pwc-archive/papers-with-abstracts huggingface dataset:
Query-focused Sentence Compression in Linear Time
Authors: Abram Handler, Brendan O'Connor
Date: 2019-04-19 00:00:00
Tasks: GPU, Sentence, Sentence Compression

Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface. This work introduces a new transition-based sentence compression technique developed for such settings. Our query-focused method constructs length and lexically constrained compressions in linear time, by growing a subgraph in the dependency parse of a sentence. This theoretically efficient approach achieves an 11X empirical speedup over baseline ILP methods, while better reconstructing gold constrained shortenings. Such speedups help query-focused applications, because users are measurably hindered by interface lags. Additionally, our technique does not require an ILP solver or a GPU.
An Investigation of End-to-End Multichannel Speech Recognition for Reverberant and Mismatch Conditions
Authors: Aswin Shanmugam Subramanian, Xiaofei Wang, Shinji Watanabe, Toru Taniguchi, Dung Tran, Yuya Fujita
Date: 2019-04-19 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Denoising, Noisy Speech Recognition, Speech Enhancement, speech-recognition, Speech Recognition

Sequence-to-sequence (S2S) modeling is becoming a popular paradigm for
automatic speech recognition (ASR) because of its ability to jointly optimize
all the conventional ASR components in an end-to-end (E2E) fashion. This report
investigates the ability of E2E ASR from standard close-talk to far-field
applications by encompassing entire multichannel speech enhancement and ASR
components within the S2S model. There have been previous studies on jointly
optimizing neural beamforming alongside E2E ASR for denoising. It is clear from
both recent challenge outcomes and successful products that far-field systems
would be incomplete without solving both denoising and dereverberation
simultaneously. This report uses a recently developed architecture for
far-field ASR by composing neural extensions of dereverberation and beamforming
modules with the S2S ASR module as a single differentiable neural network and
also clearly defining the role of each subnetwork. The original implementation
of this architecture was successfully applied to the noisy speech recognition
task (CHiME-4), while we applied this implementation to noisy reverberant tasks
(DIRHA and REVERB). Our investigation shows that the method achieves better
performance than conventional pipeline methods on the DIRHA English dataset and
comparable performance on the REVERB dataset. It also has additional advantages
of being neither iterative nor requiring parallel noisy and clean speech data.
Automated Focal Loss for Image based Object Detection
Authors: Michael Weber, Michael Fürst, J. Marius Zöllner
Date: 2019-04-19 00:00:00
Tasks: Object, object-detection, Object Detection, regression, vehicle detection

Current state-of-the-art object detection algorithms still suffer the problem
of imbalanced distribution of training data over object classes and background.
Recent work introduced a new loss function called focal loss to mitigate this
problem, but at the cost of an additional hyperparameter. Manually tuning this
hyperparameter for each training task is highly time-consuming.
  With automated focal loss we introduce a new loss function which substitutes
this hyperparameter by a parameter that is automatically adapted during the
training progress and controls the amount of focusing on hard training
examples. We show on the COCO benchmark that this leads to an up to 30% faster
training convergence. We further introduced a focal regression loss which on
the more challenging task of 3D vehicle detection outperforms other loss
functions by up to 1.8 AOS and can be used as a value range independent metric
for regression.
NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation
Authors: Subhashis Hazarika, Haoyu Li, Ko-Chih Wang, Han-Wei Shen, Ching-Shan Chou
Date: 2019-04-19 00:00:00
Tasks: Uncertainty Quantification

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. Surrogate models are widely used in the field of simulation sciences to efficiently analyze computationally expensive simulation models. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.
Human Motion Prediction via Pattern Completion in Latent Representation Space
Authors: Yi Tian Xu, Yaqiao Li, David Meger
Date: 2019-04-18 00:00:00
Tasks: Action Classification, General Classification, Human motion prediction, Motion Generation, motion prediction, Time Series, Time Series Analysis

