pwc-archive/papers-with-abstracts huggingface dataset:
The Regretful Agent: Heuristic-Aided Navigation through Progress Estimation
Authors: Chih-Yao Ma, Zuxuan Wu, Ghassan AlRegib, Caiming Xiong, Zsolt Kira
Date: 2019-03-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decision Making, Vision and Language Navigation, Vision-Language Navigation, Visual Navigation

As deep learning continues to make progress for challenging perception tasks,
there is increased interest in combining vision, language, and decision-making.
Specifically, the Vision and Language Navigation (VLN) task involves navigating
to a goal purely from language instructions and visual information without
explicit knowledge of the goal. Recent successful approaches have made in-roads
in achieving good success rates for this task but rely on beam search, which
thoroughly explores a large number of trajectories and is unrealistic for
applications such as robotics. In this paper, inspired by the intuition of
viewing the problem as search on a navigation graph, we propose to use a
progress monitor developed in prior work as a learnable heuristic for search.
We then propose two modules incorporated into an end-to-end architecture: 1) A
learned mechanism to perform backtracking, which decides whether to continue
moving forward or roll back to a previous state (Regret Module) and 2) A
mechanism to help the agent decide which direction to go next by showing
directions that are visited and their associated progress estimate (Progress
Marker). Combined, the proposed approach significantly outperforms current
state-of-the-art methods using greedy action selection, with 5% absolute
improvement on the test server in success rates, and more importantly 8% on
success rates normalized by the path length. Our code is available at
https://github.com/chihyaoma/regretful-agent .
A Dual Embedding Space Model for Document Ranking
Authors: Bhaskar Mitra, Eric Nalisnick, Nick Craswell, Rich Caruana
Date: 2016-02-02 00:00:00
Tasks: Document Ranking, model, Word Embeddings

A fundamental goal of search engines is to identify, given a query, documents
that have relevant text. This is intrinsically difficult because the query and
the document may use different vocabulary, or the document may contain query
words without being relevant. We investigate neural word embeddings as a source
of evidence in document ranking. We train a word2vec embedding model on a large
unlabelled query corpus, but in contrast to how the model is commonly used, we
retain both the input and the output projections, allowing us to leverage both
the embedding spaces to derive richer distributional relationships. During
ranking we map the query words into the input space and the document words into
the output space, and compute a query-document relevance score by aggregating
the cosine similarities across all the query-document word pairs.
  We postulate that the proposed Dual Embedding Space Model (DESM) captures
evidence on whether a document is about a query term in addition to what is
modelled by traditional term-frequency based approaches. Our experiments show
that the DESM can re-rank top documents returned by a commercial Web search
engine, like Bing, better than a term-matching based signal like TF-IDF.
However, when ranking a larger set of candidate documents, we find the
embeddings-based approach is prone to false positives, retrieving documents
that are only loosely related to the query. We demonstrate that this problem
can be solved effectively by ranking based on a linear mixture of the DESM and
the word counting features.
Learning a smooth kernel regularizer for convolutional neural networks
Authors: Reuben Feinman, Brenden M. Lake
Date: 2019-03-05 00:00:00
Tasks: L2 Regularization, Object Recognition

Modern deep neural networks require a tremendous amount of data to train,
often needing hundreds or thousands of labeled examples to learn an effective
representation. For these networks to work with less data, more structure must
be built into their architectures or learned from previous experience. The
learned weights of convolutional neural networks (CNNs) trained on large
datasets for object recognition contain a substantial amount of structure.
These representations have parallels to simple cells in the primary visual
cortex, where receptive fields are smooth and contain many regularities.
Incorporating smoothness constraints over the kernel weights of modern CNN
architectures is a promising way to improve their sample complexity. We propose
a smooth kernel regularizer that encourages spatial correlations in convolution
kernel weights. The correlation parameters of this regularizer are learned from
previous experience, yielding a method with a hierarchical Bayesian
interpretation. We show that our correlated regularizer can help constrain
models for visual recognition, improving over an L2 regularization baseline.
Statistical Guarantees for the Robustness of Bayesian Neural Networks
Authors: Luca Cardelli, Marta Kwiatkowska, Luca Laurenti, Nicola Paoletti, Andrea Patane, Matthew Wicker
Date: 2019-03-05 00:00:00
Tasks: General Classification, image-classification, Image Classification

