pwc-archive/papers-with-abstracts huggingface dataset:
Visual Representations for Semantic Target Driven Navigation
Authors: Arsalan Mousavian, Alexander Toshev, Marek Fiser, Jana Kosecka, Ayzaan Wahid, James Davidson
Date: 2018-05-15 00:00:00
Tasks: Domain Adaptation, Visual Navigation

What is a good visual representation for autonomous agents? We address this question in the context of semantic visual navigation, which is the problem of a robot finding its way through a complex environment to a target object, e.g. go to the refrigerator. Instead of acquiring a metric semantic map of an environment and using planning for navigation, our approach learns navigation policies on top of representations that capture spatial layout and semantic contextual cues. We propose to using high level semantic and contextual features including segmentation and detection masks obtained by off-the-shelf state-of-the-art vision as observations and use deep network to learn the navigation policy. This choice allows using additional data, from orthogonal sources, to better train different parts of the model the representation extraction is trained on large standard vision datasets while the navigation component leverages large synthetic environments for training. This combination of real and synthetic is possible because equitable feature representations are available in both (e.g., segmentation and detection masks), which alleviates the need for domain adaptation. Both the representation and the navigation policy can be readily applied to real non-synthetic environments as demonstrated on the Active Vision Dataset [1]. Our approach gets successfully to the target in 54% of the cases in unexplored environments, compared to 46% for non-learning based approach, and 28% for the learning-based baseline.
Paper Abstract Writing through Editing Mechanism
Authors: Qingyun Wang, Zhi-Hao Zhou, Lifu Huang, Spencer Whitehead, Boliang Zhang, Heng Ji, Kevin Knight
Date: 2018-05-15 00:00:00
Proceeding: ACL 2018 7
Tasks: Paper generation, Text Generation

We present a paper abstract writing system based on an attentive neural
sequence-to-sequence model that can take a title as input and automatically
generate an abstract. We design a novel Writing-editing Network that can attend
to both the title and the previously generated abstract drafts and then
iteratively revise and polish the abstract. With two series of Turing tests,
where the human judges are asked to distinguish the system-generated abstracts
from human-written ones, our system passes Turing tests by junior domain
experts at a rate up to 30% and by non-expert at a rate up to 80%.
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State Machines
Authors: Roy Schwartz, Sam Thomson, Noah A. Smith
Date: 2018-05-15 00:00:00
Tasks: Explainable artificial intelligence, General Classification, Representation Learning, text-classification, Text Classification

Recurrent and convolutional neural networks comprise two distinct families of
models that have proven to be useful for encoding natural language utterances.
In this paper we present SoPa, a new model that aims to bridge these two
approaches. SoPa combines neural representation learning with weighted
finite-state automata (WFSAs) to learn a soft version of traditional surface
patterns. We show that SoPa is an extension of a one-layer CNN, and that such
CNNs are equivalent to a restricted version of SoPa, and accordingly, to a
restricted form of WFSA. Empirically, on three text classification tasks, SoPa
is comparable or better than both a BiLSTM (RNN) baseline and a CNN baseline,
and is particularly useful in small data settings.
DPRed: Making Typical Activation and Weight Values Matter In Deep Learning Computing
Authors: Alberto Delmas, Sayeh Sharify, Patrick Judd, Kevin Siu, Milos Nikolic, Andreas Moshovos
Date: 2018-04-17 00:00:00