Inspired by ideas in cognitive science, we propose a novel and general
approach to solve human motion understanding via pattern completion on a
learned latent representation space. Our model outperforms current
state-of-the-art methods in human motion prediction across a number of tasks,
with no customization. To construct a latent representation for time-series of
various lengths, we propose a new and generic autoencoder based on
sequence-to-sequence learning. While traditional inference strategies find a
correlation between an input and an output, we use pattern completion, which
views the input as a partial pattern and to predict the best corresponding
complete pattern. Our results demonstrate that this approach has advantages
when combined with our autoencoder in solving human motion prediction, motion
generation and action classification.
ProductNet: a Collection of High-Quality Datasets for Product Representation Learning
Authors: Chu Wang, Lei Tang, Yang Lu, Shujun Bian, Hirohisa Fujita, Da Zhang, Zuohua Zhang, Yongning Wu
Date: 2019-04-18 00:00:00
Tasks: Active Learning, Representation Learning, Transfer Learning, Vocal Bursts Intensity Prediction

ProductNet is a collection of high-quality product datasets for better
product understanding. Motivated by ImageNet, ProductNet aims at supporting
product representation learning by curating product datasets of high quality
with properly chosen taxonomy. In this paper, the two goals of building
high-quality product datasets and learning product representation support each
other in an iterative fashion: the product embedding is obtained via a
multi-modal deep neural network (master model) designed to leverage product
image and catalog information; and in return, the embedding is utilized via
active learning (local model) to vastly accelerate the annotation process. For
the labeled data, the proposed master model yields high categorization accuracy
(94.7% top-1 accuracy for 1240 classes), which can be used as search indices,
partition keys, and input features for machine learning models. The product
embedding, as well as the fined-tuned master model for a specific business
task, can also be used for various transfer learning tasks.
A Novel BiLevel Paradigm for Image-to-Image Translation
Authors: Liqian Ma, Qianru Sun, Bernt Schiele, Luc van Gool
Date: 2019-04-18 00:00:00
Tasks: Diversity, Image-to-Image Translation, Translation

Image-to-image (I2I) translation is a pixel-level mapping that requires a
large number of paired training data and often suffers from the problems of
high diversity and strong category bias in image scenes. In order to tackle
these problems, we propose a novel BiLevel (BiL) learning paradigm that
alternates the learning of two models, respectively at an instance-specific
(IS) and a general-purpose (GP) level. In each scene, the IS model learns to
maintain the specific scene attributes. It is initialized by the GP model that
learns from all the scenes to obtain the generalizable translation knowledge.
This GP initialization gives the IS model an efficient starting point, thus
enabling its fast adaptation to the new scene with scarce training data. We
conduct extensive I2I translation experiments on human face and street view
datasets. Quantitative results validate that our approach can significantly
boost the performance of classical I2I translation models, such as PG2 and
Pix2Pix. Our visualization results show both higher image quality and more
appropriate instance-specific details, e.g., the translated image of a person
looks more like that person in terms of identity.
When is a Prediction Knowledge?
Authors: Alex Kearney, Patrick M. Pilarski
Date: 2019-04-18 00:00:00
Tasks: Decision Making, Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Within Reinforcement Learning, there is a growing collection of research
which aims to express all of an agent's knowledge of the world through
predictions about sensation, behaviour, and time. This work can be seen not
only as a collection of architectural proposals, but also as the beginnings of
a theory of machine knowledge in reinforcement learning. Recent work has
expanded what can be expressed using predictions, and developed applications
which use predictions to inform decision-making on a variety of synthetic and
real-world problems. While promising, we here suggest that the notion of
predictions as knowledge in reinforcement learning is as yet underdeveloped:
some work explicitly refers to predictions as knowledge, what the requirements
are for considering a prediction to be knowledge have yet to be well explored.
This specification of the necessary and sufficient conditions of knowledge is
important; even if claims about the nature of knowledge are left implicit in
technical proposals, the underlying assumptions of such claims have
consequences for the systems we design. These consequences manifest in both the
way we choose to structure predictive knowledge architectures, and how we
evaluate them. In this paper, we take a first step to formalizing predictive
knowledge by discussing the relationship of predictive knowledge learning
methods to existing theories of knowledge in epistemology. Specifically, we
explore the relationships between Generalized Value Functions and epistemic
notions of Justification and Truth.
Making Meaning: Semiotics Within Predictive Knowledge Architectures
Authors: Alex Kearney, Oliver Oxton
Date: 2019-04-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Within Reinforcement Learning, there is a fledgling approach to
conceptualizing the environment in terms of predictions. Central to this
predictive approach is the assertion that it is possible to construct
ontologies in terms of predictions about sensation, behaviour, and time---to
categorize the world into entities which express all aspects of the world using
only predictions. This construction of ontologies is integral to predictive
approaches to machine knowledge where objects are described exclusively in
terms of how they are perceived. In this paper, we ground the Pericean model of
semiotics in terms of Reinforcement Learning Methods, describing Peirce's Three
Categories in the notation of General Value Functions. Using the Peircean model
of semiotics, we demonstrate that predictions alone are insufficient to
construct an ontology; however, we identify predictions as being integral to
the meaning-making process. Moreover, we discuss how predictive knowledge
provides a particularly stable foundation for semiosis\textemdash the process
of making meaning\textemdash and suggest a possible avenue of research to
design algorithmic methods which construct semantics and meaning using
predictions.
A deep learning based solution for construction equipment detection: from development to deployment
Authors: Saeed Arabi, Arya Haghighat, Anuj Sharma
Date: 2019-04-18 00:00:00
Tasks: Model Optimization, Model Selection, object-detection, Object Detection

