pwc-archive/papers-with-abstracts huggingface dataset:
Vectorization of hypotheses and speech for faster beam search in encoder decoder-based speech recognition
Authors: Hiroshi Seki, Takaaki Hori, Shinji Watanabe
Date: 2018-11-12 00:00:00
Tasks: Decoder, GPU, speech-recognition, Speech Recognition

Attention-based encoder decoder network uses a left-to-right beam search
algorithm in the inference step. The current beam search expands hypotheses and
traverses the expanded hypotheses at the next time step. This traversal is
implemented using a for-loop program in general, and it leads to speed down of
the recognition process. In this paper, we propose a parallelism technique for
beam search, which accelerates the search process by vectorizing multiple
hypotheses to eliminate the for-loop program. We also propose a technique to
batch multiple speech utterances for off-line recognition use, which reduces
the for-loop program with regard to the traverse of multiple utterances. This
extension is not trivial during beam search unlike during training due to
several pruning and thresholding techniques for efficient decoding. In
addition, our method can combine scores of external modules, RNNLM and CTC, in
a batch as shallow fusion. We achieved 3.7 x speedup compared with the original
beam search algorithm by vectoring hypotheses, and achieved 10.5 x speedup by
further changing processing unit to GPU.
Learning Latent Dynamics for Planning from Pixels
Authors: Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
Date: 2018-11-12 00:00:00
Tasks: continuous-control, Continuous Control, Motion Planning, Variational Inference

Planning has been very successful for control tasks with known environment dynamics. To leverage planning in unknown environments, the agent needs to learn the dynamics from interactions with the world. However, learning dynamics models that are accurate enough for planning has been a long-standing challenge, especially in image-based domains. We propose the Deep Planning Network (PlaNet), a purely model-based agent that learns the environment dynamics from images and chooses actions through fast online planning in latent space. To achieve high performance, the dynamics model must accurately predict the rewards ahead for multiple time steps. We approach this using a latent dynamics model with both deterministic and stochastic transition components. Moreover, we propose a multi-step variational inference objective that we name latent overshooting. Using only pixel observations, our agent solves continuous control tasks with contact dynamics, partial observability, and sparse rewards, which exceed the difficulty of tasks that were previously solved by planning with learned models. PlaNet uses substantially fewer episodes and reaches final performance close to and sometimes higher than strong model-free algorithms.
Recent Research Advances on Interactive Machine Learning
Authors: Liu Jiang, Shixia Liu, Changjian Chen
Date: 2018-11-12 00:00:00
Tasks: BIG-bench Machine Learning

Interactive Machine Learning (IML) is an iterative learning process that
tightly couples a human with a machine learner, which is widely used by
researchers and practitioners to effectively solve a wide variety of real-world
application problems. Although recent years have witnessed the proliferation of
IML in the field of visual analytics, most recent surveys either focus on a
specific area of IML or aim to summarize a visualization field that is too
generic for IML. In this paper, we systematically review the recent literature
on IML and classify them into a task-oriented taxonomy built by us. We conclude
the survey with a discussion of open challenges and research opportunities that
we believe are inspiring for future work in IML.
Visual Saliency Maps Can Apply to Facial Expression Recognition
Authors: Zhenyue Qin, Jie Wu
Date: 2018-11-12 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), General Classification, Saliency Prediction

Human eyes concentrate different facial regions during distinct cognitive
activities. We study utilising facial visual saliency maps to classify
different facial expressions into different emotions. Our results show that our
novel method of merely using facial saliency maps can achieve a descent
accuracy of 65\%, much higher than the chance level of $1/7$. Furthermore, our
approach is of semi-supervision, i.e., our facial saliency maps are generated
from a general saliency prediction algorithm that is not explicitly designed
for face images. We also discovered that the classification accuracies of each
emotional class using saliency maps demonstrate a strong positive correlation
with the accuracies produced by face images. Our work implies that humans may
look at different facial areas in order to perceive different emotions.
Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy
Authors: Naman Patel, Apoorva Nandini Saridena, Anna Choromanska, Prashanth Krishnamurthy, Farshad Khorrami
Date: 2018-11-12 00:00:00
Tasks: Anomaly Detection, Generative Adversarial Network, Video Prediction

