pwc-archive/papers-with-abstracts huggingface dataset:
Fast Policy Learning through Imitation and Reinforcement
Authors: Ching-An Cheng, Xinyan Yan, Nolan Wagener, Byron Boots
Date: 2018-05-26 00:00:00
Tasks: Imitation Learning, Reinforcement Learning, Reinforcement Learning (RL)

Imitation learning (IL) consists of a set of tools that leverage expert
demonstrations to quickly learn policies. However, if the expert is suboptimal,
IL can yield policies with inferior performance compared to reinforcement
learning (RL). In this paper, we aim to provide an algorithm that combines the
best aspects of RL and IL. We accomplish this by formulating several popular RL
and IL algorithms in a common mirror descent framework, showing that these
algorithms can be viewed as a variation on a single approach. We then propose
LOKI, a strategy for policy learning that first performs a small but random
number of IL iterations before switching to a policy gradient RL method. We
show that if the switching time is properly randomized, LOKI can learn to
outperform a suboptimal expert and converge faster than running policy gradient
from scratch. Finally, we evaluate the performance of LOKI experimentally in
several simulated environments.
3D Shape Segmentation via Shape Fully Convolutional Networks
Authors: Pengyu Wang, Yuan Gan, Panpan Shui, Fenggen Yu, Yan Zhang, Songle Chen, Zhengxing Sun
Date: 2017-02-28 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We desgin a novel fully convolutional network architecture for shapes,
denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented
as graph structures in the SFCN architecture, based on novel graph convolution
and pooling operations, which are similar to convolution and pooling operations
used on images. Meanwhile, to build our SFCN architecture in the original image
segmentation fully convolutional network (FCN) architecture, we also design and
implement a generating operation} with bridging function. This ensures that the
convolution and pooling operation we have designed can be successfully applied
in the original FCN architecture. In this paper, we also present a new shape
segmentation approach based on SFCN. Furthermore, we allow more general and
challenging input, such as mixed datasets of different categories of shapes}
which can prove the ability of our generalisation. In our approach, SFCNs are
trained triangles-to-triangles by using three low-level geometric features as
input. Finally, the feature voting-based multi-label graph cuts is adopted to
optimise the segmentation results obtained by SFCN prediction. The experiment
results show that our method can effectively learn and predict mixed shape
datasets of either similar or different characteristics, and achieve excellent
segmentation results.
The Singular Values of Convolutional Layers
Authors: Hanie Sedghi, Vineet Gupta, Philip M. Long
Date: 2018-05-26 00:00:00
Proceeding: ICLR 2019 5

We characterize the singular values of the linear transformation associated
with a standard 2D multi-channel convolutional layer, enabling their efficient
computation. This characterization also leads to an algorithm for projecting a
convolutional layer onto an operator-norm ball. We show that this is an
effective regularizer; for example, it improves the test error of a deep
residual network using batch normalization on CIFAR-10 from 6.2\% to 5.3\%.
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
Authors: Neal Jean, Sang Michael Xie, Stefano Ermon
Date: 2018-05-26 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Gaussian Processes, regression, Representation Learning

Large amounts of labeled data are typically required to train deep learning
models. For many real-world problems, however, acquiring additional data can be
expensive or even impossible. We present semi-supervised deep kernel learning
(SSDKL), a semi-supervised regression model based on minimizing predictive
variance in the posterior regularization framework. SSDKL combines the
hierarchical representation learning of neural networks with the probabilistic
modeling capabilities of Gaussian processes. By leveraging unlabeled data, we
show improvements on a diverse set of real-world regression tasks over
supervised deep kernel learning and semi-supervised methods such as VAT and
mean teacher adapted for regression.
Robust Nonparametric Regression under Huber's $ε$-contamination Model
Authors: Simon S. Du, Yining Wang, Sivaraman Balakrishnan, Pradeep Ravikumar, Aarti Singh
Date: 2018-05-26 00:00:00
Tasks: model, regression