This paper aims at providing researchers and engineering professionals with a
practical and comprehensive deep learning based solution to detect construction
equipment from the very first step of its development to the last one which is
deployment. This paper focuses on the last step of deployment. The first phase
of solution development, involved data preparation, model selection, model
training, and model evaluation. The second phase of the study comprises of
model optimization, application specific embedded system selection, and
economic analysis. Several embedded systems were proposed and compared. The
review of the results confirms superior real-time performance of the solutions
with a consistent above 90% rate of accuracy. The current study validates the
practicality of deep learning based object detection solutions for construction
scenarios. Moreover, the detailed knowledge, presented in this study, can be
employed for several purposes such as, safety monitoring, productivity
assessments, and managerial decisions.
Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands
Authors: Giovanni Campagna, Silei Xu, Mehrad Moradshahi, Richard Socher, Monica S. Lam
Date: 2019-04-18 00:00:00
Tasks: Data Augmentation, Translation

To understand diverse natural language commands, virtual assistants today are
trained with numerous labor-intensive, manually annotated sentences. This paper
presents a methodology and the Genie toolkit that can handle new compound
commands with significantly less manual effort. We advocate formalizing the
capability of virtual assistants with a Virtual Assistant Programming Language
(VAPL) and using a neural semantic parser to translate natural language into
VAPL code. Genie needs only a small realistic set of input sentences for
validating the neural model. Developers write templates to synthesize data;
Genie uses crowdsourced paraphrases and data augmentation, along with the
synthesized data, to train a semantic parser. We also propose design principles
that make VAPL languages amenable to natural language translation. We apply
these principles to revise ThingTalk, the language used by the Almond virtual
assistant. We use Genie to build the first semantic parser that can support
compound virtual assistants commands with unquoted free-form parameters. Genie
achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's
generality by showing a 19% and 31% improvement over the previous state of the
art on a music skill, aggregate functions, and access control.
Graph Element Networks: adaptive, structured computation and memory
Authors: Ferran Alet, Adarsh K. Jeewajee, Maria Bauza, Alberto Rodriguez, Tomas Lozano-Perez, Leslie Pack Kaelbling
Date: 2019-04-18 00:00:00

We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure. Similar to finite element analysis, we assign nodes of a GNN to spatial locations and use a computational process defined on the graph to model the relationship between an initial function defined over a space and a resulting function in the same space. We use GNNs as a computational substrate, and show that the locations of the nodes in space as well as their connectivity can be optimized to focus on the most complex parts of the space. Moreover, this representational strategy allows the learned input-output relationship to generalize over the size of the underlying space and run the same model at different levels of precision, trading computation for accuracy. We demonstrate this method on a traditional PDE problem, a physical prediction problem from robotics, and learning to predict scene images from novel viewpoints.
Semi-bandit Optimization in the Dispersed Setting
Authors: Maria-Florina Balcan, Travis Dick, Wesley Pegden
Date: 2019-04-18 00:00:00
Tasks: Clustering

