pwc-archive/papers-with-abstracts huggingface dataset:
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
Authors: Rim Assouel, Mohamed Ahmed, Marwin H Segler, Amir Saffari, Yoshua Bengio
Date: 2018-11-24 00:00:00
Proceeding: ICLR 2019 5
Tasks: Drug Discovery, Graph Generation

Generating novel molecules with optimal properties is a crucial step in many
industries such as drug discovery. Recently, deep generative models have shown
a promising way of performing de-novo molecular design. Although graph
generative models are currently available they either have a graph size
dependency in their number of parameters, limiting their use to only very small
graphs or are formulated as a sequence of discrete actions needed to construct
a graph, making the output graph non-differentiable w.r.t the model parameters,
therefore preventing them to be used in scenarios such as conditional graph
generation. In this work we propose a model for conditional graph generation
that is computationally efficient and enables direct optimisation of the graph.
We demonstrate favourable performance of our model on prototype-based molecular
graph conditional generation tasks.
Mean Local Group Average Precision (mLGAP): A New Performance Metric for Hashing-based Retrieval
Authors: Pak Lun Kevin Ding, Yikang Li, Baoxin Li
Date: 2018-11-24 00:00:00
Tasks: Image Retrieval, Retrieval

The research on hashing techniques for visual data is gaining increased
attention in recent years due to the need for compact representations
supporting efficient search/retrieval in large-scale databases such as online
images. Among many possibilities, Mean Average Precision(mAP) has emerged as
the dominant performance metric for hashing-based retrieval. One glaring
shortcoming of mAP is its inability in balancing retrieval accuracy and
utilization of hash codes: pushing a system to attain higher mAP will
inevitably lead to poorer utilization of the hash codes. Poor utilization of
the hash codes hinders good retrieval because of increased collision of samples
in the hash space. This means that a model giving a higher mAP values does not
necessarily do a better job in retrieval. In this paper, we introduce a new
metric named Mean Local Group Average Precision (mLGAP) for better evaluation
of the performance of hashing-based retrieval. The new metric provides a
retrieval performance measure that also reconciles the utilization of hash
codes, leading to a more practically meaningful performance metric than
conventional ones like mAP. To this end, we start by mathematical analysis of
the deficiencies of mAP for hashing-based retrieval. We then propose mLGAP and
show why it is more appropriate for hashing-based retrieval. Experiments on
image retrieval are used to demonstrate the effectiveness of the proposed
metric.
Learning to Activate Relay Nodes: Deep Reinforcement Learning Approach
Authors: Minhae Kwon, Juhyeon Lee, Hyunggon Park
Date: 2018-11-24 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose a distributed solution to design a multi-hop ad hoc
network where mobile relay nodes strategically determine their wireless
transmission ranges based on a deep reinforcement learning approach. We
consider scenarios where only a limited networking infrastructure is available
but a large number of wireless mobile relay nodes are deployed in building a
multi-hop ad hoc network to deliver source data to the destination. A mobile
relay node is considered as a decision-making agent that strategically
determines its transmission range in a way that maximizes network throughput
while minimizing the corresponding transmission power consumption. Each relay
node collects information from its partial observations and learns its
environment through a sequence of experiences. Hence, the proposed solution
requires only a minimal amount of information from the system. We show that the
actions that the relay nodes take from its policy are determined as to activate
or inactivate its transmission, i.e., only necessary relay nodes are activated
with the maximum transmit power, and nonessential nodes are deactivated to
minimize power consumption. Using extensive experiments, we confirm that the
proposed solution builds a network with higher network performance than current
state-of-the-art solutions in terms of system goodput and connectivity ratio.
Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence
Authors: Xiu Yang, David Barajas-Solano, Guzel Tartakovsky, Alexandre Tartakovsky
Date: 2018-11-24 00:00:00
Tasks: Active Learning, Gaussian Processes, GPR, regression

