pwc-archive/papers-with-abstracts huggingface dataset:
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation
Authors: Chaowei Xiao, Ruizhi Deng, Bo Li, Fisher Yu, Mingyan Liu, Dawn Song
Date: 2018-10-11 00:00:00
Proceeding: ECCV 2018 9
Tasks: General Classification, Segmentation, Semantic Segmentation

Deep Neural Networks (DNNs) have been widely applied in various recognition
tasks. However, recently DNNs have been shown to be vulnerable against
adversarial examples, which can mislead DNNs to make arbitrary incorrect
predictions. While adversarial examples are well studied in classification
tasks, other learning problems may have different properties. For instance,
semantic segmentation requires additional components such as dilated
convolutions and multiscale processing. In this paper, we aim to characterize
adversarial examples based on spatial context information in semantic
segmentation. We observe that spatial consistency information can be
potentially leveraged to detect adversarial examples robustly even when a
strong adaptive attacker has access to the model and detection strategies. We
also show that adversarial examples based on attacks considered within the
paper barely transfer among models, even though transferability is common in
classification. Our observations shed new light on developing adversarial
attacks and defenses to better understand the vulnerabilities of DNNs.
Empowerment-driven Exploration using Mutual Information Estimation
Authors: Navneet Madhu Kumar
Date: 2018-10-11 00:00:00
Tasks: Deep Reinforcement Learning, Montezuma's Revenge, Mutual Information Estimation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Exploration is a difficult challenge in reinforcement learning and is of
prime importance in sparse reward environments. However, many of the state of
the art deep reinforcement learning algorithms, that rely on epsilon-greedy,
fail on these environments. In such cases, empowerment can serve as an
intrinsic reward signal to enable the agent to maximize the influence it has
over the near future. We formulate empowerment as the channel capacity between
states and actions and is calculated by estimating the mutual information
between the actions and the following states. The mutual information is
estimated using Mutual Information Neural Estimator and a forward dynamics
model. We demonstrate that an empowerment driven agent is able to improve
significantly the score of a baseline DQN agent on the game of Montezuma's
Revenge.
Probabilistic Blocking with An Application to the Syrian Conflict
Authors: Rebecca C. Steorts, Anshumali Shrivastava
Date: 2018-10-11 00:00:00
Tasks: Blocking, Entity Resolution, Information Retrieval, Retrieval

Entity resolution seeks to merge databases as to remove duplicate entries
where unique identifiers are typically unknown. We review modern blocking
approaches for entity resolution, focusing on those based upon locality
sensitive hashing (LSH). First, we introduce $k$-means locality sensitive
hashing (KLSH), which is based upon the information retrieval literature and
clusters similar records into blocks using a vector-space representation and
projections. Second, we introduce a subquadratic variant of LSH to the
literature, known as Densified One Permutation Hashing (DOPH). Third, we
propose a weighted variant of DOPH. We illustrate each method on an application
to a subset of the ongoing Syrian conflict, giving a discussion of each method.
Conceptual Knowledge Markup Language: An Introduction
Authors: Robert E. Kent
Date: 2018-10-10 00:00:00
Tasks: Philosophy

Conceptual Knowledge Markup Language (CKML) is an application of XML. Earlier
versions of CKML followed rather exclusively the philosophy of Conceptual
Knowledge Processing (CKP), a principled approach to knowledge representation
and data analysis that "advocates methods and instruments of conceptual
knowledge processing which support people in their rational thinking, judgment
and acting and promote critical discussion." The new version of CKML continues
to follow this approach, but also incorporates various principles, insights and
techniques from Information Flow (IF), the logical design of distributed
systems. Among other things, this allows diverse communities of discourse to
compare their own information structures, as coded in logical theories, with
that of other communities that share a common generic ontology. CKML
incorporates the CKP ideas of concept lattice and formal context, along with
the IF ideas of classification (= formal context), infomorphism, theory,
interpretation and local logic. Ontology Markup Language (OML), a subset of
CKML that is a self-sufficient markup language in its own right, follows the
principles and ideas of Conceptual Graphs (CG). OML is used for structuring the
specifications and axiomatics of metadata into ontologies. OML incorporates the
CG ideas of concept, conceptual relation, conceptual graph, conceptual context,
participants and ontology. The link from OML to CKML is the process of
conceptual scaling, which is the interpretive transformation of ontologically
structured knowledge to conceptual structured knowledge.
Introducing a hybrid model of DEA and data mining in evaluating efficiency. Case study: Bank Branches
Authors: Sara Hosseinzadeh Kassani, Peyman Hosseinzadeh Kassani, Seyed Esmaeel Najafi
Date: 2018-10-10 00:00:00
Tasks: Clustering