The goal of data-driven algorithm design is to obtain high-performing algorithms for specific application domains using machine learning and data. Across many fields in AI, science, and engineering, practitioners will often fix a family of parameterized algorithms and then optimize those parameters to obtain good performance on example instances from the application domain. In the online setting, we must choose algorithm parameters for each instance as they arrive, and our goal is to be competitive with the best fixed algorithm in hindsight. There are two major challenges in online data-driven algorithm design. First, it can be computationally expensive to evaluate the loss functions that map algorithm parameters to performance, which often require the learner to run a combinatorial algorithm to measure its performance. Second, the losses can be extremely volatile and have sharp discontinuities. However, we show that in many applications, evaluating the loss function for one algorithm choice can sometimes reveal the loss for a range of similar algorithms, essentially for free. We develop online optimization algorithms capable of using this kind of extra information by working in the semi-bandit feedback setting. Our algorithms achieve regret bounds that are essentially as good as algorithms under full-information feedback and are significantly more computationally efficient. We apply our semi-bandit results to obtain the first provable guarantees for data-driven algorithm design for linkage-based clustering and we improve the best regret bounds for designing greedy knapsack algorithms.
Self-Supervised Audio-Visual Co-Segmentation
Authors: Andrew Rouditchenko, Hang Zhao, Chuang Gan, Josh Mcdermott, Antonio Torralba
Date: 2019-04-18 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Segmenting objects in images and separating sound sources in audio are
challenging tasks, in part because traditional approaches require large amounts
of labeled data. In this paper we develop a neural network model for visual
object segmentation and sound source separation that learns from natural videos
through self-supervision. The model is an extension of recently proposed work
that maps image pixels to sounds. Here, we introduce a learning approach to
disentangle concepts in the neural networks, and assign semantic categories to
network feature channels to enable independent image segmentation and sound
source separation after audio-visual training on videos. Our evaluations show
that the disentangled model outperforms several baselines in semantic
segmentation and sound source separation.
DeepLocalization: Landmark-based Self-Localization with Deep Neural Networks
Authors: Nico Engel, Stefan Hoermann, Markus Horn, Vasileios Belagiannis, Klaus Dietmayer
Date: 2019-04-18 00:00:00
Tasks: Translation

We address the problem of vehicle self-localization from multi-modal sensor information and a reference map. The map is generated off-line by extracting landmarks from the vehicle's field of view, while the measurements are collected similarly on the fly. Our goal is to determine the autonomous vehicle's pose from the landmark measurements and map landmarks. To learn this mapping, we propose DeepLocalization, a deep neural network that regresses the vehicle's translation and rotation parameters from unordered and dynamic input landmarks. The proposed network architecture is robust to changes of the dynamic environment and can cope with a small number of extracted landmarks. During the training process we rely on synthetically generated ground-truth. In our experiments, we evaluate two inference approaches in real-world scenarios. We show that DeepLocalization can be combined with regular GPS signals and filtering algorithms such as the extended Kalman filter. Our approach achieves state-of-the-art accuracy and is about ten times faster than the related work.
From GAN to WGAN
Authors: Lilian Weng
Date: 2019-04-18 00:00:00
Tasks: Generative Adversarial Network, Math

This paper explains the math behind a generative adversarial network (GAN)
model and why it is hard to be trained. Wasserstein GAN is intended to improve
GANs' training by adopting a smooth metric for measuring the distance between
two probability distributions.
Playgol: learning programs through play
Authors: Andrew Cropper
Date: 2019-04-18 00:00:00
Tasks: Inductive logic programming, Program induction

Children learn though play. We introduce the analogous idea of learning programs through play. In this approach, a program induction system (the learner) is given a set of tasks and initial background knowledge. Before solving the tasks, the learner enters an unsupervised playing stage where it creates its own tasks to solve, tries to solve them, and saves any solutions (programs) to the background knowledge. After the playing stage is finished, the learner enters the supervised building stage where it tries to solve the user-supplied tasks and can reuse solutions learnt whilst playing. The idea is that playing allows the learner to discover reusable general programs on its own which can then help solve the user-supplied tasks. We claim that playing can improve learning performance. We show that playing can reduce the textual complexity of target concepts which in turn reduces the sample complexity of a learner. We implement our idea in Playgol, a new inductive logic programming system. We experimentally test our claim on two domains: robot planning and real-world string transformations. Our experimental results suggest that playing can substantially improve learning performance. We think that the idea of playing (or, more verbosely, unsupervised bootstrapping for supervised program induction) is an important contribution to the problem of developing program induction approaches that self-discover BK.