We show that selecting a single data type (precision) for all values in Deep
Neural Networks, even if that data type is different per layer, amounts to
worst case design. Much shorter data types can be used if we target the common
case by adjusting the precision at a much finer granularity. We propose Dynamic
Precision Reduction (DPRed), where we group weights and activations and encode
them using a precision specific to each group. The per group precisions are
selected statically for the weights and dynamically by hardware for the
activations. We exploit these precisions to reduce: 1) off-chip storage and
off- and on-chip communication, and 2) execution time. DPRed compression
reduces off-chip traffic to nearly 35% and 33% on average compared to no
compression respectively for 16b and 8b models. This makes it possible to
sustain higher performance for a given off-chip memory interface while also
boosting energy efficiency. We also demonstrate designs where the time required
to process each group of activations and/or weights scales proportionally to
the precision they use for convolutional and fully-connected layers. This
improves execution time and energy efficiency for both dense and sparse
networks. We show the techniques work with 8-bit networks, where 1.82x and
2.81x speedups are achieved for two different hardware variants that take
advantage of dynamic precision variability.
Automated Vision-based Bridge Component Extraction Using Multiscale Convolutional Neural Networks
Authors: Yasutaka Narazaki, Vedhus Hoskere, Tu A. Hoang, Billie F. Spencer Jr
Date: 2018-05-15 00:00:00
Tasks: Classification, General Classification, Scene Classification, Superpixels

Image data has a great potential of helping post-earthquake visual
inspections of civil engineering structures due to the ease of data acquisition
and the advantages in capturing visual information. A variety of techniques
have been applied to detect damages automatically from a close-up image of a
structural component. However, the application of the automatic damage
detection methods become increasingly difficult when the image includes
multiple components from different structures. To reduce the inaccurate false
positive alarms, critical structural components need to be recognized first,
and the damage alarms need to be cleaned using the component recognition
results. To achieve the goal, this study aims at recognizing and extracting
bridge components from images of urban scenes. The bridge component recognition
begins with pixel-wise classifications of an image into 10 scene classes. Then,
the original image and the scene classification results are combined to
classify the image pixels into five component classes. The multi-scale
convolutional neural networks (multi-scale CNNs) are used to perform pixel-wise
classification, and the classification results are post-processed by averaging
within superpixels and smoothing by conditional random fields (CRFs). The
performance of the bridge component extraction is tested in terms of accuracy
and consistency.
Vision-based Automated Bridge Component Recognition Integrated With High-level Scene Understanding
Authors: Yasutaka Narazaki, Vedhus Hoskere, Tu A. Hoang, Billie F. Spencer
Date: 2018-05-15 00:00:00
Tasks: Scene Classification, Scene Understanding

Image data has a great potential of helping conventional visual inspections
of civil engineering structures due to the ease of data acquisition and the
advantages in capturing visual information. A variety of techniques have been
proposed to detect damages, such as cracks and spalling on a close-up image of
a single component (columns and road surfaces etc.). However, these techniques
commonly suffer from severe false-positives especially when the image includes
multiple components of different structures. To reduce the false-positives and
extract reliable information about the structures' conditions, detection and
localization of critical structural components are important first steps
preceding the damage assessment. This study aims at recognizing bridge
structural and non-structural components from images of urban scenes. During
the bridge component recognition, every image pixel is classified into one of
the five classes (non-bridge, columns, beams and slabs, other structural, other
nonstructural) by multi-scale convolutional neural networks (multi-scale CNNs).
To reduce false-positives and get consistent labels, the component
classifications are integrated with scene understanding by an additional
classifier with 10 higher-level scene classes (building, greenery, person,
pavement, signs and poles, vehicles, bridges, water, sky, and others). The
bridge component recognition integrated with the scene understanding is
compared with the naive approach without scene classification in terms of
accuracy, false-positives and consistencies to demonstrate the effectiveness of
the integrated approach.
TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation
Authors: François Hernandez, Vincent Nguyen, Sahar Ghannay, Natalia Tomashenko, Yannick Estève
Date: 2018-05-12 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In this paper, we present TED-LIUM release 3 corpus dedicated to speech recognition in English, that multiplies by more than two the available data to train acoustic models in comparison with TED-LIUM 2. We present the recent development on Automatic Speech Recognition (ASR) systems in comparison with the two previous releases of the TED-LIUM Corpus from 2012 and 2014. We demonstrate that, passing from 207 to 452 hours of transcribed speech training data is really more useful for end-to-end ASR systems than for HMM-based state-of-the-art ones, even if the HMM-based ASR system still outperforms end-to-end ASR system when the size of audio training data is 452 hours, with respectively a Word Error Rate (WER) of 6.6% and 13.7%. Last, we propose two repartitions of the TED-LIUM release 3 corpus: the legacy one that is the same as the one existing in release 2, and a new one, calibrated and designed to make experiments on speaker adaptation. Like the two first releases, TED-LIUM 3 corpus will be freely available for the research community.
On the organization of grid and place cells: Neural de-noising via subspace learning
Authors: David M. Schwartz, O. Ozan Koyluoglu
Date: 2017-12-13 00:00:00
Tasks: Hippocampus