We consider the non-parametric regression problem under Huber's
$\epsilon$-contamination model, in which an $\epsilon$ fraction of observations
are subject to arbitrary adversarial noise. We first show that a simple local
binning median step can effectively remove the adversary noise and this median
estimator is minimax optimal up to absolute constants over the H\"{o}lder
function class with smoothness parameters smaller than or equal to 1.
Furthermore, when the underlying function has higher smoothness, we show that
using local binning median as pre-preprocessing step to remove the adversarial
noise, then we can apply any non-parametric estimator on top of the medians. In
particular we show local median binning followed by kernel smoothing and local
polynomial regression achieve minimaxity over H\"{o}lder and Sobolev classes
with arbitrary smoothness parameters. Our main proof technique is a decoupled
analysis of adversary noise and stochastic noise, which can be potentially
applied to other robust estimation problems. We also provide numerical results
to verify the effectiveness of our proposed methods.
Stochastic subgradient method converges on tame functions
Authors: Damek Davis, Dmitriy Drusvyatskiy, Sham Kakade, Jason D. Lee
Date: 2018-04-20 00:00:00

This work considers the question: what convergence guarantees does the
stochastic subgradient method have in the absence of smoothness and convexity?
We prove that the stochastic subgradient method, on any semialgebraic locally
Lipschitz function, produces limit points that are all first-order stationary.
More generally, our result applies to any function with a Whitney stratifiable
graph. In particular, this work endows the stochastic subgradient method, and
its proximal extension, with rigorous convergence guarantees for a wide class
of problems arising in data science---including all popular deep learning
architectures.
Deep Convolutional Neural Networks for Map-Type Classification
Authors: Xiran Zhou, Wenwen Li, Samantha T. Arundel, Jun Liu
Date: 2018-05-26 00:00:00
Tasks: Classification, General Classification, Land Cover Classification, Vocal Bursts Type Prediction

Maps are an important medium that enable people to comprehensively understand
the configuration of cultural activities and natural elements over different
times and places. Although massive maps are available in the digital era, how
to effectively and accurately access the required map remains a challenge
today. Previous works partially related to map-type classification mainly
focused on map comparison and map matching at the local scale. The features
derived from local map areas might be insufficient to characterize map content.
To facilitate establishing an automatic approach for accessing the needed map,
this paper reports our investigation into using deep learning techniques to
recognize seven types of map, including topographic map, terrain map, physical
map, urban scene map, the National Map, 3D map, nighttime map, orthophoto map,
and land cover classification map. Experimental results show that the
state-of-the-art deep convolutional neural networks can support automatic
map-type classification. Additionally, the classification accuracy varies
according to different map-types. We hope our work can contribute to the
implementation of deep learning techniques in cartographical community and
advance the progress of Geographical Artificial Intelligence (GeoAI).
Toward Abstractive Summarization Using Semantic Representations
Authors: Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, Noah A. Smith
Date: 2018-05-25 00:00:00
Proceeding: HLT 2015 5
Tasks: Abstractive Text Summarization, Abstract Meaning Representation

We present a novel abstractive summarization framework that draws on the
recent development of a treebank for the Abstract Meaning Representation (AMR).
In this framework, the source text is parsed to a set of AMR graphs, the graphs
are transformed into a summary graph, and then text is generated from the
summary graph. We focus on the graph-to-graph transformation that reduces the
source semantic graph into a summary graph, making use of an existing AMR
parser and assuming the eventual availability of an AMR-to-text generator. The
framework is data-driven, trainable, and not specifically designed for a
particular domain. Experiments on gold-standard AMR annotations and system
parses show promising results. Code is available at:
https://github.com/summarization
Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture
Authors: Dan Nguyen, Xun Jia, David Sher, Mu-Han Lin, Zohaib Iqbal, Hui Liu, Steve Jiang
Date: 2018-05-25 00:00:00