In this work, we propose a new Gaussian process regression (GPR)-based
multifidelity method: physics-informed CoKriging (CoPhIK). In CoKriging-based
multifidelity methods, the quantities of interest are modeled as linear
combinations of multiple parameterized stationary Gaussian processes (GPs), and
the hyperparameters of these GPs are estimated from data via optimization. In
CoPhIK, we construct a GP representing low-fidelity data using physics-informed
Kriging (PhIK), and model the discrepancy between low- and high-fidelity data
using a parameterized GP with hyperparameters identified via optimization. Our
approach reduces the cost of optimization for inferring hyperparameters by
incorporating partial physical knowledge. We prove that the physical
constraints in the form of deterministic linear operators are satisfied up to
an error bound. Furthermore, we combine CoPhIK with a greedy active learning
algorithm for guiding the selection of additional observation locations. The
efficiency and accuracy of CoPhIK are demonstrated for reconstructing the
partially observed modified Branin function, reconstructing the sparsely
observed state of a steady state heat transport problem, and learning a
conservative tracer distribution from sparse tracer concentration measurements.
Estimation of Inter-Sentiment Correlations Employing Deep Neural Network Models
Authors: Xinzhi Wang, Shengcheng Yuan, HUI ZHANG, Yi Liu
Date: 2018-11-24 00:00:00
Tasks: Articles

This paper focuses on sentiment mining and sentiment correlation analysis of
web events. Although neural network models have contributed a lot to mining
text information, little attention is paid to analysis of the inter-sentiment
correlations. This paper fills the gap between sentiment calculation and
inter-sentiment correlations. In this paper, the social emotion is divided into
six categories: love, joy, anger, sadness, fear, and surprise. Two deep neural
network models are presented for sentiment calculation. Three datasets - the
titles, the bodies, the comments of news articles - are collected, covering
both objective and subjective texts in varying lengths (long and short). From
each dataset, three kinds of features are extracted: explicit expression,
implicit expression, and alphabet characters. The performance of the two models
are analyzed, with respect to each of the three kinds of the features. There is
controversial phenomenon on the interpretation of anger (fn) and love (gd). In
subjective text, other emotions are easily to be considered as anger. By
contrast, in objective news bodies and titles, it is easy to regard text as
caused love (gd). It means, journalist may want to arouse emotion love by
writing news, but cause anger after the news is published. This result reflects
the sentiment complexity and unpredictability.
Characterizing and Avoiding Negative Transfer
Authors: Zirui Wang, Zihang Dai, Barnabás Póczos, Jaime Carbonell
Date: 2018-11-24 00:00:00
Proceeding: CVPR 2019 6
Tasks: Transfer Learning

When labeled data is scarce for a specific target task, transfer learning often offers an effective solution by utilizing data from a related source task. However, when transferring knowledge from a less related source, it may inversely hurt the target performance, a phenomenon known as negative transfer. Despite its pervasiveness, negative transfer is usually described in an informal manner, lacking rigorous definition, careful analysis, or systematic treatment. This paper proposes a formal definition of negative transfer and analyzes three important aspects thereof. Stemming from this analysis, a novel technique is proposed to circumvent negative transfer by filtering out unrelated source data. Based on adversarial networks, the technique is highly generic and can be applied to a wide range of transfer learning algorithms. The proposed approach is evaluated on six state-of-the-art deep transfer methods via experiments on four benchmark datasets with varying levels of difficulty. Empirically, the proposed method consistently improves the performance of all baseline methods and largely avoids negative transfer, even when the source data is degenerate.
Automating Motion Correction in Multishot MRI Using Generative Adversarial Networks
Authors: Siddique Latif, Muhammad Asim, Muhammad Usman, Junaid Qadir, Rajib Rana
Date: 2018-11-24 00:00:00
Tasks: Generative Adversarial Network, Image Reconstruction, Motion Correction In Multishot Mri