The paper proposes an on-line monitoring framework for continuous real-time
safety/security in learning-based control systems (specifically application to
a unmanned ground vehicle). We monitor validity of mappings from sensor inputs
to actuator commands, controller-focused anomaly detection (CFAM), and from
actuator commands to sensor inputs, system-focused anomaly detection (SFAM).
CFAM is an image conditioned energy based generative adversarial network
(EBGAN) in which the energy based discriminator distinguishes between proper
and anomalous actuator commands. SFAM is based on an action condition video
prediction framework to detect anomalies between predicted and observed
temporal evolution of sensor data. We demonstrate the effectiveness of the
approach on our autonomous ground vehicle for indoor environments and on
Udacity dataset for outdoor environments.
Identification of Internal Faults in Indirect Symmetrical Phase Shift Transformers Using Ensemble Learning
Authors: Pallav Kumar Bera, Rajesh Kumar, Can Isik
Date: 2018-11-12 00:00:00
Tasks: Ensemble Learning

This paper proposes methods to identify 40 different types of internal faults
in an Indirect Symmetrical Phase Shift Transformer (ISPST). The ISPST was
modeled using Power System Computer Aided Design (PSCAD)/ Electromagnetic
Transients including DC (EMTDC). The internal faults were simulated by varying
the transformer tapping, backward and forward phase shifts, loading, and
percentage of winding faulted. Data for 960 cases of each type of fault was
recorded. A series of features were extracted for a, b, and c phases from time,
frequency, time-frequency, and information theory domains. The importance of
the extracted features was evaluated through univariate tests which helped to
reduce the number of features. The selected features were then used for
training five state-of-the-art machine learning classifiers. Extremely Random
Trees and Random Forest, the ensemble-based learners, achieved the accuracy of
98.76% and 97.54% respectively outperforming Multilayer Perceptron (96.13%),
Logistic Regression (93.54%), and Support Vector Machines (92.60%)
Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN
Authors: Janpreet Singh, Shashank Shekhar
Date: 2018-11-12 00:00:00
Tasks: Autonomous Driving, Classification, General Classification, Instance Segmentation, object-detection, Object Detection, Road Damage Detection, Semantic Segmentation

This paper summarizes the design, experiments and results of our solution to
the Road Damage Detection and Classification Challenge held as part of the 2018
IEEE International Conference On Big Data Cup. Automatic detection and
classification of damage in roads is an essential problem for multiple
applications like maintenance and autonomous driving. We demonstrate that
convolutional neural net based instance detection and classfication approaches
can be used to solve this problem. In particular we show that Mask-RCNN, one of
the state-of-the-art algorithms for object detection, localization and instance
segmentation of natural images, can be used to perform this task in a fast
manner with effective results. We achieve a mean F1 score of 0.528 at an IoU of
50% on the task of detection and classification of different types of damages
in real-world road images acquired using a smartphone camera and our average
inference time for each image is 0.105 seconds on an NVIDIA GeForce 1080Ti
graphic card. The code and saved models for our approach can be found here :
https://github.com/sshkhr/BigDataCup18 Submission
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
Authors: Qijie Zhao, Tao Sheng, Yongtao Wang, Zhi Tang, Ying Chen, Ling Cai, Haibin Ling
Date: 2018-11-12 00:00:00
Tasks: Decoder, Object, object-detection, Object Detection