The banking industry is very important for an economic cycle of each country
and provides some quality of services for us. With the advancement in
technology and rapidly increasing of the complexity of the business
environment, it has become more competitive than the past so that efficiency
analysis in the banking industry attracts much attention in recent years. From
many aspects, such analyses at the branch level are more desirable. Evaluating
the branch performance with the purpose of eliminating deficiency can be a
crucial issue for branch managers to measure branch efficiency. This work not
only can lead to a better understanding of bank branch performance but also
give further information to enhance managerial decisions to recognize
problematic areas. To achieve this purpose, this study presents an integrated
approach based on Data Envelopment Analysis (DEA), Clustering algorithms and
Polynomial Pattern Classifier for constructing a classifier to identify a class
of bank branches. First, the efficiency estimates of individual branches are
evaluated by using the DEA approach. Next, when the range and number of classes
were identified by experts, the number of clusters is identified by an
agglomerative hierarchical clustering algorithm based on some statistical
methods. Next, we divide our raw data into k clusters By means of
self-organizing map (SOM) neural networks. Finally, all clusters are fed into
the reduced multivariate polynomial model to predict the classes of data.
Automatic Configuration of Deep Neural Networks with EGO
Authors: Bas van Stein, Hao Wang, Thomas Bäck
Date: 2018-10-10 00:00:00
Tasks: Data Augmentation, global-optimization, image-classification, Image Classification

Designing the architecture for an artificial neural network is a cumbersome
task because of the numerous parameters to configure, including activation
functions, layer types, and hyper-parameters. With the large number of
parameters for most networks nowadays, it is intractable to find a good
configuration for a given task by hand. In this paper an Efficient Global
Optimization (EGO) algorithm is adapted to automatically optimize and configure
convolutional neural network architectures. A configurable neural network
architecture based solely on convolutional layers is proposed for the
optimization. Without using any knowledge on the target problem and not using
any data augmentation techniques, it is shown that on several image
classification tasks this approach is able to find competitive network
architectures in terms of prediction accuracy, compared to the best
hand-crafted ones in literature. In addition, a very small training budget (200
evaluations and 10 epochs in training) is spent on each optimized architectures
in contrast to the usual long training time of hand-crafted networks. Moreover,
instead of the standard sequential evaluation in EGO, several candidate
architectures are proposed and evaluated in parallel, which saves the execution
overheads significantly and leads to an efficient automation for deep neural
network design.
Deterministic Pod Repositioning Problem in Robotic Mobile Fulfillment Systems
Authors: Ruslan Krenzler, Lin Xie, Hanyi Li
Date: 2018-10-09 00:00:00

In a robotic mobile fulfillment system, robots bring shelves, called pods,
with storage items from the storage area to pick stations. At every pick
station there is a person -- the picker -- who takes parts from the pod and
packs them into boxes according to orders. Usually there are multiple shelves
at the pick station. In this case, they build a queue with the picker at its
head. When the picker does not need the pod any more, a robot transports the
pod back to the storage area. At that time, we need to answer a question:
"Where is the optimal place in the inventory to put this pod back?". It is a
tough question, because there are many uncertainties to consider before
answering it. Moreover, each decision made to answer the question influences
the subsequent ones. The goal of this paper is to answer the question properly.
We call this problem the Pod Repositioning Problem and formulate a
deterministic model. This model is tested with different algorithms, including
binary integer programming, cheapest place, fixed place, random place, genetic
algorithms, and a novel algorithm called tetris.
Federated Learning for Keyword Spotting
Authors: David Leroy, Alice Coucke, Thibaut Lavril, Thibault Gisselbrecht, Joseph Dureau
Date: 2018-10-09 00:00:00
Tasks: Federated Learning, Keyword Spotting

