pwc-archive/papers-with-abstracts huggingface dataset:
wav2letter++: The Fastest Open-source Speech Recognition System
Authors: Vineel Pratap, Awni Hannun, Qiantong Xu, Jeff Cai, Jacob Kahn, Gabriel Synnaeve, Vitaliy Liptchinsky, Ronan Collobert
Date: 2018-12-18 00:00:00
Tasks: Speech Recognition

This paper introduces wav2letter++, the fastest open-source deep learning
speech recognition framework. wav2letter++ is written entirely in C++, and uses
the ArrayFire tensor library for maximum efficiency. Here we explain the
architecture and design of the wav2letter++ system and compare it to other
major open-source speech recognition systems. In some cases wav2letter++ is
more than 2x faster than other optimized frameworks for training end-to-end
neural networks for speech recognition. We also show that wav2letter++'s
training times scale linearly to 64 GPUs, the highest we tested, for models
with 100 million parameters. High-performance frameworks enable fast iteration,
which is often a crucial factor in successful research and model tuning on new
datasets and tasks.
Towards Deep Conversational Recommendations
Authors: Raymond Li, Samira Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, Chris Pal
Date: 2018-12-18 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Conversational Recommendation, Recommendation Systems, Sentiment Analysis

There has been growing interest in using neural networks and deep learning
techniques to create dialogue systems. Conversational recommendation is an
interesting setting for the scientific exploration of dialogue with natural
language as the associated discourse involves goal-driven dialogue that often
transforms naturally into more free-form chat. This paper provides two
contributions. First, until now there has been no publicly available
large-scale dataset consisting of real-world dialogues centered around
recommendations. To address this issue and to facilitate our exploration here,
we have collected ReDial, a dataset consisting of over 10,000 conversations
centered around the theme of providing movie recommendations. We make this data
available to the community for further research. Second, we use this dataset to
explore multiple facets of conversational recommendations. In particular we
explore new neural architectures, mechanisms, and methods suitable for
composing conversational recommendation systems. Our dataset allows us to
systematically probe model sub-components addressing different parts of the
overall problem domain ranging from: sentiment analysis and cold-start
recommendation generation to detailed aspects of how natural language is used
in this setting in the real world. We combine such sub-components into a
full-blown dialogue system and examine its behavior.
Deep Transfer Learning for Static Malware Classification
Authors: Li Chen
Date: 2018-12-18 00:00:00
Tasks: Binary Classification, Classification, General Classification, Malware Classification, Malware Detection, Transfer Learning

We propose to apply deep transfer learning from computer vision to static
malware classification. In the transfer learning scheme, we borrow knowledge
from natural images or objects and apply to the target domain of static malware
detection. As a result, training time of deep neural networks is accelerated
while high classification performance is still maintained. We demonstrate the
effectiveness of our approach on three experiments and show that our proposed
method outperforms other classical machine learning methods measured in
accuracy, false positive rate, true positive rate and $F_1$ score (in binary
classification). We instrument an interpretation component to the algorithm and
provide interpretable explanations to enhance security practitioners' trust to
the model. We further discuss a convex combination scheme of transfer learning
and training from scratch for enhanced malware detection, and provide insights
of the algorithmic interpretation of vision-based malware classification
techniques.
FML: Face Model Learning from Videos
Authors: Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhöfer, Christian Theobalt
Date: 2018-12-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Reconstruction, Face Model, model

Monocular image-based 3D reconstruction of faces is a long-standing problem
in computer vision. Since image data is a 2D projection of a 3D face, the
resulting depth ambiguity makes the problem ill-posed. Most existing methods
rely on data-driven priors that are built from limited 3D face scans. In
contrast, we propose multi-frame video-based self-supervised training of a deep
network that (i) learns a face identity model both in shape and appearance
while (ii) jointly learning to reconstruct 3D faces. Our face model is learned
using only corpora of in-the-wild video clips collected from the Internet. This
virtually endless source of training data enables learning of a highly general
3D face model. In order to achieve this, we propose a novel multi-frame
consistency loss that ensures consistent shape and appearance across multiple
frames of a subject's face, thus minimizing depth ambiguity. At test time we
can use an arbitrary number of frames, so that we can perform both monocular as
well as multi-frame reconstruction.
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Authors: Zhe Cao, Gines Hidalgo, Tomas Simon, Shih-En Wei, Yaser Sheikh
Date: 2018-12-18 00:00:00
Tasks: 2D Pose Estimation, Keypoint Detection, Multi-Person Pose Estimation, Pose Estimation

OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Automatic Detection of Reflective Thinking in Mathematical Problem Solving based on Unconstrained Bodily Exploration
Authors: Temitayo A. Olugbade, Joseph Newbold, Rose Johnson, Erica Volta, Paolo Alborno, Radoslaw Niewiadomski, Max Dillon, Gualtiero Volpe, Nadia Bianchi-Berthouze
Date: 2018-12-18 00:00:00
Tasks: Mathematical Problem-Solving

For technology (like serious games) that aims to deliver interactive learning, it is important to address relevant mental experiences such as reflective thinking during problem solving. To facilitate research in this direction, we present the weDraw-1 Movement Dataset of body movement sensor data and reflective thinking labels for 26 children solving mathematical problems in unconstrained settings where the body (full or parts) was required to explore these problems. Further, we provide qualitative analysis of behaviours that observers used in identifying reflective thinking moments in these sessions. The body movement cues from our compilation informed features that lead to average F1 score of 0.73 for automatic detection of reflective thinking based on Long Short-Term Memory neural networks. We further obtained 0.79 average F1 score for end-to-end detection of reflective thinking periods, i.e. based on raw sensor data. Finally, the algorithms resulted in 0.64 average F1 score for period subsegments as short as 4 seconds. Overall, our results show the possibility of detecting reflective thinking moments from body movement behaviours of a child exploring mathematical concepts bodily, such as within serious game play.
Explanatory Graphs for CNNs
Authors: Quanshi Zhang, Xin Wang, Ruiming Cao, Ying Nian Wu, Feng Shi, Song-Chun Zhu
Date: 2018-12-18 00:00:00
Tasks: Object

This paper introduces a graphical model, namely an explanatory graph, which
reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN.
Each filter in a conv-layer of a CNN for object classification usually
represents a mixture of object parts. We develop a simple yet effective method
to disentangle object-part pattern components from each filter. We construct an
explanatory graph to organize the mined part patterns, where a node represents
a part pattern, and each edge encodes co-activation relationships and spatial
relationships between patterns. More crucially, given a pre-trained CNN, the
explanatory graph is learned without a need of annotating object parts.
Experiments show that each graph node consistently represented the same object
part through different images, which boosted the transferability of CNN
features. We transferred part patterns in the explanatory graph to the task of
part localization, and our method significantly outperformed other approaches.
Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering
Authors: Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu
Date: 2018-12-18 00:00:00
Tasks: Object, Question Answering

In this paper, we present a method to mine object-part patterns from
conv-layers of a pre-trained convolutional neural network (CNN). The mined
object-part patterns are organized by an And-Or graph (AOG). This interpretable
AOG representation consists of a four-layer semantic hierarchy, i.e., semantic
parts, part templates, latent patterns, and neural units. The AOG associates
each object part with certain neural units in feature maps of conv-layers. The
AOG is constructed in a weakly-supervised manner, i.e., very few annotations
(e.g., 3-20) of object parts are used to guide the learning of AOGs. We develop
a question-answering (QA) method that uses active human-computer communications
to mine patterns from a pre-trained CNN, in order to incrementally explain more
features in conv-layers. During the learning process, our QA method uses the
current AOG for part localization. The QA method actively identifies objects,
whose feature maps cannot be explained by the AOG. Then, our method asks people
to annotate parts on the unexplained objects, and uses answers to discover CNN
patterns corresponding to the newly labeled parts. In this way, our method
gradually grows new branches and refines existing branches on the AOG to
semanticize CNN representations. In experiments, our method exhibited a high
learning efficiency. Our method used about 1/6-1/3 of the part annotations for
training, but achieved similar or better part-localization performance than
fast-RCNN methods.
Uniform Convergence Bounds for Codec Selection
Authors: Clayton Sanford, Cyrus Cousins, Eli Upfal
Date: 2018-12-18 00:00:00
Tasks: Selection bias

We frame the problem of selecting an optimal audio encoding scheme as a
supervised learning task. Through uniform convergence theory, we guarantee
approximately optimal codec selection while controlling for selection bias. We
present rigorous statistical guarantees for the codec selection problem that
hold for arbitrary distributions over audio sequences and for arbitrary quality
metrics. Our techniques can thus balance sound quality and compression ratio,
and use audio samples from the distribution to select a codec that performs
well on that particular type of data. The applications of our technique are
immense, as it can be used to optimize for quality and bandwidth usage of
streaming and other digital media, while significantly outperforming approaches
that apply a fixed codec to all data sources.
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search
Authors: Yesmina Jaafra, Jean Luc Laurent, Aline Deruyver, Mohamed Saber Naceur
Date: 2018-12-17 00:00:00
Tasks: General Classification, Meta Reinforcement Learning, Natural Language Understanding, Neural Architecture Search, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), speech-recognition, Speech Recognition

