pwc-archive/papers-with-abstracts huggingface dataset:
Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems
Authors: Andrea Madotto, Chien-Sheng Wu, Pascale Fung
Date: 2018-04-23 00:00:00
Proceeding: ACL 2018 7
Tasks: Task-Oriented Dialogue Systems

End-to-end task-oriented dialog systems usually suffer from the challenge of
incorporating knowledge bases. In this paper, we propose a novel yet simple
end-to-end differentiable model called memory-to-sequence (Mem2Seq) to address
this issue. Mem2Seq is the first neural generative model that combines the
multi-hop attention over memories with the idea of pointer network. We
empirically show how Mem2Seq controls each generation step, and how its
multi-hop attention mechanism helps in learning correlations between memories.
In addition, our model is quite general without complicated task-specific
designs. As a result, we show that Mem2Seq can be trained faster and attain the
state-of-the-art performance on three different task-oriented dialog datasets.
Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference
Authors: Ruying Bao, Sihang Liang, Qingcan Wang
Date: 2018-05-21 00:00:00
Proceeding: ICLR 2019 5
Tasks: Adversarial Defense

Deep neural networks have been demonstrated to be vulnerable to adversarial
attacks, where small perturbations intentionally added to the original inputs
can fool the classifier. In this paper, we propose a defense method, Featurized
Bidirectional Generative Adversarial Networks (FBGAN), to extract the semantic
features of the input and filter the non-semantic perturbation. FBGAN is
pre-trained on the clean dataset in an unsupervised manner, adversarially
learning a bidirectional mapping between the high-dimensional data space and
the low-dimensional semantic space; also mutual information is applied to
disentangle the semantically meaningful features. After the bidirectional
mapping, the adversarial data can be reconstructed to denoised data, which
could be fed into any pre-trained classifier. We empirically show the quality
of reconstruction images and the effectiveness of defense.
Automata-Guided Hierarchical Reinforcement Learning for Skill Composition
Authors: Xiao Li, Yao Ma, Calin Belta
Date: 2017-10-31 00:00:00
Tasks: Deep Reinforcement Learning, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Skills learned through (deep) reinforcement learning often generalizes poorly
across domains and re-training is necessary when presented with a new task. We
present a framework that combines techniques in \textit{formal methods} with
\textit{reinforcement learning} (RL). The methods we provide allows for
convenient specification of tasks with logical expressions, learns hierarchical
policies (meta-controller and low-level controllers) with well-defined
intrinsic rewards, and construct new skills from existing ones with little to
no additional exploration. We evaluate the proposed methods in a simple grid
world simulation as well as a more complicated kitchen environment in AI2Thor
Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge
Authors: Todor Mihaylov, Anette Frank
Date: 2018-05-21 00:00:00
Proceeding: ACL 2018 7
Tasks: Reading Comprehension

We introduce a neural reading comprehension model that integrates external
commonsense knowledge, encoded as a key-value memory, in a cloze-style setting.
Instead of relying only on document-to-question interaction or discrete
features as in prior work, our model attends to relevant external knowledge and
combines this knowledge with the context representation before inferring the
answer. This allows the model to attract and imply knowledge from an external
knowledge source that is not explicitly stated in the text, but that is
relevant for inferring the answer. Our model improves results over a very
strong baseline on a hard Common Nouns dataset, making it a strong competitor
of much more complex models. By including knowledge explicitly, our model can
also provide evidence about the background knowledge used in the RC process.
Parallel Transport Convolution: A New Tool for Convolutional Neural Networks on Manifolds
Authors: Stefan C. Schonsheck, Bin Dong, Rongjie Lai
Date: 2018-05-21 00:00:00

Convolution has been playing a prominent role in various applications in
science and engineering for many years. It is the most important operation in
convolutional neural networks. There has been a recent growth of interests of
research in generalizing convolutions on curved domains such as manifolds and
graphs. However, existing approaches cannot preserve all the desirable
properties of Euclidean convolutions, namely compactly supported filters,
directionality, transferability across different manifolds. In this paper we
develop a new generalization of the convolution operation, referred to as
parallel transport convolution (PTC), on Riemannian manifolds and their
discrete counterparts. PTC is designed based on the parallel transportation
which is able to translate information along a manifold and to intrinsically
preserve directionality. PTC allows for the construction of compactly supported
filters and is also robust to manifold deformations. This enables us to preform
wavelet-like operations and to define deep convolutional neural networks on
curved domains.
Accelerated Bayesian Optimization throughWeight-Prior Tuning
Authors: Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Laurence Park, Cheng Li, Svetha Venkatesh, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height, Teo Slezak
Date: 2018-05-21 00:00:00
Tasks: Bayesian Optimisation, Bayesian Optimization, Transfer Learning

