pwc-archive/papers-with-abstracts huggingface dataset:
Frequentist Consistency of Variational Bayes
Authors: Yixin Wang, David M. Blei
Date: 2017-05-09 00:00:00

A key challenge for modern Bayesian statistics is how to perform scalable inference of posterior distributions. To address this challenge, variational Bayes (VB) methods have emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) methods. VB methods tend to be faster while achieving comparable predictive performance. However, there are few theoretical results around VB. In this paper, we establish frequentist consistency and asymptotic normality of VB methods. Specifically, we connect VB methods to point estimates based on variational approximations, called frequentist variational approximations, and we use the connection to prove a variational Bernstein-von Mises theorem. The theorem leverages the theoretical characterizations of frequentist variational approximations to understand asymptotic properties of VB. In summary, we prove that (1) the VB posterior converges to the Kullback-Leibler (KL) minimizer of a normal distribution, centered at the truth and (2) the corresponding variational expectation of the parameter is consistent and asymptotically normal. As applications of the theorem, we derive asymptotic properties of VB posteriors in Bayesian mixture models, Bayesian generalized linear mixed models, and Bayesian stochastic block models. We conduct a simulation study to illustrate these theoretical results.
Learning and Testing Causal Models with Interventions
Authors: Jayadev Acharya, Arnab Bhattacharyya, Constantinos Daskalakis, Saravanan Kandasamy
Date: 2018-05-24 00:00:00
Proceeding: NeurIPS 2018 12

We consider testing and learning problems on causal Bayesian networks as
defined by Pearl (Pearl, 2009). Given a causal Bayesian network $\mathcal{M}$
on a graph with $n$ discrete variables and bounded in-degree and bounded
`confounded components', we show that $O(\log n)$ interventions on an unknown
causal Bayesian network $\mathcal{X}$ on the same graph, and
$\tilde{O}(n/\epsilon^2)$ samples per intervention, suffice to efficiently
distinguish whether $\mathcal{X}=\mathcal{M}$ or whether there exists some
intervention under which $\mathcal{X}$ and $\mathcal{M}$ are farther than
$\epsilon$ in total variation distance. We also obtain sample/time/intervention
efficient algorithms for: (i) testing the identity of two unknown causal
Bayesian networks on the same graph; and (ii) learning a causal Bayesian
network on a given graph. Although our algorithms are non-adaptive, we show
that adaptivity does not help in general: $\Omega(\log n)$ interventions are
necessary for testing the identity of two unknown causal Bayesian networks on
the same graph, even adaptively. Our algorithms are enabled by a new
subadditivity inequality for the squared Hellinger distance between two causal
Bayesian networks.
Weakly-supervised Semantic Parsing with Abstract Examples
Authors: Omer Goldman, Veronica Latcinnik, Udi Naveh, Amir Globerson, Jonathan Berant
Date: 2017-11-14 00:00:00
Tasks: Semantic Parsing, Visual Reasoning

Training semantic parsers from weak supervision (denotations) rather than
strong supervision (programs) complicates training in two ways. First, a large
search space of potential programs needs to be explored at training time to
find a correct program. Second, spurious programs that accidentally lead to a
correct denotation add noise to training. In this work we propose that in
closed worlds with clear semantic types, one can substantially alleviate these
problems by utilizing an abstract representation, where tokens in both the
language utterance and program are lifted to an abstract form. We show that
these abstractions can be defined with a handful of lexical rules and that they
result in sharing between different examples that alleviates the difficulties
in training. To test our approach, we develop the first semantic parser for
CNLVR, a challenging visual reasoning dataset, where the search space is large
and overcoming spuriousness is critical, because denotations are either TRUE or
FALSE, and thus random programs are likely to lead to a correct denotation. Our
method substantially improves performance, and reaches 82.5% accuracy, a 14.7%
absolute accuracy improvement compared to the best reported accuracy so far.
Been There, Done That: Meta-Learning with Episodic Recall
Authors: Samuel Ritter, Jane. X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew Botvinick
Date: 2018-05-24 00:00:00
Proceeding: ICML 2018 7
Tasks: Meta-Learning

