we
define a reference model for MEBN in PSAW, called a PSAW-MEBN reference model.
The PSAW-MEBN reference model enables us to easily develop a MEBN model for
PSAW by supporting the design of a MEBN model for PSAW. In this research, we
introduce two example use cases using the PSAW-MEBN reference model to develop
MEBN models to support PSAW: a Smart Manufacturing System and a Maritime Domain
Awareness System.
MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model
Authors: Cheol Young Park, Kathryn Blackmond Laskey
Date: 2018-06-06 00:00:00

Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism
combining Bayesian Networks (BN) with First-Order Logic (FOL). MEBN has
sufficient expressive power for general-purpose knowledge representation and
reasoning. Developing a MEBN model to support a given application is a
challenge, requiring definition of entities, relationships, random variables,
conditional dependence relationships, and probability distributions. When
available, data can be invaluable both to improve performance and to streamline
development. By far the most common format for available data is the relational
database (RDB). Relational databases describe and organize data according to
the Relational Model (RM). Developing a MEBN model from data stored in an RDB
therefore requires mapping between the two formalisms. This paper presents
MEBN-RM, a set of mapping rules between key elements of MEBN and RM. We
identify links between the two languages (RM and MEBN) and define four levels
of mapping from elements of RM to elements of MEBN. These definitions are
implemented in the MEBN-RM algorithm, which converts a relational schema in RM
to a partial MEBN model. Through this research, the software has been released
as a MEBN-RM open-source software tool. The method is illustrated through two
example use cases using MEBN-RM to develop MEBN models: a Critical
Infrastructure Defense System and a Smart Manufacturing System.
Robust and Scalable Models of Microbiome Dynamics
Authors: Travis E. Gibson, Georg K. Gerber
Date: 2018-05-11 00:00:00
Proceeding: ICML 2018
Tasks: Time Series Analysis

Microbes are everywhere, including in and on our bodies, and have been shown
to play key roles in a variety of prevalent human diseases. Consequently, there
has been intense interest in the design of bacteriotherapies or "bugs as
drugs," which are communities of bacteria administered to patients for specific
therapeutic applications. Central to the design of such therapeutics is an
understanding of the causal microbial interaction network and the population
dynamics of the organisms. In this work we present a Bayesian nonparametric
model and associated efficient inference algorithm that addresses the key
conceptual and practical challenges of learning microbial dynamics from time
series microbe abundance data. These challenges include high-dimensional (300+
strains of bacteria in the gut) but temporally sparse and non-uniformly sampled
data; high measurement noise; and, nonlinear and physically non-negative
dynamics. Our contributions include a new type of dynamical systems model for
microbial dynamics based on what we term interaction modules, or learned
clusters of latent variables with redundant interaction structure (reducing the
expected number of interaction coefficients from $O(n^2)$ to $O((\log n)^2)$);
a fully Bayesian formulation of the stochastic dynamical systems model that
propagates measurement and latent state uncertainty throughout the model; and
introduction of a temporally varying auxiliary variable technique to enable
efficient inference by relaxing the hard non-negativity constraint on states.
We apply our method to simulated and real data, and demonstrate the utility of
our technique for system identification from limited data and gaining new
biological insights into bacteriotherapy design.
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Authors: Shuai Zheng, James T. Kwok
Date: 2018-06-08 00:00:00
Proceeding: ICML 2018 7
Tasks: Data Augmentation, Stochastic Optimization

Variance reduction has been commonly used in stochastic optimization. It
relies crucially on the assumption that the data set is finite. However, when
the data are imputed with random noise as in data augmentation, the perturbed
data set be- comes essentially infinite. Recently, the stochastic MISO (S-MISO)
algorithm is introduced to address this expected risk minimization problem.
Though it converges faster than SGD, a significant amount of memory is
required. In this pa- per, we propose two SGD-like algorithms for expected risk
minimization with random perturbation, namely, stochastic sample average
gradient (SSAG) and stochastic SAGA (S-SAGA). The memory cost of SSAG does not
depend on the sample size, while that of S-SAGA is the same as those of
variance reduction methods on un- perturbed data. Theoretical analysis and
experimental results on logistic regression and AUC maximization show that SSAG
has faster convergence rate than SGD with comparable space requirement, while
S-SAGA outperforms S-MISO in terms of both iteration complexity and storage.
The Language of Generalization
Authors: Michael Henry Tessler, Noah D. Goodman
Date: 2016-08-09 00:00:00

Language provides simple ways of communicating generalizable knowledge to
each other (e.g., "Birds fly", "John hikes", "Fire makes smoke"). Though found
in every language and emerging early in development, the language of
generalization is philosophically puzzling and has resisted precise
formalization. Here, we propose the first formal account of generalizations
conveyed with language that makes quantitative predictions about human
understanding. We test our model in three diverse domains: generalizations
about categories (generic language), events (habitual language), and causes
(causal language). The model explains the gradience in human endorsement
through the interplay between a simple truth-conditional semantic theory and
diverse beliefs about properties, formalized in a probabilistic model of
language understanding. This work opens the door to understanding precisely how
abstract knowledge is learned from language.
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Authors: Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang, Guy Van Den Broeck
Date: 2017-11-29 00:00:00
Proceeding: ICML 2018 7
Tasks: Deep Learning, General Classification, Multi-class Classification

This paper develops a novel methodology for using symbolic knowledge in deep
learning. From first principles, we derive a semantic loss function that
bridges between neural output vectors and logical constraints. This loss
function captures how close the neural network is to satisfying the constraints
on its output. An experimental evaluation shows that it effectively guides the
learner to achieve (near-)state-of-the-art results on semi-supervised
multi-class classification. Moreover, it significantly increases the ability of
the neural network to predict structured objects, such as rankings and paths.
These discrete concepts are tremendously difficult to learn, and benefit from a
tight integration of deep learning and symbolic reasoning methods.
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Authors: Patrick Schwab, Emanuela Keller, Carl Muroi, David J. Mack, Christian Strässle, Walter Karlen
Date: 2018-02-14 00:00:00
Proceeding: ICML 2018 7
Tasks: Time Series, Time Series Analysis

Patients in the intensive care unit (ICU) require constant and close
supervision. To assist clinical staff in this task, hospitals use monitoring
systems that trigger audiovisual alarms if their algorithms indicate that a
patient's condition may be worsening. However, current monitoring systems are
extremely sensitive to movement artefacts and technical errors. As a result,
they typically trigger hundreds to thousands of false alarms per patient per
day - drowning the important alarms in noise and adding to the exhaustion of
clinical staff. In this setting, data is abundantly available, but obtaining
trustworthy annotations by experts is laborious and expensive. We frame the
problem of false alarm reduction from multivariate time series as a
machine-learning task and address it with a novel multitask network
architecture that utilises distant supervision through multiple related
auxiliary tasks in order to reduce the number of expensive labels required for
training. We show that our approach leads to significant improvements over
several state-of-the-art baselines on real-world ICU data and provide new
insights on the importance of task selection and architectural choices in
distantly supervised multitask learning.