Bayesian optimization (BO) is a widely-used method for optimizing expensive (to evaluate) problems. At the core of most BO methods is the modeling of the objective function using a Gaussian Process (GP) whose covariance is selected from a set of standard covariance functions. From a weight-space view, this models the objective as a linear function in a feature space implied by the given covariance K, with an arbitrary Gaussian weight prior ${\bf w} \sim \mathcal{N} ({\bf 0}, {\bf I})$. In many practical applications there is data available that has a similar (covariance) structure to the objective, but which, having different form, cannot be used directly in standard transfer learning. In this paper we show how such auxiliary data may be used to construct a GP covariance corresponding to a more appropriate weight prior for the objective function. Building on this, we show that we may accelerate BO by modeling the objective function using this (learned) weight prior, which we demonstrate on both test functions and a practical application to short-polymer fibre manufacture.
A Text Analysis of Federal Reserve meeting minutes
Authors: Harish Gandhi Ramachandran, Dan DeRose Jr
Date: 2018-05-21 00:00:00

Recent developments in monetary policy by the Federal Reserve has created a
need for an objective method of communication analysis.Using methods developed
for text analysis, we present a novel technique of analysis which creates a
semantic space defined by various policymakers public comments and places the
committee consensus in the appropriate location. Its then possible to determine
which member of the committee is most closely aligned with the committee
consensus over time and create a foundation for further actionable research.
Projection-Free Algorithms in Statistical Estimation
Authors: Yan Li, Chao Qu, Huan Xu
Date: 2018-05-20 00:00:00

Frank-Wolfe algorithm (FW) and its variants have gained a surge of interests
in machine learning community due to its projection-free property. Recently
people have reduced the gradient evaluation complexity of FW algorithm to
$\log(\frac{1}{\epsilon})$ for the smooth and strongly convex objective. This
complexity result is especially significant in learning problem, as the
overwhelming data size makes a single evluation of gradient computational
expensive. However, in high-dimensional statistical estimation problems, the
objective is typically not strongly convex, and sometimes even non-convex. In
this paper, we extend the state-of-the-art FW type algorithms for the
large-scale, high-dimensional estimation problem. We show that as long as the
objective satisfies {\em restricted strong convexity}, and we are not
optimizing over statistical limit of the model, the $\log(\frac{1}{\epsilon})$
gradient evaluation complexity could still be attained.
Communication-Efficient Projection-Free Algorithm for Distributed Optimization
Authors: Yan Li, Chao Qu, Huan Xu
Date: 2018-05-20 00:00:00
Tasks: Distributed Optimization, Matrix Completion

Distributed optimization has gained a surge of interest in recent years. In
this paper we propose a distributed projection free algorithm named Distributed
Conditional Gradient Sliding(DCGS). Compared to the state-of-the-art
distributed Frank-Wolfe algorithm, our algorithm attains the same communication
complexity under much more realistic assumptions. In contrast to the consensus
based algorithm, DCGS is based on the primal-dual algorithm, yielding a modular
analysis that can be exploited to improve linear oracle complexity whenever
centralized Frank-Wolfe can be improved. We demonstrate this advantage and show
that the linear oracle complexity can be reduced to almost the same order of
magnitude as the communication complexity, when the feasible set is polyhedral.
Finally we present experimental results on Lasso and matrix completion,
demonstrating significant performance improvement compared to the existing
distributed Frank-Wolfe algorithm.
Attention U-Net: Learning Where to Look for the Pancreas
Authors: Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y. Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert
Date: 2018-04-11 00:00:00
Tasks: Brain Tumor Segmentation, Computational Efficiency, Image Segmentation, Medical Image Segmentation, Pancreas Segmentation, Semantic Segmentation

We propose a novel attention gate (AG) model for medical imaging that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us to eliminate the necessity of using explicit external
tissue/organ localisation modules of cascaded convolutional neural networks
(CNNs). AGs can be easily integrated into standard CNN architectures such as
the U-Net model with minimal computational overhead while increasing the model
sensitivity and prediction accuracy. The proposed Attention U-Net architecture
is evaluated on two large CT abdominal datasets for multi-class image
segmentation. Experimental results show that AGs consistently improve the
prediction performance of U-Net across different datasets and training sizes
while preserving computational efficiency. The code for the proposed
architecture is publicly available.
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Authors: Zhilu Zhang, Mert R. Sabuncu
Date: 2018-05-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Image Classification, Learning with noisy labels