We introduce a probabilistic robustness measure for Bayesian Neural Networks
(BNNs), defined as the probability that, given a test point, there exists a
point within a bounded set such that the BNN prediction differs between the
two. Such a measure can be used, for instance, to quantify the probability of
the existence of adversarial examples. Building on statistical verification
techniques for probabilistic models, we develop a framework that allows us to
estimate probabilistic robustness for a BNN with statistical guarantees, i.e.,
with a priori error and confidence bounds. We provide experimental comparison
for several approximate BNN inference techniques on image classification tasks
associated to MNIST and a two-class subset of the GTSRB dataset. Our results
enable quantification of uncertainty of BNN predictions in adversarial
settings.
Gradient Coding with Clustering and Multi-message Communication
Authors: Emre Ozfatura, Deniz Gunduz, Sennur Ulukus
Date: 2019-03-05 00:00:00
Tasks: Clustering, Distributed Computing

Gradient descent (GD) methods are commonly employed in machine learning
problems to optimize the parameters of the model in an iterative fashion. For
problems with massive datasets, computations are distributed to many parallel
computing servers (i.e., workers) to speed up GD iterations. While distributed
computing can increase the computation speed significantly, the per-iteration
completion time is limited by the slowest straggling workers. Coded distributed
computing can mitigate straggling workers by introducing redundant
computations; however, existing coded computing schemes are mainly designed
against persistent stragglers, and partial computations at straggling workers
are discarded, leading to wasted computational capacity. In this paper, we
propose a novel gradient coding (GC) scheme which allows multiple coded
computations to be conveyed from each worker to the master per iteration. We
numerically show that the proposed GC with multi-message communication (MMC)
together with clustering provides significant improvements in the average
completion time (of each iteration), with minimal or no increase in the
communication load.
PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers
Authors: Saeed Amizadeh, Sergiy Matusevych, Markus Weimer
Date: 2019-03-05 00:00:00
Tasks: Graph Neural Network

There have been recent efforts for incorporating Graph Neural Network models
for learning full-stack solvers for constraint satisfaction problems (CSP) and
particularly Boolean satisfiability (SAT). Despite the unique representational
power of these neural embedding models, it is not clear how the search strategy
in the learned models actually works. On the other hand, by fixing the search
strategy (e.g. greedy search), we would effectively deprive the neural models
of learning better strategies than those given. In this paper, we propose a
generic neural framework for learning CSP solvers that can be described in
terms of probabilistic inference and yet learn search strategies beyond greedy
search. Our framework is based on the idea of propagation, decimation and
prediction (and hence the name PDP) in graphical models, and can be trained
directly toward solving CSP in a fully unsupervised manner via energy
minimization, as shown in the paper. Our experimental results demonstrate the
effectiveness of our framework for SAT solving compared to both neural and the
state-of-the-art baselines.
Dealing with Qualitative and Quantitative Features in Legal Domains
Authors: Maximiliano C. D. Budán, María Laura Cobo, Diego I. Martínez, Antonino Rotolo
Date: 2019-03-05 00:00:00

In this work, we enrich a formalism for argumentation by including a formal
characterization of features related to the knowledge, in order to capture
proper reasoning in legal domains. We add meta-data information to the
arguments in the form of labels representing quantitative and qualitative data
about them. These labels are propagated through an argumentative graph
according to the relations of support, conflict, and aggregation between
arguments.
Complexity Results and Algorithms for Bipolar Argumentation
Authors: Amin Karamlou, Kristijonas Čyras, Francesca Toni
Date: 2019-03-05 00:00:00