Place cells in the hippocampus are active when an animal visits a certain
location (referred to as a place field) within an environment. Grid cells in
the medial entorhinal cortex (MEC) respond at multiple locations, with firing
fields that form a periodic and hexagonal tiling of the environment. The joint
activity of grid and place cell populations, as a function of location, forms a
neural code for space. An ensemble of codes is generated by varying grid and
place cell population parameters. For each code in this ensemble, codewords are
generated by stimulating a network with a discrete set of locations. In this
manuscript, we develop an understanding of the relationships between coding
theoretic properties of these combined populations and code construction
parameters. These relationships are revisited by measuring the performances of
biologically realizable algorithms implemented by networks of place and grid
cell populations, as well as constraint neurons, which perform de-noising
operations. Objectives of this work include the investigation of coding
theoretic limitations of the mammalian neural code for location and how
communication between grid and place cell networks may improve the accuracy of
each population's representation. Simulations demonstrate that de-noising
mechanisms analyzed here can significantly improve fidelity of this neural
representation of space. Further, patterns observed in connectivity of each
population of simulated cells suggest that
inter-hippocampal-medial-entorhinal-cortical connectivity decreases downward
along the dorsoventral axis.
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry
Authors: Benjamin Hou, Nina Miolane, Bishesh Khanal, Matthew C. H. Lee, Amir Alansary, Steven McDonagh, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz
Date: 2018-05-02 00:00:00
Tasks: Camera Pose Estimation, Medical Image Analysis, Pose Estimation, Translation

Pose estimation, i.e. predicting a 3D rigid transformation with respect to a
fixed co-ordinate frame in, SE(3), is an omnipresent problem in medical image
analysis with applications such as: image rigid registration, anatomical
standard plane detection, tracking and device/camera pose estimation. Deep
learning methods often parameterise a pose with a representation that separates
rotation and translation. As commonly available frameworks do not provide means
to calculate loss on a manifold, regression is usually performed using the
L2-norm independently on the rotation's and the translation's
parameterisations, which is a metric for linear spaces that does not take into
account the Lie group structure of SE(3). In this paper, we propose a general
Riemannian formulation of the pose estimation problem. We propose to train the
CNN directly on SE(3) equipped with a left-invariant Riemannian metric,
coupling the prediction of the translation and rotation defining the pose. At
each training step, the ground truth and predicted pose are elements of the
manifold, where the loss is calculated as the Riemannian geodesic distance. We
then compute the optimisation direction by back-propagating the gradient with
respect to the predicted pose on the tangent space of the manifold SE(3) and
update the network weights. We thoroughly evaluate the effectiveness of our
loss function by comparing its performance with popular and most commonly used
existing methods, on tasks such as image-based localisation and intensity-based
2D/3D registration. We also show that hyper-parameters, used in our loss
function to weight the contribution between rotations and translations, can be
intrinsically calculated from the dataset to achieve greater performance
margins.
Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions
Authors: Vinodkumar Prabhakaran, Premkumar Ganeshkumar, Owen Rambow
Date: 2018-05-15 00:00:00
Proceeding: NAACL 2018 6
Tasks: Attribute