We propose a practical approach based on federated learning to solve
out-of-domain issues with continuously running embedded speech-based models
such as wake word detectors. We conduct an extensive empirical study of the
federated averaging algorithm for the "Hey Snips" wake word based on a
crowdsourced dataset that mimics a federation of wake word users. We
empirically demonstrate that using an adaptive averaging strategy inspired from
Adam in place of standard weighted model averaging highly reduces the number of
communication rounds required to reach our target performance. The associated
upstream communication costs per user are estimated at 8 MB, which is a
reasonable in the context of smart home voice assistants. Additionally, the
dataset used for these experiments is being open sourced with the aim of
fostering further transparent research in the application of federated learning
to speech data.
Robust Neural Abstractive Summarization Systems and Evaluation against Adversarial Information
Authors: Lisa Fan, Dong Yu, Lu Wang
Date: 2018-10-14 00:00:00
Tasks: Abstractive Text Summarization

Sequence-to-sequence (seq2seq) neural models have been actively investigated
for abstractive summarization. Nevertheless, existing neural abstractive
systems frequently generate factually incorrect summaries and are vulnerable to
adversarial information, suggesting a crucial lack of semantic understanding.
In this paper, we propose a novel semantic-aware neural abstractive
summarization model that learns to generate high quality summaries through
semantic interpretation over salient content. A novel evaluation scheme with
adversarial samples is introduced to measure how well a model identifies
off-topic information, where our model yields significantly better performance
than the popular pointer-generator summarizer. Human evaluation also confirms
that our system summaries are uniformly more informative and faithful as well
as less redundant than the seq2seq model.
MeanSum: A Neural Model for Unsupervised Multi-document Abstractive Summarization
Authors: Eric Chu, Peter J. Liu
Date: 2018-10-12 00:00:00
Tasks: Abstractive Text Summarization

Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples. However, such datasets are rare and the models trained from them do not generalize to other domains. Recently, some progress has been made in learning sequence-to-sequence mappings with only unpaired examples. In our work, we consider the setting where there are only documents (product or business reviews) with no summaries provided, and propose an end-to-end, neural model architecture to perform unsupervised abstractive summarization. Our proposed model consists of an auto-encoder where the mean of the representations of the input reviews decodes to a reasonable summary-review while not relying on any review-specific features. We consider variants of the proposed architecture and perform an ablation study to show the importance of specific components. We show through automated metrics and human evaluation that the generated summaries are highly abstractive, fluent, relevant, and representative of the average sentiment of the input reviews. Finally, we collect a reference evaluation dataset and show that our model outperforms a strong extractive baseline.
Poincaré GloVe: Hyperbolic Word Embeddings
Authors: Alexandru Tifrea, Gary Bécigneul, Octavian-Eugen Ganea
Date: 2018-10-15 00:00:00
Tasks: Word Embeddings