Bipolar Argumentation Frameworks (BAFs) admit several interpretations of the
support relation and diverging definitions of semantics. Recently, several
classes of BAFs have been captured as instances of bipolar Assumption-Based
Argumentation, a class of Assumption-Based Argumentation (ABA). In this paper,
we establish the complexity of bipolar ABA, and consequently of several classes
of BAFs. In addition to the standard five complexity problems, we analyse the
rarely-addressed extension enumeration problem too. We also advance
backtracking-driven algorithms for enumerating extensions of bipolar ABA
frameworks, and consequently of BAFs under several interpretations. We prove
soundness and completeness of our algorithms, describe their implementation and
provide a scalability evaluation. We thus contribute to the study of the as yet
uninvestigated complexity problems of (variously interpreted) BAFs as well as
of bipolar ABA, and provide the lacking implementations thereof.
Learning Exploration Policies for Navigation
Authors: Tao Chen, Saurabh Gupta, Abhinav Gupta
Date: 2019-03-05 00:00:00
Proceeding: ICLR 2019 5
Tasks: Efficient Exploration, General Reinforcement Learning, Imitation Learning, Visual Navigation

Numerous past works have tackled the problem of task-driven navigation. But,
how to effectively explore a new environment to enable a variety of down-stream
tasks has received much less attention. In this work, we study how agents can
autonomously explore realistic and complex 3D environments without the context
of task-rewards. We propose a learning-based approach and investigate different
policy architectures, reward functions, and training paradigms. We find that
the use of policies with spatial memory that are bootstrapped with imitation
learning and finally finetuned with coverage rewards derived purely from
on-board sensors can be effective at exploring novel environments. We show that
our learned exploration policies can explore better than classical approaches
based on geometry alone and generic learning-based exploration techniques.
Finally, we also show how such task-agnostic exploration can be used for
down-stream tasks. Code and Videos are available at:
https://sites.google.com/view/exploration-for-nav.
TableBank: A Benchmark Dataset for Table Detection and Recognition
Authors: Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou, Zhoujun Li
Date: 2019-03-05 00:00:00
Proceeding: LREC 2020 5
Tasks: Table Detection

We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet. Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousand human-labeled examples, which is difficult to generalize on real-world applications. With TableBank that contains 417K high quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks. We make TableBank publicly available and hope it will empower more deep learning approaches in the table detection and recognition task. The dataset and models are available at \url{https://github.com/doc-analysis/TableBank}.
MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
Authors: Yazan Abu Farha, Juergen Gall
Date: 2019-03-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Segmentation, Segmentation, Temporal Action Segmentation

Temporally locating and classifying action segments in long untrimmed videos
is of particular interest to many applications like surveillance and robotics.
While traditional approaches follow a two-step pipeline, by generating
frame-wise probabilities and then feeding them to high-level temporal models,
recent approaches use temporal convolutions to directly classify the video
frames. In this paper, we introduce a multi-stage architecture for the temporal
action segmentation task. Each stage features a set of dilated temporal
convolutions to generate an initial prediction that is refined by the next one.
This architecture is trained using a combination of a classification loss and a
proposed smoothing loss that penalizes over-segmentation errors. Extensive
evaluation shows the effectiveness of the proposed model in capturing
long-range dependencies and recognizing action segments. Our model achieves
state-of-the-art results on three challenging datasets: 50Salads, Georgia Tech
Egocentric Activities (GTEA), and the Breakfast dataset.
Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective
Authors: Chao Gao, Yuan YAO, Weizhi Zhu
Date: 2019-03-05 00:00:00
Tasks: scoring rule