Multishot Magnetic Resonance Imaging (MRI) has recently gained popularity as
it accelerates the MRI data acquisition process without compromising the
quality of final MR image. However, it suffers from motion artifacts caused by
patient movements which may lead to misdiagnosis. Modern state-of-the-art
motion correction techniques are able to counter small degree motion, however,
their adoption is hindered by their time complexity. This paper proposes a
Generative Adversarial Network (GAN) for reconstructing motion free
high-fidelity images while reducing the image reconstruction time by an
impressive two orders of magnitude.
Amortized Bayesian inference for clustering models
Authors: Ari Pakman, Liam Paninski
Date: 2018-11-24 00:00:00
Tasks: Bayesian Inference, Clustering

We develop methods for efficient amortized approximate Bayesian inference
over posterior distributions of probabilistic clustering models, such as
Dirichlet process mixture models. The approach is based on mapping distributed,
symmetry-invariant representations of cluster arrangements into conditional
probabilities. The method parallelizes easily, yields iid samples from the
approximate posterior of cluster assignments with the same computational cost
of a single Gibbs sampler sweep, and can easily be applied to both conjugate
and non-conjugate models, as training only requires samples from the generative
model.
Divergence Prior and Vessel-tree Reconstruction
Authors: Zhongwen Zhang, Egor Chesakov, Dmitrii Marin, Yuri Boykov
Date: 2018-11-24 00:00:00
Proceeding: CVPR 2019 6

We propose a new geometric regularization principle for reconstructing vector
fields based on prior knowledge about their divergence. As one important
example of this general idea, we focus on vector fields modelling blood flow
pattern that should be divergent in arteries and convergent in veins. We show
that this previously ignored regularization constraint can significantly
improve the quality of vessel tree reconstruction particularly around
bifurcations where non-zero divergence is concentrated. Our divergence prior is
critical for resolving (binary) sign ambiguity in flow orientations produced by
standard vessel filters, e.g. Frangi. Our vessel tree centerline reconstruction
combines divergence constraints with robust curvature regularization. Our
unsupervised method can reconstruct complete vessel trees with near-capillary
details on synthetic and real 3D volumes.
Connecting the Dots Between MLE and RL for Sequence Prediction
Authors: Bowen Tan, Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Eric Xing
Date: 2018-11-24 00:00:00
Tasks: Imitation Learning, Machine Translation, Reinforcement Learning, Text Summarization, Translation

Sequence prediction models can be learned from example sequences with a variety of training algorithms. Maximum likelihood learning is simple and efficient, yet can suffer from compounding error at test time. Reinforcement learning such as policy gradient addresses the issue but can have prohibitively poor exploration efficiency. A rich set of other algorithms such as RAML, SPG, and data noising, have also been developed from different perspectives. This paper establishes a formal connection between these algorithms. We present a generalized entropy regularized policy optimization formulation, and show that the apparently distinct algorithms can all be reformulated as special instances of the framework, with the only difference being the configurations of a reward function and a couple of hyperparameters. The unified interpretation offers a systematic view of the varying properties of exploration and learning efficiency. Besides, inspired from the framework, we present a new algorithm that dynamically interpolates among the family of algorithms for scheduled sequence model learning. Experiments on machine translation, text summarization, and game imitation learning demonstrate the superiority of the proposed algorithm.
Frustrated with Replicating Claims of a Shared Model? A Solution
Authors: Abdul Dakkak, Cheng Li, JinJun Xiong, Wen-mei Hwu
Date: 2018-11-24 00:00:00

Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that model owners and evaluators are hard-pressed analyzing and studying them. This is exacerbated by the complicated procedures for evaluation. The lack of standard systems and efficient techniques for specifying and provisioning ML/DL evaluation is the main cause of this "pain point". This work discusses common pitfalls for replicating DL model evaluation, and shows that these subtle pitfalls can affect both accuracy and performance. It then proposes a solution to remedy these pitfalls called MLModelScope, a specification for repeatable model evaluation and a runtime to provision and measure experiments. We show that by easing the model specification and evaluation process, MLModelScope facilitates rapid adoption of ML/DL innovations.
A Multi-variable Stacked Long-Short Term Memory Network for Wind Speed Forecasting
Authors: Sisheng Liang, Long Nguyen, Fang Jin
Date: 2018-11-24 00:00:00