The treatment planning process for patients with head and neck (H&N) cancer
is regarded as one of the most complicated due to large target volume, multiple
prescription dose levels, and many radiation-sensitive critical structures near
the target. Treatment planning for this site requires a high level of human
expertise and a tremendous amount of effort to produce personalized high
quality plans, taking as long as a week, which deteriorates the chances of
tumor control and patient survival. To solve this problem, we propose to
investigate a deep learning-based dose prediction model, Hierarchically Densely
Connected U-net, based on two highly popular network architectures: U-net and
DenseNet. We find that this new architecture is able to accurately and
efficiently predict the dose distribution, outperforming the other two models,
the Standard U-net and DenseNet, in homogeneity, dose conformity, and dose
coverage on the test data. Averaging across all organs at risk, our proposed
model is capable of predicting the organ-at-risk max dose within 6.3% and mean
dose within 5.1% of the prescription dose on the test data. The other models,
the Standard U-net and DenseNet, performed worse, having an averaged
organ-at-risk max dose prediction error of 8.2% and 9.3%, respectively, and
averaged mean dose prediction error of 6.4% and 6.8%, respectively. In
addition, our proposed model used 12 times less trainable parameters than the
Standard U-net, and predicted the patient dose 4 times faster than DenseNet.
An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses
Authors: Wencan Luo, Fei Liu, Diane Litman
Date: 2018-05-25 00:00:00
Proceeding: COLING 2016 12
Tasks: Text Summarization

Teaching large classes remains a great challenge, primarily because it is
difficult to attend to all the student needs in a timely manner. Automatic text
summarization systems can be leveraged to summarize the student feedback,
submitted immediately after each lecture, but it is left to be discovered what
makes a good summary for student responses. In this work we explore a new
methodology that effectively extracts summary phrases from the student
responses. Each phrase is tagged with the number of students who raise the
issue. The phrases are evaluated along two dimensions: with respect to text
content, they should be informative and well-formed, measured by the ROUGE
metric; additionally, they shall attend to the most pressing student needs,
measured by a newly proposed metric. This work is enabled by a phrase-based
annotation and highlighting scheme, which is new to the summarization task. The
phrase-based framework allows us to summarize the student responses into a set
of bullet points and present to the instructor promptly.
Automatic Summarization of Student Course Feedback
Authors: Wencan Luo, Fei Liu, Zitao Liu, Diane Litman
Date: 2018-05-25 00:00:00
Proceeding: NAACL 2016 6

Student course feedback is generated daily in both classrooms and online
course discussion forums. Traditionally, instructors manually analyze these
responses in a costly manner. In this work, we propose a new approach to
summarizing student course feedback based on the integer linear programming
(ILP) framework. Our approach allows different student responses to share
co-occurrence statistics and alleviates sparsity issues. Experimental results
on a student feedback corpus show that our approach outperforms a range of
baselines in terms of both ROUGE scores and human evaluation.
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
Authors: Arash Vahdat, William G. Macready, Zhengbing Bian, Amir Khoshaman, Evgeny Andriyash
Date: 2018-02-14 00:00:00
Proceeding: ICML 2018 7
Tasks: Image Generation

Training of discrete latent variable models remains challenging because
passing gradient information through discrete units is difficult. We propose a
new class of smoothing transformations based on a mixture of two overlapping
distributions, and show that the proposed transformation can be used for
training binary latent models with either directed or undirected priors. We
derive a new variational bound to efficiently train with Boltzmann machine
priors. Using this bound, we develop DVAE++, a generative model with a global
discrete prior and a hierarchy of convolutional continuous variables.
Experiments on several benchmarks show that overlapping transformations
outperform other recent continuous relaxations of discrete latent variables
including Gumbel-Softmax (Maddison et al., 2016; Jang et al., 2016), and
discrete variational autoencoders (Rolfe 2016).
A Mathematical Framework for Deep Learning in Elastic Source Imaging
Authors: Jaejun Yoo, Abdul Wahab, Jong Chul Ye
Date: 2018-02-27 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