Robust scatter estimation is a fundamental task in statistics. The recent
discovery on the connection between robust estimation and generative
adversarial nets (GANs) by Gao et al. (2018) suggests that it is possible to
compute depth-like robust estimators using similar techniques that optimize
GANs. In this paper, we introduce a general learning via classification
framework based on the notion of proper scoring rules. This framework allows us
to understand both matrix depth function and various GANs through the lens of
variational approximations of $f$-divergences induced by proper scoring rules.
We then propose a new class of robust scatter estimators in this framework by
carefully constructing discriminators with appropriate neural network
structures. These estimators are proved to achieve the minimax rate of scatter
estimation under Huber's contamination model. Our numerical results demonstrate
its good performance under various settings against competitors in the
literature.
Universal approximations of permutation invariant/equivariant functions by deep neural networks
Authors: Akiyoshi Sannai, Yuuki Takai, Matthieu Cordonnier
Date: 2019-03-05 00:00:00

In this paper, we develop a theory about the relationship between $G$-invariant/equivariant functions and deep neural networks for finite group $G$. Especially, for a given $G$-invariant/equivariant function, we construct its universal approximator by deep neural network whose layers equip $G$-actions and each affine transformations are $G$-equivariant/invariant. Due to representation theory, we can show that this approximator has exponentially fewer free parameters than usual models.
O-GAN: Extremely Concise Approach for Auto-Encoding Generative Adversarial Networks
Authors: Jianlin Su
Date: 2019-03-05 00:00:00
Tasks: Generative Adversarial Network

In this paper, we propose Orthogonal Generative Adversarial Networks
(O-GANs). We decompose the network of discriminator orthogonally and add an
extra loss into the objective of common GANs, which can enforce discriminator
become an effective encoder. The same extra loss can be embedded into any kind
of GANs and there is almost no increase in computation. Furthermore, we discuss
the principle of our method, which is relative to the fully-exploiting of the
remaining degrees of freedom of discriminator. As we know, our solution is the
simplest approach to train a generative adversarial network with auto-encoding
ability.
A Deep Learning based approach to VM behavior identification in cloud systems
Authors: Matteo Stefanini, Riccardo Lancellotti, Lorenzo Baraldi, Simone Calderara
Date: 2019-03-05 00:00:00
Tasks: Cloud Computing, Clustering, Management

Cloud computing data centers are growing in size and complexity to the point
where monitoring and management of the infrastructure become a challenge due to
scalability issues. A possible approach to cope with the size of such data
centers is to identify VMs exhibiting a similar behavior. Existing literature
demonstrated that clustering together VMs that show a similar behavior may
improve the scalability of both monitoring andmanagement of a data center.
However, available techniques suffer from a trade-off between accuracy and time
to achieve this result. Throughout this paper we propose a different approach
where, instead of an unsupervised clustering, we rely on classifiers based on
deep learning techniques to assigna newly deployed VMs to a cluster of
already-known VMs. The two proposed classifiers, namely DeepConv and DeepFFT
use a convolution neural network and (in the latter model) exploits Fast
Fourier Transformation to classify the VMs. Our proposal is validated using a
set of traces describing the behavior of VMs from a realcloud data center. The
experiments compare our proposal with state-of-the-art solutions available in
literature, demonstrating that our proposal achieve better performance.
Furthermore, we show that our solution issignificantly faster than the
alternatives as it can produce a perfect classification even with just a few
samples of data, making our proposal viable also toclassify on-demand VMs that
are characterized by a short life span.
Using a Segmenting Description approach in Multiple Criteria Decision Aiding
Authors: Milosz Kadzinski, Jan Badura, Jose Rui Figueira
Date: 2019-03-05 00:00:00