Meta-learning agents excel at rapidly learning new tasks from open-ended task
distributions; yet, they forget what they learn about each task as soon as the
next begins. When tasks reoccur - as they do in natural environments -
metalearning agents must explore again instead of immediately exploiting
previously discovered solutions. We propose a formalism for generating
open-ended yet repetitious environments, then develop a meta-learning
architecture for solving these environments. This architecture melds the
standard LSTM working memory with a differentiable neural episodic memory. We
explore the capabilities of agents with this episodic LSTM in five
meta-learning environments with reoccurring tasks, ranging from bandits to
navigation and stochastic sequential decision problems.
On Improving Deep Reinforcement Learning for POMDPs
Authors: Pengfei Zhu, Xin Li, Pascal Poupart, Guanghui Miao
Date: 2017-04-26 00:00:00
Tasks: Atari Games, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making, Time Series, Time Series Analysis

Deep Reinforcement Learning (RL) recently emerged as one of the most
competitive approaches for learning in sequential decision making problems with
fully observable environments, e.g., computer Go. However, very little work has
been done in deep RL to handle partially observable environments. We propose a
new architecture called Action-specific Deep Recurrent Q-Network (ADRQN) to
enhance learning performance in partially observable domains. Actions are
encoded by a fully connected layer and coupled with a convolutional observation
to form an action-observation pair. The time series of action-observation pairs
are then integrated by an LSTM layer that learns latent states based on which a
fully connected layer computes Q-values as in conventional Deep Q-Networks
(DQNs). We demonstrate the effectiveness of our new architecture in several
partially observable domains, including flickering Atari games.
Deep Residual Networks with a Fully Connected Recon-struction Layer for Single Image Super-Resolution
Authors: Yongliang Tang, Jiashui Huang, Faen Zhang, Weiguo Gong
Date: 2018-05-24 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Recently, deep neural networks have achieved impressive performance in terms of both reconstruction accuracy and efficiency for single image super-resolution (SISR). However, the network model of these methods is a fully convolutional neural network, which is limit to exploit the differentiated contextual information over the global region of the input image because of the weight sharing in convolution height and width extent. In this paper, we discuss a new SISR architecture where features are extracted in the low-resolution (LR) space, and then we use a fully connected layer which learns an array of differentiated upsampling weights to reconstruct the desired high-resolution (HR) image from the final obtained LR features. By doing so, we effectively exploit the differentiated contextual information over the whole input image region, whilst maintaining the low computational complexity for the overall SR operations. In addition, we introduce an edge difference constraint into our loss function to preserve edges and texture structures. Extensive experiments validate that our SISR method outperforms the existing state-of-the-art methods.
A Unified Knowledge Representation and Context-aware Recommender System in Internet of Things
Authors: Yinhao Li, Awa Alqahtani, Ellis Solaiman, Charith Perera, Prem Prakash Jayaraman, Boualem Benatallah, Rajiv Ranjan
Date: 2018-05-10 00:00:00
Tasks: Recommendation Systems

Within the rapidly developing Internet of Things (IoT), numerous and diverse
physical devices, Edge devices, Cloud infrastructure, and their quality of
service requirements (QoS), need to be represented within a unified
specification in order to enable rapid IoT application development, monitoring,
and dynamic reconfiguration. But heterogeneities among different configuration
knowledge representation models pose limitations for acquisition, discovery and
curation of configuration knowledge for coordinated IoT applications. This
paper proposes a unified data model to represent IoT resource configuration
knowledge artifacts. It also proposes IoT-CANE (Context-Aware recommendatioN
systEm) to facilitate incremental knowledge acquisition and declarative context
driven knowledge recommendation.
Forming IDEAS Interactive Data Exploration & Analysis System
Authors: Robert A. Bridges, Maria A. Vincent, Kelly M. T. Huffer, John R. Goodall, Jessie D. Jamieson, Zachary Burch
Date: 2018-05-24 00:00:00