An inverse elastic source problem with sparse measurements is of concern. A
generic mathematical framework is proposed which incorporates a low-
dimensional manifold regularization in the conventional source reconstruction
algorithms thereby enhancing their performance with sparse datasets. It is
rigorously established that the proposed framework is equivalent to the
so-called \emph{deep convolutional framelet expansion} in machine learning
literature for inverse problems. Apposite numerical examples are furnished to
substantiate the efficacy of the proposed framework.
Modeling Language Vagueness in Privacy Policies using Deep Neural Networks
Authors: Fei Liu, Nicole Lee Fella, Kexin Liao
Date: 2018-05-25 00:00:00

Website privacy policies are too long to read and difficult to understand.
The over-sophisticated language makes privacy notices to be less effective than
they should be. People become even less willing to share their personal
information when they perceive the privacy policy as vague. This paper focuses
on decoding vagueness from a natural language processing perspective. While
thoroughly identifying the vague terms and their linguistic scope remains an
elusive challenge, in this work we seek to learn vector representations of
words in privacy policies using deep neural networks. The vector
representations are fed to an interactive visualization tool (LSTMVis) to test
on their ability to discover syntactically and semantically related vague
terms. The approach holds promise for modeling and understanding language
vagueness.
Reinforced Extractive Summarization with Question-Focused Rewards
Authors: Kristjan Arumae, Fei Liu
Date: 2018-05-25 00:00:00
Proceeding: ACL 2018 7
Tasks: Extractive Summarization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We investigate a new training paradigm for extractive summarization.
Traditionally, human abstracts are used to derive goldstandard labels for
extraction units. However, the labels are often inaccurate, because human
abstracts and source documents cannot be easily aligned at the word level. In
this paper we convert human abstracts to a set of Cloze-style comprehension
questions. System summaries are encouraged to preserve salient source content
useful for answering questions and share common words with the abstracts. We
use reinforcement learning to explore the space of possible extractive
summaries and introduce a question-focused reward function to promote concise,
fluent, and informative summaries. Our experiments show that the proposed
method is effective. It surpasses state-of-the-art systems on the standard
summarization dataset.
Toward Extractive Summarization of Online Forum Discussions via Hierarchical Attention Networks
Authors: Sansiri Tarnpradab, Fei Liu, Kien A. Hua
Date: 2018-05-25 00:00:00
Tasks: Extractive Summarization, Sentence

Forum threads are lengthy and rich in content. Concise thread summaries will
benefit both newcomers seeking information and those who participate in the
discussion. Few studies, however, have examined the task of forum thread
summarization. In this work we make the first attempt to adapt the hierarchical
attention networks for thread summarization. The model draws on the recent
development of neural attention mechanisms to build sentence and thread
representations and use them for summarization. Our results indicate that the
proposed approach can outperform a range of competitive baselines. Further, a
redundancy removal step is crucial for achieving outstanding results.
A Study of Question Effectiveness Using Reddit "Ask Me Anything" Threads
Authors: Kristjan Arumae, Guo-Jun Qi, Fei Liu
Date: 2018-05-25 00:00:00

Asking effective questions is a powerful social skill. In this paper we seek
to build computational models that learn to discriminate effective questions
from ineffective ones. Armed with such a capability, future advanced systems
can evaluate the quality of questions and provide suggestions for effective
question wording. We create a large-scale, real-world dataset that contains
over 400,000 questions collected from Reddit "Ask Me Anything" threads. Each
thread resembles an online press conference where questions compete with each
other for attention from the host. This dataset enables the development of a
class of computational models for predicting whether a question will be
answered. We develop a new convolutional neural network architecture with
variable-length context and demonstrate the efficacy of the model by comparing
it with state-of-the-art baselines and human judges.
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
Authors: Oleksii Kuchaiev, Boris Ginsburg, Igor Gitman, Vitaly Lavrukhin, Jason Li, Huyen Nguyen, Carl Case, Paulius Micikevicius
Date: 2018-05-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, Speech Synthesis, Translation