Words are not created equal. In fact, they form an aristocratic graph with a
latent hierarchical structure that the next generation of unsupervised learned
word embeddings should reveal. In this paper, justified by the notion of
delta-hyperbolicity or tree-likeliness of a space, we propose to embed words in
a Cartesian product of hyperbolic spaces which we theoretically connect to the
Gaussian word embeddings and their Fisher geometry. This connection allows us
to introduce a novel principled hypernymy score for word embeddings. Moreover,
we adapt the well-known Glove algorithm to learn unsupervised word embeddings
in this type of Riemannian manifolds. We further explain how to solve the
analogy task using the Riemannian parallel transport that generalizes vector
arithmetics to this new type of geometry. Empirically, based on extensive
experiments, we prove that our embeddings, trained unsupervised, are the first
to simultaneously outperform strong and popular baselines on the tasks of
similarity, analogy and hypernymy detection. In particular, for word hypernymy,
we obtain new state-of-the-art on fully unsupervised WBLESS classification
accuracy.
Deep Imitative Models for Flexible Inference, Planning, and Control
Authors: Nicholas Rhinehart, Rowan Mcallister, Sergey Levine
Date: 2018-10-15 00:00:00
Proceeding: ICLR 2020 1
Tasks: Autonomous Driving, Imitation Learning

Imitation Learning (IL) is an appealing approach to learn desirable autonomous behavior. However, directing IL to achieve arbitrary goals is difficult. In contrast, planning-based algorithms use dynamics models and reward functions to achieve goals. Yet, reward functions that evoke desirable behavior are often difficult to specify. In this paper, we propose Imitative Models to combine the benefits of IL and goal-directed planning. Imitative Models are probabilistic predictive models of desirable behavior able to plan interpretable expert-like trajectories to achieve specified goals. We derive families of flexible goal objectives, including constrained goal regions, unconstrained goal sets, and energy-based goals. We show that our method can use these objectives to successfully direct behavior. Our method substantially outperforms six IL approaches and a planning-based approach in a dynamic simulated autonomous driving task, and is efficiently learned from expert demonstrations without online data collection. We also show our approach is robust to poorly specified goals, such as goals on the wrong side of the road.
Visual Semantic Navigation using Scene Priors
Authors: Wei Yang, Xiaolong Wang, Ali Farhadi, Abhinav Gupta, Roozbeh Mottaghi
Date: 2018-10-15 00:00:00
Proceeding: ICLR 2019 5
Tasks: Deep Reinforcement Learning, Navigate, Reinforcement Learning

How do humans navigate to target objects in novel scenes? Do we use the
semantic/functional priors we have built over years to efficiently search and
navigate? For example, to search for mugs, we search cabinets near the coffee
machine and for fruits we try the fridge. In this work, we focus on
incorporating semantic priors in the task of semantic navigation. We propose to
use Graph Convolutional Networks for incorporating the prior knowledge into a
deep reinforcement learning framework. The agent uses the features from the
knowledge graph to predict the actions. For evaluation, we use the AI2-THOR
framework. Our experiments show how semantic knowledge improves performance
significantly. More importantly, we show improvement in generalization to
unseen scenes and/or objects. The supplementary video can be accessed at the
following link: https://youtu.be/otKjuO805dE .
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
Authors: David Janz, Jiri Hron, Przemysław Mazur, Katja Hofmann, José Miguel Hernández-Lobato, Sebastian Tschiatschek
Date: 2018-10-15 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Posterior sampling for reinforcement learning (PSRL) is an effective method for balancing exploration and exploitation in reinforcement learning. Randomised value functions (RVF) can be viewed as a promising approach to scaling PSRL. However, we show that most contemporary algorithms combining RVF with neural network function approximation do not possess the properties which make PSRL effective, and provably fail in sparse reward problems. Moreover, we find that propagation of uncertainty, a property of PSRL previously thought important for exploration, does not preclude this failure. We use these insights to design Successor Uncertainties (SU), a cheap and easy to implement RVF algorithm that retains key properties of PSRL. SU is highly effective on hard tabular exploration benchmarks. Furthermore, on the Atari 2600 domain, it surpasses human performance on 38 of 49 games tested (achieving a median human normalised score of 2.09), and outperforms its closest RVF competitor, Bootstrapped DQN, on 36 of those.
Structured Content Preservation for Unsupervised Text Style Transfer
Authors: Youzhi Tian, Zhiting Hu, Zhou Yu
Date: 2018-10-15 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence, Style Transfer, Text Style Transfer, Unsupervised Text Style Transfer