Deep neural networks (DNNs) have achieved tremendous success in a variety of
applications across many disciplines. Yet, their superior performance comes
with the expensive cost of requiring correctly annotated large-scale datasets.
Moreover, due to DNNs' rich capacity, errors in training labels can hamper
performance. To combat this problem, mean absolute error (MAE) has recently
been proposed as a noise-robust alternative to the commonly-used categorical
cross entropy (CCE) loss. However, as we show in this paper, MAE can perform
poorly with DNNs and challenging datasets. Here, we present a theoretically
grounded set of noise-robust loss functions that can be seen as a
generalization of MAE and CCE. Proposed loss functions can be readily applied
with any existing DNN architecture and algorithm, while yielding good
performance in a wide range of noisy label scenarios. We report results from
experiments conducted with CIFAR-10, CIFAR-100 and FASHION-MNIST datasets and
synthetically generated noisy labels.
Wasserstein regularization for sparse multi-task regression
Authors: Hicham Janati, Marco Cuturi, Alexandre Gramfort
Date: 2018-05-20 00:00:00
Tasks: regression

We focus in this paper on high-dimensional regression problems where each
regressor can be associated to a location in a physical space, or more
generally a generic geometric space. Such problems often employ sparse priors,
which promote models using a small subset of regressors. To increase
statistical power, the so-called multi-task techniques were proposed, which
consist in the simultaneous estimation of several related models. Combined with
sparsity assumptions, it lead to models enforcing the active regressors to be
shared across models, thanks to, for instance L1 / Lq norms. We argue in this
paper that these techniques fail to leverage the spatial information associated
to regressors. Indeed, while sparse priors enforce that only a small subset of
variables is used, the assumption that these regressors overlap across all
tasks is overly simplistic given the spatial variability observed in real data.
In this paper, we propose a convex regularizer for multi-task regression that
encodes a more flexible geometry. Our regularizer is based on unbalanced
optimal transport (OT) theory, and can take into account a prior geometric
knowledge on the regressor variables, without necessarily requiring overlapping
supports. We derive an efficient algorithm based on a regularized formulation
of OT, which iterates through applications of Sinkhorn's algorithm along with
coordinate descent iterations. The performance of our model is demonstrated on
regular grids with both synthetic and real datasets as well as complex
triangulated geometries of the cortex with an application in neuroimaging.
Stereoscopic Neural Style Transfer
Authors: Dongdong Chen, Lu Yuan, Jing Liao, Nenghai Yu, Gang Hua
Date: 2018-02-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Style Transfer

This paper presents the first attempt at stereoscopic neural style transfer,
which responds to the emerging demand for 3D movies or AR/VR. We start with a
careful examination of applying existing monocular style transfer methods to
left and right views of stereoscopic images separately. This reveals that the
original disparity consistency cannot be well preserved in the final
stylization results, which causes 3D fatigue to the viewers. To address this
issue, we incorporate a new disparity loss into the widely adopted style loss
function by enforcing the bidirectional disparity constraint in non-occluded
regions. For a practical real-time solution, we propose the first feed-forward
network by jointly training a stylization sub-network and a disparity
sub-network, and integrate them in a feature level middle domain. Our disparity
sub-network is also the first end-to-end network for simultaneous bidirectional
disparity and occlusion mask estimation. Finally, our network is effectively
extended to stereoscopic videos, by considering both temporal coherence and
disparity consistency. We will show that the proposed method clearly
outperforms the baseline algorithms both quantitatively and qualitatively.
A VEST of the Pseudoinverse Learning Algorithm
Authors: Ping Guo
Date: 2018-05-20 00:00:00

In this paper, we briefly review the basic scheme of the pseudoinverse
learning (PIL) algorithm and present some discussions on the PIL, as well as
its variants. The PIL algorithm, first presented in 1995, is a non-gradient
descent and non-iterative learning algorithm for multi-layer neural networks
and has several advantages compared with gradient descent based algorithms.
Some new viewpoints to PIL algorithm are presented, and several common pitfalls
in practical implementation of the neural network learning task are also
addressed. In addition, we show that so called extreme learning machine is a
Variant crEated by Simple name alTernation (VEST) of the PIL algorithm for
single hidden layer feedforward neural networks.
Validating WordNet Meronymy Relations using Adimen-SUMO
Authors: Javier Álvez, Itziar Gonzalez-Dios, German Rigau
Date: 2018-05-20 00:00:00

In this paper, we report on the practical application of a novel approach for
validating the knowledge of WordNet using Adimen-SUMO. In particular, this
paper focuses on cross-checking the WordNet meronymy relations against the
knowledge encoded in Adimen-SUMO. Our validation approach tests a large set of
competency questions (CQs), which are derived (semi)-automatically from the
knowledge encoded in WordNet, SUMO and their mapping, by applying efficient
first-order logic automated theorem provers. Unfortunately, despite of being
created manually, these knowledge resources are not free of errors and
discrepancies. In consequence, some of the resulting CQs are not plausible
according to the knowledge included in Adimen-SUMO. Thus, first we focus on
(semi)-automatically improving the alignment between these knowledge resources,
and second, we perform a minimal set of corrections in the ontology. Our aim is
to minimize the manual effort required for an extensive validation process. We
report on the strategies followed, the changes made, the effort needed and its
impact when validating the WordNet meronymy relations using improved versions
of the mapping and the ontology. Based on the new results, we discuss the
implications of the appropriate corrections and the need of future
enhancements.
Generating Multi-Agent Trajectories using Programmatic Weak Supervision
Authors: Eric Zhan, Stephan Zheng, Yisong Yue, Long Sha, Patrick Lucey
Date: 2018-03-20 00:00:00
Proceeding: ICLR 2019 5
Tasks: Imitation Learning