Modern cyber security operations collect an enormous amount of logging and
alerting data. While analysts have the ability to query and compute simple
statistics and plots from their data, current analytical tools are too simple
to admit deep understanding. To detect advanced and novel attacks, analysts
turn to manual investigations. While commonplace, current investigations are
time-consuming, intuition-based, and proving insufficient. Our hypothesis is
that arming the analyst with easy-to-use data science tools will increase their
work efficiency, provide them with the ability to resolve hypotheses with
scientific inquiry of their data, and support their decisions with evidence
over intuition. To this end, we present our work to build IDEAS (Interactive
Data Exploration and Analysis System). We present three real-world use-cases
that drive the system design from the algorithmic capabilities to the user
interface. Finally, a modular and scalable software architecture is discussed
along with plans for our pilot deployment with a security operation command.
Classical Structured Prediction Losses for Sequence to Sequence Learning
Authors: Sergey Edunov, Myle Ott, Michael Auli, David Grangier, Marc'Aurelio Ranzato
Date: 2017-11-14 00:00:00
Proceeding: NAACL 2018 6
Tasks: Abstractive Text Summarization, Machine Translation, Prediction, Reinforcement Learning, Reinforcement Learning (RL), Structured Prediction, Translation

There has been much recent work on training neural attention models at the
sequence-level using either reinforcement learning-style methods or by
optimizing the beam. In this paper, we survey a range of classical objective
functions that have been widely used to train linear models for structured
prediction and apply them to neural sequence to sequence models. Our
experiments show that these losses can perform surprisingly well by slightly
outperforming beam search optimization in a like for like setup. We also report
new state of the art results on both IWSLT'14 German-English translation as
well as Gigaword abstractive summarization. On the larger WMT'14 English-French
translation task, sequence-level training achieves 41.5 BLEU which is on par
with the state of the art.
LF-Net: Learning Local Features from Images
Authors: Yuki Ono, Eduard Trulls, Pascal Fua, Kwang Moo Yi
Date: 2018-05-24 00:00:00
Proceeding: NeurIPS 2018 12

We present a novel deep architecture and a training strategy to learn a local
feature pipeline from scratch, using collections of images without the need for
human supervision. To do so we exploit depth and relative camera pose cues to
create a virtual target that the network should achieve on one image, provided
the outputs of the network for the other image. While this process is
inherently non-differentiable, we show that we can optimize the network in a
two-branch setup by confining it to one branch, while preserving
differentiability in the other. We train our method on both indoor and outdoor
datasets, with depth data from 3D sensors for the former, and depth estimates
from an off-the-shelf Structure-from-Motion solution for the latter. Our models
outperform the state of the art on sparse feature matching on both datasets,
while running at 60+ fps for QVGA images.
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
Authors: Kai Zhang, WangMeng Zuo, Lei Zhang
Date: 2017-12-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Super-Resolution, Super-Resolution, Video Super-Resolution

Recent years have witnessed the unprecedented success of deep convolutional
neural networks (CNNs) in single image super-resolution (SISR). However,
existing CNN-based SISR methods mostly assume that a low-resolution (LR) image
is bicubicly downsampled from a high-resolution (HR) image, thus inevitably
giving rise to poor performance when the true degradation does not follow this
assumption. Moreover, they lack scalability in learning a single model to
non-blindly deal with multiple degradations. To address these issues, we
propose a general framework with dimensionality stretching strategy that
enables a single convolutional super-resolution network to take two key factors
of the SISR degradation process, i.e., blur kernel and noise level, as input.
Consequently, the super-resolver can handle multiple and even spatially variant
degradations, which significantly improves the practicability. Extensive
experimental results on synthetic and real LR images show that the proposed
convolutional super-resolution network not only can produce favorable results
on multiple degradations but also is computationally efficient, providing a
highly effective and scalable solution to practical SISR applications.
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition
Authors: Albert Zeyer, Tamer Alkhouli, Hermann Ney
Date: 2018-05-14 00:00:00
Proceeding: ACL 2018 7
Tasks: Decoder, speech-recognition, Speech Recognition, Translation