We propose a new method for analyzing a set of parameters in a multiple
criteria ranking method. Unlike the existing techniques, we do not use any
optimization technique, instead incorporating and extending a Segmenting
Description approach. While considering a value-based preference disaggregation
method, we demonstrate the usefulness of the introduced algorithm in a
multi-purpose decision analysis exploiting a system of inequalities that models
the Decision Maker's preferences. Specifically, we discuss how it can be
applied for verifying the consistency between the revealed and estimated
preferences as well as for identifying the sources of potential incoherence.
Moreover, we employ the method for conducting robustness analysis, i.e.,
discovering a set of all compatible parameter values and verifying the
stability of suggested recommendation in view of multiplicity of feasible
solutions. In addition, we make clear its suitability for generating arguments
about the validity of outcomes and the role of particular criteria. We discuss
the favorable characteristics of the Segmenting Description approach which
enhance its suitability for use in Multiple Criteria Decision Aiding. These
include keeping in memory an entire process of transforming a system of
inequalities and avoiding the need for processing the inequalities contained in
the basic system which is subsequently enriched with some hypothesis to be
verified. The applicability of the proposed method is exemplified on a
numerical study.
An approach to Decision Making based on Dynamic Argumentation Systems
Authors: Edgardo Ferretti, Luciano H. Tamargo, Alejandro J. Garcia, Marcelo L. Errecalde, Guillermo R. Simari
Date: 2019-03-05 00:00:00
Tasks: Decision Making

In this paper, we introduce a formalism for single-agent decision making that
is based on Dynamic Argumentation Frameworks. The formalism can be used to
justify a choice, which is based on the current situation the agent is
involved. Taking advantage of the inference mechanism of the argumentation
formalism, it is possible to consider preference relations and conflicts among
the available alternatives for that reasoning. With this formalization, given a
particular set of evidence, the justified conclusions supported by warranted
arguments will be used by the agent's decision rules to determine which
alternatives will be selected. We also present an algorithm that implements a
choice function based on our formalization. Finally, we complete our
presentation by introducing formal results that relate the proposed framework
with approaches of classical decision theory.
FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms
Authors: Daniel Grzech, Loïc le Folgoc, Mattias P. Heinrich, Bishesh Khanal, Jakub Moll, Julia A. Schnabel, Ben Glocker, Bernhard Kainz
Date: 2019-03-05 00:00:00
Tasks: Optical Flow Estimation

We present an implementation of a new approach to diffeomorphic non-rigid
registration of medical images. The method is based on optical flow and warps
images via gradient flow with the standard $L^2$ inner product. To compute the
transformation, we rely on accelerated optimisation on the manifold of
diffeomorphisms. We achieve regularity properties of Sobolev gradient flows,
which are expensive to compute, owing to a novel method of averaging the
gradients in time rather than space. We successfully register brain MRI and
challenging abdominal CT scans at speeds orders of magnitude faster than
previous approaches. We make our code available in a public repository:
https://github.com/dgrzech/fastreg
Language and Dialect Identification of Cuneiform Texts
Authors: Tommi Jauhiainen, Heidi Jauhiainen, Tero Alstola, Krister Lindén
Date: 2019-03-05 00:00:00
Proceeding: WS 2019 6
Tasks: Dialect Identification, Language Identification

This article introduces a corpus of cuneiform texts from which the dataset
for the use of the Cuneiform Language Identification (CLI) 2019 shared task was
derived as well as some preliminary language identification experiments
conducted using that corpus. We also describe the CLI dataset and how it was
derived from the corpus. In addition, we provide some baseline language
identification results using the CLI dataset. To the best of our knowledge, the
experiments detailed here are the first time automatic language identification
methods have been used on cuneiform data.
Gated Graph Convolutional Recurrent Neural Networks
Authors: Luana Ruiz, Fernando Gama, Alejandro Ribeiro
Date: 2019-03-05 00:00:00
Tasks: Node Classification

Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather. In this paper, we propose a Graph Convolutional Recurrent Neural Network (GCRNN) architecture specifically tailored to deal with these problems. GCRNNs use convolutional filter banks to keep the number of trainable parameters independent of the size of the graph and of the time sequences considered. We also put forward Gated GCRNNs, a time-gated variation of GCRNNs akin to LSTMs. When compared with GNNs and another graph recurrent architecture in experiments using both synthetic and real-word data, GCRNNs significantly improve performance while using considerably less parameters.