Understanding how social power structures affect the way we interact with one
another is of great interest to social scientists who want to answer
fundamental questions about human behavior, as well as to computer scientists
who want to build automatic methods to infer the social contexts of
interactions. In this paper, we employ advancements in extra-propositional
semantics extraction within NLP to study how author commitment reflects the
social context of an interaction. Specifically, we investigate whether the
level of commitment expressed by individuals in an organizational interaction
reflects the hierarchical power structures they are part of. We find that
subordinates use significantly more instances of non-commitment than superiors.
More importantly, we also find that subordinates attribute propositions to
other agents more often than superiors do --- an aspect that has not been
studied before. Finally, we show that enriching lexical features with
commitment labels captures important distinctions in social meanings.
Superconducting Optoelectronic Neurons V: Networks and Scaling
Authors: Jeffrey M. Shainline, Jeff Chiles, Sonia M. Buckley, Adam N. McCaughan, Richard P. Mirin, Sae Woo Nam
Date: 2018-05-04 00:00:00
Tasks: Clustering

Networks of superconducting optoelectronic neurons are investigated for their
near-term technological potential and long-term physical limitations. Networks
with short average path length, high clustering coefficient, and power-law
degree distribution are designed using a growth model that assigns connections
between new and existing nodes based on spatial distance as well as degree of
existing nodes. The network construction algorithm is scalable to arbitrary
levels of network hierarchy and achieves systems with fractal spatial
properties and efficient wiring. By modeling the physical size of
superconducting optoelectronic neurons, we calculate the area of these
networks. A system with 8100 neurons and 330,430 total synapses will fit on a
1\,cm $\times$ 1\,cm die. Systems of millions of neurons with hundreds of
millions of synapses will fit on a 300\,mm wafer. For multi-wafer assemblies,
communication at light speed enables a neuronal pool the size of a large data
center comprising 100 trillion neurons with coherent oscillations at 1\,MHz.
Assuming a power law frequency distribution, as is necessary for self-organized
criticality, we calculate the power consumption of the networks. We find the
use of single photons for communication and superconducting circuits for
computation leads to power density low enough to be cooled by liquid $^4$He for
networks of any scale.
Superconducting Optoelectronic Neurons III: Synaptic Plasticity
Authors: Jeffrey M. Shainline, Adam N. McCaughan, Sonia M. Buckley, Christine A. Donnelly, Manuel Castellanos-Beltran, Michael L. Schneider, Richard P. Mirin, Sae Woo Nam
Date: 2018-05-04 00:00:00

As a means of dynamically reconfiguring the synaptic weight of a
superconducting optoelectronic loop neuron, a superconducting flux storage loop
is inductively coupled to the synaptic current bias of the neuron. A standard
flux memory cell is used to achieve a binary synapse, and loops capable of
storing many flux quanta are used to enact multi-stable synapses. Circuits are
designed to implement supervised learning wherein current pulses add or remove
flux from the loop to strengthen or weaken the synaptic weight. Designs are
presented for circuits with hundreds of intermediate synaptic weights between
minimum and maximum strengths. Circuits for implementing unsupervised learning
are modeled using two photons to strengthen and two photons to weaken the
synaptic weight via Hebbian and anti-Hebbian learning rules, and techniques are
proposed to control the learning rate. Implementation of short-term plasticity,
homeostatic plasticity, and metaplasticity in loop neurons is discussed.
Superconducting Optoelectronic Neurons II: Receiver Circuits
Authors: Jeffrey M. Shainline, Sonia M. Buckley, Adam N. McCaughan, Manuel Castellanos-Beltran, Christine A. Donnelly, Michael L. Schneider, Richard P. Mirin, Sae Woo Nam
Date: 2018-05-07 00:00:00