We compare the fast training and decoding speed of RETURNN of attention
models for translation, due to fast CUDA LSTM kernels, and a fast pure
TensorFlow beam search decoder. We show that a layer-wise pretraining scheme
for recurrent attention models gives over 1% BLEU improvement absolute and it
allows to train deeper recurrent encoder networks. Promising preliminary
results on max. expected BLEU training are presented. We are able to train
state-of-the-art models for translation and end-to-end models for speech
recognition and show results on WMT 2017 and Switchboard. The flexibility of
RETURNN allows a fast research feedback loop to experiment with alternative
architectures, and its generality allows to use it on a wide range of
applications.
Supervised Community Detection with Line Graph Neural Networks
Authors: Zhengdao Chen, Xiang Li, Joan Bruna
Date: 2017-05-23 00:00:00
Proceeding: ICLR 2019 5
Tasks: Community Detection, Graph Classification, Stochastic Block Model

Traditionally, community detection in graphs can be solved using spectral methods or posterior inference under probabilistic graphical models. Focusing on random graph families such as the stochastic block model, recent research has unified both approaches and identified both statistical and computational detection thresholds in terms of the signal-to-noise ratio. By recasting community detection as a node-wise classification problem on graphs, we can also study it from a learning perspective. We present a novel family of Graph Neural Networks (GNNs) for solving community detection problems in a supervised learning setting. We show that, in a data-driven manner and without access to the underlying generative models, they can match or even surpass the performance of the belief propagation algorithm on binary and multi-class stochastic block models, which is believed to reach the computational threshold. In particular, we propose to augment GNNs with the non-backtracking operator defined on the line graph of edge adjacencies. Our models also achieve good performance on real-world datasets. In addition, we perform the first analysis of the optimization landscape of training linear GNNs for community detection problems, demonstrating that under certain simplifications and assumptions, the loss values at local and global minima are not far apart.
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Authors: Jay Heo, Hae Beom Lee, Saehoon Kim, Juho Lee, Kwang Joon Kim, Eunho Yang, Sung Ju Hwang
Date: 2018-05-24 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Prediction, Variational Inference

Attention mechanism is effective in both focusing the deep learning models on
relevant features and interpreting them. However, attentions may be unreliable
since the networks that generate them are often trained in a weakly-supervised
manner. To overcome this limitation, we introduce the notion of input-dependent
uncertainty to the attention mechanism, such that it generates attention for
each feature with varying degrees of noise based on the given input, to learn
larger variance on instances it is uncertain about. We learn this
Uncertainty-aware Attention (UA) mechanism using variational inference, and
validate it on various risk prediction tasks from electronic health records on
which our model significantly outperforms existing attention models. The
analysis of the learned attentions shows that our model generates attentions
that comply with clinicians' interpretation, and provide richer interpretation
via learned variance. Further evaluation of both the accuracy of the
uncertainty calibration and the prediction performance with "I don't know"
decision show that UA yields networks with high reliability as well.
Filtering and Mining Parallel Data in a Joint Multilingual Space
Authors: Holger Schwenk
Date: 2018-05-24 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, Sentence, Sentence Embedding, Sentence-Embedding, Translation