Feature pyramids are widely exploited by both the state-of-the-art one-stage
object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object
detectors (e.g., Mask R-CNN, DetNet) to alleviate the problem arising from
scale variation across object instances. Although these object detectors with
feature pyramids achieve encouraging results, they have some limitations due to
that they only simply construct the feature pyramid according to the inherent
multi-scale, pyramidal architecture of the backbones which are actually
designed for object classification task. Newly, in this work, we present a
method called Multi-Level Feature Pyramid Network (MLFPN) to construct more
effective feature pyramids for detecting objects of different scales. First, we
fuse multi-level features (i.e. multiple layers) extracted by backbone as the
base feature. Second, we feed the base feature into a block of alternating
joint Thinned U-shape Modules and Feature Fusion Modules and exploit the
decoder layers of each u-shape module as the features for detecting objects.
Finally, we gather up the decoder layers with equivalent scales (sizes) to
develop a feature pyramid for object detection, in which every feature map
consists of the layers (features) from multiple levels. To evaluate the
effectiveness of the proposed MLFPN, we design and train a powerful end-to-end
one-stage object detector we call M2Det by integrating it into the architecture
of SSD, which gets better detection performance than state-of-the-art one-stage
detectors. Specifically, on MS-COCO benchmark, M2Det achieves AP of 41.0 at
speed of 11.8 FPS with single-scale inference strategy and AP of 44.2 with
multi-scale inference strategy, which is the new state-of-the-art results among
one-stage detectors. The code will be made available on
\url{https://github.com/qijiezhao/M2Det.
Sequence-Level Knowledge Distillation for Model Compression of Attention-based Sequence-to-Sequence Speech Recognition
Authors: Raden Mu'az Mun'im, Nakamasa Inoue, Koichi Shinoda
Date: 2018-11-12 00:00:00
Tasks: Knowledge Distillation, Model Compression, Sequence-To-Sequence Speech Recognition, speech-recognition, Speech Recognition

We investigate the feasibility of sequence-level knowledge distillation of
Sequence-to-Sequence (Seq2Seq) models for Large Vocabulary Continuous Speech
Recognition (LVSCR). We first use a pre-trained larger teacher model to
generate multiple hypotheses per utterance with beam search. With the same
input, we then train the student model using these hypotheses generated from
the teacher as pseudo labels in place of the original ground truth labels. We
evaluate our proposed method using Wall Street Journal (WSJ) corpus. It
achieved up to $ 9.8 \times$ parameter reduction with accuracy loss of up to
7.0\% word-error rate (WER) increase
Agent Embeddings: A Latent Representation for Pole-Balancing Networks
Authors: Oscar Chang, Robert Kwiatkowski, Siyuan Chen, Hod Lipson
Date: 2018-11-12 00:00:00

We show that it is possible to reduce a high-dimensional object like a neural
network agent into a low-dimensional vector representation with semantic
meaning that we call agent embeddings, akin to word or face embeddings. This
can be done by collecting examples of existing networks, vectorizing their
weights, and then learning a generative model over the weight space in a
supervised fashion. We investigate a pole-balancing task, Cart-Pole, as a case
study and show that multiple new pole-balancing networks can be generated from
their agent embeddings without direct access to training data from the
Cart-Pole simulator. In general, the learned embedding space is helpful for
mapping out the space of solutions for a given task. We observe in the case of
Cart-Pole the surprising finding that good agents make different decisions
despite learning similar representations, whereas bad agents make similar (bad)
decisions while learning dissimilar representations. Linearly interpolating
between the latent embeddings for a good agent and a bad agent yields an agent
embedding that generates a network with intermediate performance, where the
performance can be tuned according to the coefficient of interpolation. Linear
extrapolation in the latent space also results in performance boosts, up to a
point.
An Interpretable Generative Model for Handwritten Digit Image Synthesis
Authors: Yao Zhu, Saksham Suri, Pranav Kulkarni, Yueru Chen, Jiali Duan, C. -C. Jay Kuo
Date: 2018-11-11 00:00:00
Tasks: Handwritten Digit Image Synthesis, Image Generation