Circuits using superconducting single-photon detectors and Josephson
junctions to perform signal reception, synaptic weighting, and integration are
investigated. The circuits convert photon-detection events into flux quanta,
the number of which is determined by the synaptic weight. The current from many
synaptic connections is inductively coupled to a superconducting loop that
implements the neuronal threshold operation. Designs are presented for synapses
and neurons that perform integration as well as detect coincidence events for
temporal coding. Both excitatory and inhibitory connections are demonstrated.
It is shown that a neuron with a single integration loop can receive input from
1000 such synaptic connections, and neurons of similar design could employ many
loops for dendritic processing.
Contextual Bandit with Adaptive Feature Extraction
Authors: Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi, Irina Rish
Date: 2018-02-03 00:00:00
Tasks: Clustering, Decision Making, Online Clustering, Representation Learning

We consider an online decision making setting known as contextual bandit problem, and propose an approach for improving contextual bandit performance by using an adaptive feature extraction (representation learning) based on online clustering. Our approach starts with an off-line pre-training on unlabeled history of contexts (which can be exploited by our approach, but not by the standard contextual bandit), followed by an online selection and adaptation of encoders. Specifically, given an input sample (context), the proposed approach selects the most appropriate encoding function to extract a feature vector which becomes an input for a contextual bandit, and updates both the bandit and the encoding function based on the context and on the feedback (reward). Our experiments on a variety of datasets, and both in stationary and non-stationary environments of several kinds demonstrate clear advantages of the proposed adaptive representation learning over the standard contextual bandit based on "raw" input contexts.
Non-Projective Dependency Parsing with Non-Local Transitions
Authors: Daniel Fernández-González, Carlos Gómez-Rodríguez
Date: 2017-10-25 00:00:00
Tasks: ARC, Dependency Parsing

We present a novel transition system, based on the Covington non-projective
parser, introducing non-local transitions that can directly create arcs
involving nodes to the left of the current focus positions. This avoids the
need for long sequences of No-Arc transitions to create long-distance arcs,
thus alleviating error propagation. The resulting parser outperforms the
original version and achieves the best accuracy on the Stanford Dependencies
conversion of the Penn Treebank among greedy transition-based algorithms.
A Dynamic Oracle for Linear-Time 2-Planar Dependency Parsing
Authors: Daniel Fernández-González, Carlos Gómez-Rodríguez
Date: 2018-05-14 00:00:00
Tasks: ARC, Dependency Parsing

We propose an efficient dynamic oracle for training the 2-Planar
transition-based parser, a linear-time parser with over 99% coverage on
non-projective syntactic corpora. This novel approach outperforms the static
training strategy in the vast majority of languages tested and scored better on
most datasets than the arc-hybrid parser enhanced with the SWAP transition,
which can handle unrestricted non-projectivity.
ClaiRE at SemEval-2018 Task 7 - Extended Version
Authors: Lena Hettinger, Alexander Dallmann, Albin Zehe, Thomas Niebler, Andreas Hotho
Date: 2018-04-16 00:00:00
Tasks: General Classification

In this paper we describe our post-evaluation results for SemEval-2018 Task 7
on clas- sification of semantic relations in scientific literature for clean
(subtask 1.1) and noisy data (subtask 1.2). This is an extended ver- sion of
our workshop paper (Hettinger et al., 2018) including further technical details
(Sec- tions 3.2 and 4.3) and changes made to the preprocessing step in the
post-evaluation phase (Section 2.1). Due to these changes Classification of
Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for
the first subtask and 81.44% for the second.
Simulating Action Dynamics with Neural Process Networks
Authors: Antoine Bosselut, Omer Levy, Ari Holtzman, Corin Ennis, Dieter Fox, Yejin Choi
Date: 2017-11-14 00:00:00
Proceeding: ICLR 2018 1

Understanding procedural language requires anticipating the causal effects of
actions, even when they are not explicitly stated. In this work, we introduce
Neural Process Networks to understand procedural text through (neural)
simulation of action dynamics. Our model complements existing memory
architectures with dynamic entity tracking by explicitly modeling actions as
state transformers. The model updates the states of the entities by executing
learned action operators. Empirical results demonstrate that our proposed model
can reason about the unstated causal effects of actions, allowing it to provide
more accurate contextual information for understanding and generating
procedural text, all while offering more interpretable internal representations
than existing alternatives.