Deep Neural networks are efficient and flexible models that perform well for
a variety of tasks such as image, speech recognition and natural language
understanding. In particular, convolutional neural networks (CNN) generate a
keen interest among researchers in computer vision and more specifically in
classification tasks. CNN architecture and related hyperparameters are
generally correlated to the nature of the processed task as the network
extracts complex and relevant characteristics allowing the optimal convergence.
Designing such architectures requires significant human expertise, substantial
computation time and doesn't always lead to the optimal network. Model
configuration topic has been extensively studied in machine learning without
leading to a standard automatic method. This survey focuses on reviewing and
discussing the current progress in automating CNN architecture search.
Steerable $e$PCA: Rotationally Invariant Exponential Family PCA
Authors: Zhizhen Zhao, Lydia T. Liu, Amit Singer
Date: 2018-12-20 00:00:00

In photon-limited imaging, the pixel intensities are affected by photon count noise. Many applications, such as 3-D reconstruction using correlation analysis in X-ray free electron laser (XFEL) single molecule imaging, require an accurate estimation of the covariance of the underlying 2-D clean images. Accurate estimation of the covariance from low-photon count images must take into account that pixel intensities are Poisson distributed, hence the classical sample covariance estimator is sub-optimal. Moreover, in single molecule imaging, including in-plane rotated copies of all images could further improve the accuracy of covariance estimation. In this paper we introduce an efficient and accurate algorithm for covariance matrix estimation of count noise 2-D images, including their uniform planar rotations and possibly reflections. Our procedure, steerable $e$PCA, combines in a novel way two recently introduced innovations. The first is a methodology for principal component analysis (PCA) for Poisson distributions, and more generally, exponential family distributions, called $e$PCA. The second is steerable PCA, a fast and accurate procedure for including all planar rotations for PCA. The resulting principal components are invariant to the rotation and reflection of the input images. We demonstrate the efficiency and accuracy of steerable $e$PCA in numerical experiments involving simulated XFEL datasets and rotated Yale B face data.
Deep Metric Transfer for Label Propagation with Limited Annotated Data
Authors: Bin Liu, Zhirong Wu, Han Hu, Stephen Lin
Date: 2018-12-20 00:00:00
Tasks: All, Metric Learning, Object Recognition, Transfer Learning

We study object recognition under the constraint that each object class is only represented by very few observations. Semi-supervised learning, transfer learning, and few-shot recognition all concern with achieving fast generalization with few labeled data. In this paper, we propose a generic framework that utilizes unlabeled data to aid generalization for all three tasks. Our approach is to create much more training data through label propagation from the few labeled examples to a vast collection of unannotated images. The main contribution of the paper is that we show such a label propagation scheme can be highly effective when the similarity metric used for propagation is transferred from other related domains. We test various combinations of supervised and unsupervised metric learning methods with various label propagation algorithms. We find that our framework is very generic without being sensitive to any specific techniques. By taking advantage of unlabeled data in this way, we achieve significant improvements on all three tasks.
Deep Paper Gestalt
Authors: Jia-Bin Huang
Date: 2018-12-20 00:00:00

Recent years have witnessed a significant increase in the number of paper
submissions to computer vision conferences. The sheer volume of paper
submissions and the insufficient number of competent reviewers cause a
considerable burden for the current peer review system. In this paper, we learn
a classifier to predict whether a paper should be accepted or rejected based
solely on the visual appearance of the paper (i.e., the gestalt of a paper).
Experimental results show that our classifier can safely reject 50% of the bad
papers while wrongly reject only 0.4% of the good papers, and thus dramatically
reduce the workload of the reviewers. We also provide tools for providing
suggestions to authors so that they can improve the gestalt of their papers.
What are the biases in my word embedding?
Authors: Nathaniel Swinger, Maria De-Arteaga, Neil Thomas Heffernan IV, Mark DM Leiserson, Adam Tauman Kalai
Date: 2018-12-20 00:00:00
Tasks: Word Embeddings

This paper presents an algorithm for enumerating biases in word embeddings. The algorithm exposes a large number of offensive associations related to sensitive features such as race and gender on publicly available embeddings, including a supposedly "debiased" embedding. These biases are concerning in light of the widespread use of word embeddings. The associations are identified by geometric patterns in word embeddings that run parallel between people's names and common lower-case tokens. The algorithm is highly unsupervised: it does not even require the sensitive features to be pre-specified. This is desirable because: (a) many forms of discrimination--such as racial discrimination--are linked to social constructs that may vary depending on the context, rather than to categories with fixed definitions; and (b) it makes it easier to identify biases against intersectional groups, which depend on combinations of sensitive features. The inputs to our algorithm are a list of target tokens, e.g. names, and a word embedding. It outputs a number of Word Embedding Association Tests (WEATs) that capture various biases present in the data. We illustrate the utility of our approach on publicly available word embeddings and lists of names, and evaluate its output using crowdsourcing. We also show how removing names may not remove potential proxy bias.
Splitting Epistemic Logic Programs
Authors: Pedro Cabalar, Jorge Fandinno, Luis Fariñas del Cerro
Date: 2018-12-20 00:00:00