Text style transfer aims to modify the style of a sentence while keeping its
content unchanged. Recent style transfer systems often fail to faithfully
preserve the content after changing the style. This paper proposes a structured
content preserving model that leverages linguistic information in the
structured fine-grained supervisions to better preserve the style-independent
content during style transfer. In particular, we achieve the goal by devising
rich model objectives based on both the sentence's lexical information and a
language model that conditions on content. The resulting model therefore is
encouraged to retain the semantic meaning of the target sentences. We perform
extensive experiments that compare our model to other existing approaches in
the tasks of sentiment and political slant transfer. Our model achieves
significant improvement in terms of both content preservation and style
transfer in automatic and human evaluation.
Factorized Machine Self-Confidence for Decision-Making Agents
Authors: Brett W. Israelsen, Nisar R. Ahmed, Eric Frew, Dale Lawrence, Brian Argrow
Date: 2018-10-15 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Reinforcement Learning

Algorithmic assurances from advanced autonomous systems assist human users in
understanding, trusting, and using such systems appropriately. Designing these
systems with the capacity of assessing their own capabilities is one approach
to creating an algorithmic assurance. The idea of `machine self-confidence' is
introduced for autonomous systems. Using a factorization based framework for
self-confidence assessment, one component of self-confidence, called
`solver-quality', is discussed in the context of Markov decision processes for
autonomous systems. Markov decision processes underlie much of the theory of
reinforcement learning, and are commonly used for planning and decision making
under uncertainty in robotics and autonomous systems. A `solver quality' metric
is formally defined in the context of decision making algorithms based on
Markov decision processes. A method for assessing solver quality is then
derived, drawing inspiration from empirical hardness models. Finally, numerical
experiments for an unmanned autonomous vehicle navigation problem under
different solver, parameter, and environment conditions indicate that the
self-confidence metric exhibits the desired properties. Discussion of results,
and avenues for future investigation are included.
Deep Surface Light Fields
Authors: Anpei Chen, Minye Wu, Yingliang Zhang, Nianyi Li, Jie Lu, Shenghua Gao, Jingyi Yu
Date: 2018-10-15 00:00:00
Tasks: Data Compression, GPU, Image Registration

A surface light field represents the radiance of rays originating from any
points on the surface in any directions. Traditional approaches require
ultra-dense sampling to ensure the rendering quality. In this paper, we present
a novel neural network based technique called deep surface light field or DSLF
to use only moderate sampling for high fidelity rendering. DSLF automatically
fills in the missing data by leveraging different sampling patterns across the
vertices and at the same time eliminates redundancies due to the network's
prediction capability. For real data, we address the image registration problem
as well as conduct texture-aware remeshing for aligning texture edges with
vertices to avoid blurring. Comprehensive experiments show that DSLF can
further achieve high data compression ratio while facilitating real-time
rendering on the GPU.
Predictor-Corrector Policy Optimization
Authors: Ching-An Cheng, Xinyan Yan, Nathan Ratliff, Byron Boots
Date: 2018-10-15 00:00:00
Tasks: Imitation Learning

We present a predictor-corrector framework, called PicCoLO, that can transform a first-order model-free reinforcement or imitation learning algorithm into a new hybrid method that leverages predictive models to accelerate policy learning. The new "PicCoLOed" algorithm optimizes a policy by recursively repeating two steps: In the Prediction Step, the learner uses a model to predict the unseen future gradient and then applies the predicted estimate to update the policy; in the Correction Step, the learner runs the updated policy in the environment, receives the true gradient, and then corrects the policy using the gradient error. Unlike previous algorithms, PicCoLO corrects for the mistakes of using imperfect predicted gradients and hence does not suffer from model bias. The development of PicCoLO is made possible by a novel reduction from predictable online learning to adversarial online learning, which provides a systematic way to modify existing first-order algorithms to achieve the optimal regret with respect to predictable information. We show, in both theory and simulation, that the convergence rate of several first-order model-free algorithms can be improved by PicCoLO.