We study the problem of training sequential generative models for capturing
coordinated multi-agent trajectory behavior, such as offensive basketball
gameplay. When modeling such settings, it is often beneficial to design
hierarchical models that can capture long-term coordination using intermediate
variables. Furthermore, these intermediate variables should capture interesting
high-level behavioral semantics in an interpretable and manipulatable way. We
present a hierarchical framework that can effectively learn such sequential
generative models. Our approach is inspired by recent work on leveraging
programmatically produced weak labels, which we extend to the spatiotemporal
regime. In addition to synthetic settings, we show how to instantiate our
framework to effectively model complex interactions between basketball players
and generate realistic multi-agent trajectories of basketball gameplay over
long time periods. We validate our approach using both quantitative and
qualitative evaluations, including a user study comparison conducted with
professional sports analysts.
Targeted Adversarial Examples for Black Box Audio Systems
Authors: Rohan Taori, Amog Kamsetty, Brenton Chu, Nikita Vemuri
Date: 2018-05-20 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence. Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known. In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task. We achieve a 89.25% targeted attack similarity after 3000 generations while maintaining 94.6% audio file similarity.
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
Authors: Sergey Levine
Date: 2018-05-02 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Variational Inference

The framework of reinforcement learning or optimal control provides a
mathematical formalization of intelligent decision making that is powerful and
broadly applicable. While the general form of the reinforcement learning
problem enables effective reasoning about uncertainty, the connection between
reinforcement learning and inference in probabilistic models is not immediately
obvious. However, such a connection has considerable value when it comes to
algorithm design: formalizing a problem as probabilistic inference in principle
allows us to bring to bear a wide array of approximate inference tools, extend
the model in flexible and powerful ways, and reason about compositionality and
partial observability. In this article, we will discuss how a generalization of
the reinforcement learning or optimal control problem, which is sometimes
termed maximum entropy reinforcement learning, is equivalent to exact
probabilistic inference in the case of deterministic dynamics, and variational
inference in the case of stochastic dynamics. We will present a detailed
derivation of this framework, overview prior work that has drawn on this and
related ideas to propose new reinforcement learning and control algorithms, and
describe perspectives on future research.
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Authors: Jonathan Frankle, Michael Carbin
Date: 2018-03-09 00:00:00
Proceeding: ICLR 2019 5
Tasks: Network Pruning

Neural network pruning techniques can reduce the parameter counts of trained
networks by over 90%, decreasing storage requirements and improving
computational performance of inference without compromising accuracy. However,
contemporary experience is that the sparse architectures produced by pruning
are difficult to train from the start, which would similarly improve training
performance.
  We find that a standard pruning technique naturally uncovers subnetworks
whose initializations made them capable of training effectively. Based on these
results, we articulate the "lottery ticket hypothesis:" dense,
randomly-initialized, feed-forward networks contain subnetworks ("winning
tickets") that - when trained in isolation - reach test accuracy comparable to
the original network in a similar number of iterations. The winning tickets we
find have won the initialization lottery: their connections have initial
weights that make training particularly effective.
  We present an algorithm to identify winning tickets and a series of
experiments that support the lottery ticket hypothesis and the importance of
these fortuitous initializations. We consistently find winning tickets that are
less than 10-20% of the size of several fully-connected and convolutional
feed-forward architectures for MNIST and CIFAR10. Above this size, the winning
tickets that we find learn faster than the original network and reach higher
test accuracy.
Learning Real-World Robot Policies by Dreaming
Authors: AJ Piergiovanni, Alan Wu, Michael S. Ryoo
Date: 2018-05-20 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning to control robots directly based on images is a primary challenge in robotics. However, many existing reinforcement learning approaches require iteratively obtaining millions of robot samples to learn a policy, which can take significant time. In this paper, we focus on learning a realistic world model capturing the dynamics of scene changes conditioned on robot actions. Our dreaming model can emulate samples equivalent to a sequence of images from the actual environment, technically by learning an action-conditioned future representation/scene regressor. This allows the agent to learn action policies (i.e., visuomotor policies) by interacting with the dreaming model rather than the real-world. We experimentally confirm that our dreaming model enables robot learning of policies that transfer to the real-world.