We present OpenSeq2Seq - a TensorFlow-based toolkit for training
sequence-to-sequence models that features distributed and mixed-precision
training. Benchmarks on machine translation and speech recognition tasks show
that models built using OpenSeq2Seq give state-of-the-art performance at 1.5-3x
less training time. OpenSeq2Seq currently provides building blocks for models
that solve a wide range of tasks including neural machine translation,
automatic speech recognition, and speech synthesis.
Large-scale Distance Metric Learning with Uncertainty
Authors: Qi Qian, Jiasheng Tang, Hao Li, Shenghuo Zhu, Rong Jin
Date: 2018-05-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Metric Learning, Triplet

Distance metric learning (DML) has been studied extensively in the past
decades for its superior performance with distance-based algorithms. Most of
the existing methods propose to learn a distance metric with pairwise or
triplet constraints. However, the number of constraints is quadratic or even
cubic in the number of the original examples, which makes it challenging for
DML to handle the large-scale data set. Besides, the real-world data may
contain various uncertainty, especially for the image data. The uncertainty can
mislead the learning procedure and cause the performance degradation. By
investigating the image data, we find that the original data can be observed
from a small set of clean latent examples with different distortions. In this
work, we propose the margin preserving metric learning framework to learn the
distance metric and latent examples simultaneously. By leveraging the ideal
properties of latent examples, the training efficiency can be improved
significantly while the learned metric also becomes robust to the uncertainty
in the original data. Furthermore, we can show that the metric is learned from
latent examples only, but it can preserve the large margin property even for
the original data. The empirical study on the benchmark image data sets
demonstrates the efficacy and efficiency of the proposed method.
Gradient Coding via the Stochastic Block Model
Authors: Zachary Charles, Dimitris Papailiopoulos
Date: 2018-05-25 00:00:00
Tasks: model, Stochastic Block Model

Gradient descent and its many variants, including mini-batch stochastic
gradient descent, form the algorithmic foundation of modern large-scale machine
learning. Due to the size and scale of modern data, gradient computations are
often distributed across multiple compute nodes. Unfortunately, such
distributed implementations can face significant delays caused by straggler
nodes, i.e., nodes that are much slower than average. Gradient coding is a new
technique for mitigating the effect of stragglers via algorithmic redundancy.
While effective, previously proposed gradient codes can be computationally
expensive to construct, inaccurate, or susceptible to adversarial stragglers.
In this work, we present the stochastic block code (SBC), a gradient code based
on the stochastic block model. We show that SBCs are efficient, accurate, and
that under certain settings, adversarial straggler selection becomes as hard as
detecting a community structure in the multiple community, block stochastic
graph model.
Deep Generative Dual Memory Network for Continual Learning
Authors: Nitin Kamra, Umang Gupta, Yan Liu
Date: 2017-10-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: Continual Learning, Hippocampus

Despite advances in deep learning, neural networks can only learn multiple
tasks when trained on them jointly. When tasks arrive sequentially, they lose
performance on previously learnt tasks. This phenomenon called catastrophic
forgetting is a fundamental challenge to overcome before neural networks can
learn continually from incoming data. In this work, we derive inspiration from
human memory to develop an architecture capable of learning continuously from
sequentially incoming tasks, while averting catastrophic forgetting.
Specifically, our contributions are: (i) a dual memory architecture emulating
the complementary learning systems (hippocampus and the neocortex) in the human
brain, (ii) memory consolidation via generative replay of past experiences,
(iii) demonstrating advantages of generative replay and dual memories via
experiments, and (iv) improved performance retention on challenging tasks even
for low capacity models. Our architecture displays many characteristics of the
mammalian memory and provides insights on the connection between sleep and
learning.