An interpretable generative model for handwritten digits synthesis is
proposed in this work. Modern image generative models, such as Generative
Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are trained by
backpropagation (BP). The training process is complex and the underlying
mechanism is difficult to explain. We propose an interpretable multi-stage PCA
method to achieve the same goal and use handwritten digit images synthesis as
an illustrative example. First, we derive principal-component-analysis-based
(PCA-based) transform kernels at each stage based on the covariance of its
inputs. This results in a sequence of transforms that convert input images of
correlated pixels to spectral vectors of uncorrelated components. In other
words, it is a whitening process. Then, we can synthesize an image based on
random vectors and multi-stage transform kernels through a coloring process.
The generative model is a feedforward (FF) design since no BP is used in model
parameter determination. Its design complexity is significantly lower, and the
whole design process is explainable. Finally, we design an FF generative model
using the MNIST dataset, compare synthesis results with those obtained by
state-of-the-art GAN and VAE methods, and show that the proposed generative
model achieves comparable performance.
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
Authors: Aaron Mishkin, Frederik Kunstner, Didrik Nielsen, Mark Schmidt, Mohammad Emtiyaz Khan
Date: 2018-11-11 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Variational Inference

Uncertainty estimation in large deep-learning models is a computationally
challenging task, where it is difficult to form even a Gaussian approximation
to the posterior distribution. In such situations, existing methods usually
resort to a diagonal approximation of the covariance matrix despite, the fact
that these matrices are known to result in poor uncertainty estimates. To
address this issue, we propose a new stochastic, low-rank, approximate
natural-gradient (SLANG) method for variational inference in large, deep
models. Our method estimates a "diagonal plus low-rank" structure based solely
on back-propagated gradients of the network log-likelihood. This requires
strictly less gradient computations than methods that compute the gradient of
the whole variational objective. Empirical evaluations on standard benchmarks
confirm that SLANG enables faster and more accurate estimation of uncertainty
than mean-field methods, and performs comparably to state-of-the-art methods.
Product Title Refinement via Multi-Modal Generative Adversarial Learning
Authors: Jian-Guo Zhang, Pengcheng Zou, Zhao Li, Yao Wan, Ye Liu, Xiuming Pan, Yu Gong, Philip S. Yu
Date: 2018-11-11 00:00:00
Tasks: Attribute, Generative Adversarial Network, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Nowadays, an increasing number of customers are in favor of using E-commerce
Apps to browse and purchase products. Since merchants are usually inclined to
employ redundant and over-informative product titles to attract customers'
attention, it is of great importance to concisely display short product titles
on limited screen of cell phones. Previous researchers mainly consider textual
information of long product titles and lack of human-like view during training
and evaluation procedure. In this paper, we propose a Multi-Modal Generative
Adversarial Network (MM-GAN) for short product title generation, which
innovatively incorporates image information, attribute tags from the product
and the textual information from original long titles. MM-GAN treats short
titles generation as a reinforcement learning process, where the generated
titles are evaluated by the discriminator in a human-like view.
Multiple Subspace Alignment Improves Domain Adaptation
Authors: Kowshik Thopalli, Rushil Anirudh, Jayaraman J. Thiagarajan, Pavan Turaga
Date: 2018-11-11 00:00:00
Tasks: Domain Adaptation, subspace methods, Unsupervised Domain Adaptation

We present a novel unsupervised domain adaptation (DA) method for
cross-domain visual recognition. Though subspace methods have found success in
DA, their performance is often limited due to the assumption of approximating
an entire dataset using a single low-dimensional subspace. Instead, we develop
a method to effectively represent the source and target datasets via a
collection of low-dimensional subspaces, and subsequently align them by
exploiting the natural geometry of the space of subspaces, on the Grassmann
manifold. We demonstrate the effectiveness of this approach, using empirical
studies on two widely used benchmarks, with state of the art domain adaptation
performance
Semi-supervised Deep Representation Learning for Multi-View Problems
Authors: Vahid Noroozi, Sara Bahaadini, Lei Zheng, Sihong Xie, Weixiang Shao, Philip S. Yu
Date: 2018-11-11 00:00:00
Tasks: Dimensionality Reduction, Learning Representation Of Multi-View Data, Representation Learning