We learn a joint multilingual sentence embedding and use the distance between
sentences in different languages to filter noisy parallel data and to mine for
parallel data in large news collections. We are able to improve a competitive
baseline on the WMT'14 English to German task by 0.3 BLEU by filtering out 25%
of the training data. The same approach is used to mine additional bitexts for
the WMT'14 system and to obtain competitive results on the BUCC shared task to
identify parallel sentences in comparable corpora. The approach is generic, it
can be applied to many language pairs and it is independent of the architecture
of the machine translation system.
Online Regularized Nonlinear Acceleration
Authors: Damien Scieur, Edouard Oyallon, Alexandre d'Aspremont, Francis Bach
Date: 2018-05-24 00:00:00
Tasks: General Classification

Regularized nonlinear acceleration (RNA) estimates the minimum of a function by post-processing iterates from an algorithm such as the gradient method. It can be seen as a regularized version of Anderson acceleration, a classical acceleration scheme from numerical analysis. The new scheme provably improves the rate of convergence of fixed step gradient descent, and its empirical performance is comparable to that of quasi-Newton methods. However, RNA cannot accelerate faster multistep algorithms like Nesterov's method and often diverges in this context. Here, we adapt RNA to overcome these issues, so that our scheme can be used on fast algorithms such as gradient methods with momentum. We show optimal complexity bounds for quadratics and asymptotically optimal rates on general convex minimization problems. Moreover, this new scheme works online, i.e., extrapolated solution estimates can be reinjected at each iteration, significantly improving numerical performance over classical accelerated methods.
Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning
Authors: Philip Soeffker, Dimitri Block, Nico Wiebusch, Uwe Meier
Date: 2018-05-24 00:00:00
Tasks: Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Self-Learning

In industrial environments, an increasing amount of wireless devices are
used, which utilize license-free bands. As a consequence of these mutual
interferences of wireless systems might decrease the state of coexistence.
Therefore, a central coexistence management system is needed, which allocates
conflict-free resources to wireless systems. To ensure a conflict-free resource
utilization, it is useful to predict the prospective medium utilization before
resources are allocated. This paper presents a self-learning concept, which is
based on reinforcement learning. A simulative evaluation of reinforcement
learning agents based on neural networks, called deep Q-networks and double
deep Q-networks, was realized for exemplary and practically relevant
coexistence scenarios. The evaluation of the double deep Q-network showed that
a prediction accuracy of at least 98 % can be reached in all investigated
scenarios.
SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels
Authors: Or Litany, Daniel Freedman
Date: 2018-05-24 00:00:00
Proceeding: ICLR 2019 5
Tasks: Bilevel Optimization, Classification, General Classification, Transfer Learning

We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset. SOSELETO is based on the following simple intuition: some source examples are more informative than others for the target problem. To capture this intuition, source samples are each given weights; these weights are solved for jointly with the source and target classification problems via a bilevel optimization scheme. The target therefore gets to choose the source samples which are most informative for its own classification task. Furthermore, the bilevel nature of the optimization acts as a kind of regularization on the target, mitigating overfitting. SOSELETO may be applied to both classic transfer learning, as well as the problem of training on datasets with noisy labels; we show state of the art results on both of these problems.
Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products
Authors: Zhe-Cheng Fan, Tak-Shing T. Chan, Yi-Hsuan Yang, Jyh-Shing R. Jang
Date: 2018-05-24 00:00:00
Tasks: Denoising, Image Denoising

Vector-valued neural learning has emerged as a promising direction in deep
learning recently. Traditionally, training data for neural networks (NNs) are
formulated as a vector of scalars; however, its performance may not be optimal
since associations among adjacent scalars are not modeled. In this paper, we
propose a new vector neural architecture called the Arbitrary BIlinear Product
Neural Network (ABIPNN), which processes information as vectors in each neuron,
and the feedforward projections are defined using arbitrary bilinear products.
Such bilinear products can include circular convolution, seven-dimensional
vector product, skew circular convolution, reversed- time circular convolution,
or other new products not seen in previous work. As a proof-of-concept, we
apply our proposed network to multispectral image denoising and singing voice
sepa- ration. Experimental results show that ABIPNN gains substantial
improvements when compared to conventional NNs, suggesting that associations
are learned during training.