Precisely forecasting wind speed is essential for wind power producers and
grid operators. However, this task is challenging due to the stochasticity of
wind speed. To accurately predict short-term wind speed under uncertainties,
this paper proposed a multi-variable stacked LSTMs model (MSLSTM). The proposed
method utilizes multiple historical meteorological variables, such as wind
speed, temperature, humidity, pressure, dew point and solar radiation to
accurately predict wind speeds. The prediction performance is extensively
assessed using real data collected in West Texas, USA. The experimental results
show that the proposed MSLSTM can preferably capture and learn uncertainties
while output competitive performance.
Generate, Segment and Refine: Towards Generic Manipulation Segmentation
Authors: Peng Zhou, Bor-Chun Chen, Xintong Han, Mahyar Najibi, Abhinav Shrivastava, Ser Nam Lim, Larry S. Davis
Date: 2018-11-24 00:00:00
Tasks: Detecting Image Manipulation, Image Generation, Image Manipulation Detection, Misinformation, Semantic Segmentation

Detecting manipulated images has become a significant emerging challenge. The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet. While the intent behind such manipulations varies widely, concerns on the spread of fake news and misinformation is growing. Current state of the art methods for detecting these manipulated images suffers from the lack of training data due to the laborious labeling process. We address this problem in this paper, for which we introduce a manipulated image generation process that creates true positives using currently available datasets. Drawing from traditional work on image blending, we propose a novel generator for creating such examples. In addition, we also propose to further create examples that force the algorithm to focus on boundary artifacts during training. Strong experimental results validate our proposal.
Interpretable Convolutional Filters with SincNet
Authors: Mirco Ravanelli, Yoshua Bengio
Date: 2018-11-23 00:00:00
Tasks: Distant Speech Recognition, Inductive Bias, speech-recognition, Speech Recognition

Deep learning is currently playing a crucial role toward higher levels of artificial intelligence. This paradigm allows neural networks to learn complex and abstract representations, that are progressively obtained by combining simpler ones. Nevertheless, the internal "black-box" representations automatically discovered by current neural architectures often suffer from a lack of interpretability, making of primary interest the study of explainable machine learning techniques. This paper summarizes our recent efforts to develop a more interpretable neural model for directly processing speech from the raw waveform. In particular, we propose SincNet, a novel Convolutional Neural Network (CNN) that encourages the first layer to discover more meaningful filters by exploiting parametrized sinc functions. In contrast to standard CNNs, which learn all the elements of each filter, only low and high cutoff frequencies of band-pass filters are directly learned from data. This inductive bias offers a very compact way to derive a customized filter-bank front-end, that only depends on some parameters with a clear physical meaning. Our experiments, conducted on both speaker and speech recognition, show that the proposed architecture converges faster, performs better, and is more interpretable than standard CNNs.
3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys
Authors: Rahul Singh, Aayush Sharma, Onur Rauf Bingol, Aditya Balu, Ganesh Balasubramanian, Duane D. Johnson, Soumik Sarkar
Date: 2018-11-23 00:00:00

The need for advanced materials has led to the development of complex,
multi-component alloys or solid-solution alloys. These materials have shown
exceptional properties like strength, toughness, ductility, electrical and
electronic properties. Current development of such material systems are
hindered by expensive experiments and computationally demanding
first-principles simulations. Atomistic simulations can provide reasonable
insights on properties in such material systems. However, the issue of
designing robust potentials still exists. In this paper, we explore a deep
convolutional neural-network based approach to develop the atomistic potential
for such complex alloys to investigate materials for insights into controlling
properties. In the present work, we propose a voxel representation of the
atomic configuration of a cell and design a 3D convolutional neural network to
learn the interaction of the atoms. Our results highlight the performance of
the 3D convolutional neural network and its efficacy in machine-learning the
atomistic potential. We also explore the role of voxel resolution and provide
insights into the two bounding box methodologies implemented for voxelization.
Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior
Authors: Tathagata Chakraborti, Anagha Kulkarni, Sarath Sreedharan, David E. Smith, Subbarao Kambhampati
Date: 2018-11-23 00:00:00