Epistemic logic programs constitute an extension of the stable models semantics to deal with new constructs called subjective literals. Informally speaking, a subjective literal allows checking whether some regular literal is true in all stable models or in some stable model. As it can be imagined, the associated semantics has proved to be non-trivial, as the truth of the subjective literal may interfere with the set of stable models it is supposed to query. As a consequence, no clear agreement has been reached and different semantic proposals have been made in the literature. Unfortunately, comparison among these proposals has been limited to a study of their effect on individual examples, rather than identifying general properties to be checked. In this paper, we propose an extension of the well-known splitting property for logic programs to the epistemic case. To this aim, we formally define when an arbitrary semantics satisfies the epistemic splitting property and examine some of the consequences that can be derived from that, including its relation to conformant planning and to epistemic constraints. Interestingly, we prove (through counterexamples) that most of the existing proposals fail to fulfill the epistemic splitting property, except the original semantics proposed by Gelfond in 1991.
Subsurface structure analysis using computational interpretation and learning: A visual signal processing perspective
Authors: G. AlRegib, M. Deriche, Z. Long, H. Di, Z. Wang, Y. Alaudah, M. Shafiq, M. Alfarraj
Date: 2018-12-20 00:00:00
Tasks: BIG-bench Machine Learning, Seismic Interpretation

Understanding Earth's subsurface structures has been and continues to be an
essential component of various applications such as environmental monitoring,
carbon sequestration, and oil and gas exploration. By viewing the seismic
volumes that are generated through the processing of recorded seismic traces,
researchers were able to learn from applying advanced image processing and
computer vision algorithms to effectively analyze and understand Earth's
subsurface structures. In this paper, first, we summarize the recent advances
in this direction that relied heavily on the fields of image processing and
computer vision. Second, we discuss the challenges in seismic interpretation
and provide insights and some directions to address such challenges using
emerging machine learning algorithms.
A Bayesian Additive Model for Understanding Public Transport Usage in Special Events
Authors: Filipe Rodrigues, Stanislav S. Borysov, Bernardete Ribeiro, Francisco C. Pereira
Date: 2018-12-20 00:00:00

Public special events, like sports games, concerts and festivals are well
known to create disruptions in transportation systems, often catching the
operators by surprise. Although these are usually planned well in advance,
their impact is difficult to predict, even when organisers and transportation
operators coordinate. The problem highly increases when several events happen
concurrently. To solve these problems, costly processes, heavily reliant on
manual search and personal experience, are usual practice in large cities like
Singapore, London or Tokyo. This paper presents a Bayesian additive model with
Gaussian process components that combines smart card records from public
transport with context information about events that is continuously mined from
the Web. We develop an efficient approximate inference algorithm using
expectation propagation, which allows us to predict the total number of public
transportation trips to the special event areas, thereby contributing to a more
adaptive transportation system. Furthermore, for multiple concurrent event
scenarios, the proposed algorithm is able to disaggregate gross trip counts
into their most likely components related to specific events and routine
behavior. Using real data from Singapore, we show that the presented model
outperforms the best baseline model by up to 26% in R2 and also has explanatory
power for its individual components.
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation
Authors: Filipe Rodrigues, Kristian Henrickson, Francisco C. Pereira
Date: 2018-12-20 00:00:00
Tasks: Gaussian Processes, Imputation, Traffic Data Imputation, Traffic Prediction

Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such user-generated data for many analysis, planning, and decision support applications. However, due to the mechanics of the data collection process, crowdsourced traffic data such as probe-vehicle data is highly prone to missing observations, making accurate imputation crucial for the success of any application that makes use of that type of data. In this article, we propose the use of multi-output Gaussian processes (GPs) to model the complex spatial and temporal patterns in crowdsourced traffic data. While the Bayesian nonparametric formalism of GPs allows us to model observation uncertainty, the multi-output extension based on convolution processes effectively enables us to capture complex spatial dependencies between nearby road segments. Using 6 months of crowdsourced traffic speed data or "probe vehicle data" for several locations in Copenhagen, the proposed approach is empirically shown to significantly outperform popular state-of-the-art imputation methods.