While neural networks for learning representation of multi-view data have
been previously proposed as one of the state-of-the-art multi-view dimension
reduction techniques, how to make the representation discriminative with only a
small amount of labeled data is not well-studied. We introduce a
semi-supervised neural network model, named Multi-view Discriminative Neural
Network (MDNN), for multi-view problems. MDNN finds nonlinear view-specific
mappings by projecting samples to a common feature space using multiple coupled
deep networks. It is capable of leveraging both labeled and unlabeled data to
project multi-view data so that samples from different classes are separated
and those from the same class are clustered together. It also uses the
inter-view correlation between views to exploit the available information in
both the labeled and unlabeled data. Extensive experiments conducted on four
datasets demonstrate the effectiveness of the proposed algorithm for multi-view
semi-supervised learning.
Unifying Gaussian LWF and AMP Chain Graphs to Model Interference
Authors: Jose M. Peña
Date: 2018-11-11 00:00:00
Tasks: parameter estimation

An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence. We also propose an algorithm for maximum likelihood parameter estimation for the new models, and report experimental results. Finally, we show how to compute the effects of interventions in the new models.
Managing App Install Ad Campaigns in RTB: A Q-Learning Approach
Authors: Anit Kumar Sahu, Shaunak Mishra, Narayan Bhamidipati
Date: 2018-11-11 00:00:00
Tasks: Q-Learning

Real time bidding (RTB) enables demand side platforms (bidders) to scale ad
campaigns across multiple publishers affiliated to an RTB ad exchange. While
driving multiple campaigns for mobile app install ads via RTB, the bidder
typically has to: (i) maintain each campaign's efficiency (i.e., meet
advertiser's target cost-per-install), (ii) be sensitive to advertiser's
budget, and (iii) make profit after payouts to the ad exchange. In this
process, there is a sense of delayed rewards for the bidder's actions; the
exchange charges the bidder right after the ad is shown, but the bidder gets to
know about resultant installs after considerable delay. This makes it
challenging for the bidder to decide beforehand the bid (and corresponding cost
charged to advertiser) for each ad display opportunity. To jointly handle the
objectives mentioned above, we propose a state space based policy which decides
the exchange bid and advertiser cost for each opportunity. The state space
captures the current efficiency, budget utilization and profit. The policy
based on this state space is trained on past decisions and outcomes via a novel
Q-learning algorithm which accounts for the delay in install notifications. In
our experiments based on data from app install campaigns managed by Yahoo's
Gemini advertising platform, the Q-learning based policy led to a significant
increase in the profit and number of efficient campaigns.
Thompson Sampling for Pursuit-Evasion Problems
Authors: Zhen Li, Nicholas J. Meyer, Eric B. Laber, Robert Brigantic
Date: 2018-11-11 00:00:00
Tasks: Thompson Sampling

Pursuit-evasion is a multi-agent sequential decision problem wherein a group
of agents known as pursuers coordinate their traversal of a spatial domain to
locate an agent trying to evade them. Pursuit evasion problems arise in a
number of import application domains including defense and route planning.
Learning to optimally coordinate pursuer behaviors so as to minimize time to
capture of the evader is challenging because of a large action space and sparse
noisy state information; consequently, previous approaches have relied
primarily on heuristics. We propose a variant of Thompson Sampling for
pursuit-evasion that allows for the application of existing model-based
planning algorithms. This approach is general in that it allows for an
arbitrary number of pursuers, a general spatial domain, and the integration of
auxiliary information provided by informants. In a suite of simulation
experiments, Thompson Sampling for pursuit evasion significantly reduces
time-to-capture relative to competing algorithms.