There has been significant interest of late in generating behavior of agents
that is interpretable to the human (observer) in the loop. However, the work in
this area has typically lacked coherence on the topic, with proposed solutions
for "explicable", "legible", "predictable" and "transparent" planning with
overlapping, and sometimes conflicting, semantics all aimed at some notion of
understanding what intentions the observer will ascribe to an agent by
observing its behavior. This is also true for the recent works on "security"
and "privacy" of plans which are also trying to answer the same question, but
from the opposite point of view -- i.e. when the agent is trying to hide
instead of revealing its intentions. This paper attempts to provide a workable
taxonomy of relevant concepts in this exciting and emerging field of inquiry.
Representer Point Selection for Explaining Deep Neural Networks
Authors: Chih-Kuan Yeh, Joon Sik Kim, Ian E. H. Yen, Pradeep Ravikumar
Date: 2018-11-23 00:00:00
Proceeding: NeurIPS 2018 12

We propose to explain the predictions of a deep neural network, by pointing
to the set of what we call representer points in the training set, for a given
test point prediction. Specifically, we show that we can decompose the
pre-activation prediction of a neural network into a linear combination of
activations of training points, with the weights corresponding to what we call
representer values, which thus capture the importance of that training point on
the learned parameters of the network. But it provides a deeper understanding
of the network than simply training point influence: with positive representer
values corresponding to excitatory training points, and negative values
corresponding to inhibitory points, which as we show provides considerably more
insight. Our method is also much more scalable, allowing for real-time feedback
in a manner not feasible with influence functions.
Robustness via curvature regularization, and vice versa
Authors: Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, Pascal Frossard
Date: 2018-11-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Adversarial Robustness

State-of-the-art classifiers have been shown to be largely vulnerable to
adversarial perturbations. One of the most effective strategies to improve
robustness is adversarial training. In this paper, we investigate the effect of
adversarial training on the geometry of the classification landscape and
decision boundaries. We show in particular that adversarial training leads to a
significant decrease in the curvature of the loss surface with respect to
inputs, leading to a drastically more "linear" behaviour of the network. Using
a locally quadratic approximation, we provide theoretical evidence on the
existence of a strong relation between large robustness and small curvature. To
further show the importance of reduced curvature for improving the robustness,
we propose a new regularizer that directly minimizes curvature of the loss
surface, and leads to adversarial robustness that is on par with adversarial
training. Besides being a more efficient and principled alternative to
adversarial training, the proposed regularizer confirms our claims on the
importance of exhibiting quasi-linear behavior in the vicinity of data points
in order to achieve robustness.
Dancing in the Dark: Private Multi-Party Machine Learning in an Untrusted Setting
Authors: Clement Fung, Jamie Koerner, Stewart Grant, Ivan Beschastnikh
Date: 2018-11-23 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning

Distributed machine learning (ML) systems today use an unsophisticated threat
model: data sources must trust a central ML process. We propose a brokered
learning abstraction that allows data sources to contribute towards a
globally-shared model with provable privacy guarantees in an untrusted setting.
We realize this abstraction by building on federated learning, the state of the
art in multi-party ML, to construct TorMentor: an anonymous hidden service that
supports private multi-party ML.
  We define a new threat model by characterizing, developing and evaluating new
attacks in the brokered learning setting, along with new defenses for these
attacks. We show that TorMentor effectively protects data providers against
known ML attacks while providing them with a tunable trade-off between model
accuracy and privacy. We evaluate TorMentor with local and geo-distributed
deployments on Azure/Tor. In an experiment with 200 clients and 14 MB of data
per client, our prototype trained a logistic regression model using stochastic
gradient descent in 65s.
  Code is available at: https://github.com/DistributedML/TorML