pwc-archive/papers-with-abstracts huggingface dataset:
Visual Features for Context-Aware Speech Recognition
Authors: Abhinav Gupta, Yajie Miao, Leonardo Neves, Florian Metze
Date: 2017-12-01 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Speech-to-Text

Automatic transcriptions of consumer-generated multi-media content such as
"Youtube" videos still exhibit high word error rates. Such data typically
occupies a very broad domain, has been recorded in challenging conditions, with
cheap hardware and a focus on the visual modality, and may have been
post-processed or edited. In this paper, we extend our earlier work on adapting
the acoustic model of a DNN-based speech recognition system to an RNN language
model and show how both can be adapted to the objects and scenes that can be
automatically detected in the video. We are working on a corpus of "how-to"
videos from the web, and the idea is that an object that can be seen ("car"),
or a scene that is being detected ("kitchen") can be used to condition both
models on the "context" of the recording, thereby reducing perplexity and
improving transcription. We achieve good improvements in both cases and compare
and analyze the respective reductions in word error rate. We expect that our
results can be used for any type of speech processing in which "context"
information is available, for example in robotics, man-machine interaction, or
when indexing large audio-visual archives, and should ultimately help to bring
together the "video-to-text" and "speech-to-text" communities.
Intelligent EHRs: Predicting Procedure Codes From Diagnosis Codes
Authors: Hasham Ul Haq, Rameel Ahmad, Sibt Ul Hussain
Date: 2017-12-01 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In order to submit a claim to insurance companies, a doctor needs to code a
patient encounter with both the diagnosis (ICDs) and procedures performed
(CPTs) in an Electronic Health Record (EHR). Identifying and applying relevant
procedures code is a cumbersome and time-consuming task as a doctor has to
choose from around 13,000 procedure codes with no predefined one-to-one
mapping. In this paper, we propose a state-of-the-art deep learning method for
automatic and intelligent coding of procedures (CPTs) from the diagnosis codes
(ICDs) entered by the doctor. Precisely, we cast the learning problem as a
multi-label classification problem and use distributed representation to learn
the input mapping of high-dimensional sparse ICDs codes. Our final model
trained on 2.3 million claims is able to outperform existing rule-based
probabilistic and association-rule mining based methods and has a recall of
90@3.
Image to Image Translation for Domain Adaptation
Authors: Zak Murez, Soheil Kolouri, David Kriegman, Ravi Ramamoorthi, Kyungnam Kim
Date: 2017-12-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Image-to-Image Translation, Translation, Unsupervised Domain Adaptation

We propose a general framework for unsupervised domain adaptation, which
allows deep neural networks trained on a source domain to be tested on a
different target domain without requiring any training annotations in the
target domain. This is achieved by adding extra networks and losses that help
regularize the features extracted by the backbone encoder network. To this end
we propose the novel use of the recently proposed unpaired image-toimage
translation framework to constrain the features extracted by the encoder
network. Specifically, we require that the features extracted are able to
reconstruct the images in both domains. In addition we require that the
distribution of features extracted from images in the two domains are
indistinguishable. Many recent works can be seen as specific cases of our
general framework. We apply our method for domain adaptation between MNIST,
USPS, and SVHN datasets, and Amazon, Webcam and DSLR Office datasets in
classification tasks, and also between GTA5 and Cityscapes datasets for a
segmentation task. We demonstrate state of the art performance on each of these
datasets.
Subject Selection on a Riemannian Manifold for Unsupervised Cross-subject Seizure Detection
Authors: Samaneh Nasiri Ghosheh Bolagh, Gari. D. Clifford
Date: 2017-12-01 00:00:00
Tasks: Clustering, EEG, Electroencephalogram (EEG), Seizure Detection, Specificity

Inter-subject variability between individuals poses a challenge in
inter-subject brain signal analysis problems. A new algorithm for
subject-selection based on clustering covariance matrices on a Riemannian
manifold is proposed. After unsupervised selection of the subsets of relevant
subjects, data in a cluster is mapped to a tangent space at the mean point of
covariance matrices in that cluster and an SVM classifier on labeled data from
relevant subjects is trained. Experiment on an EEG seizure database shows that
the proposed method increases the accuracy over state-of-the-art from 86.83% to
89.84% and specificity from 87.38% to 89.64% while reducing the false positive
rate/hour from 0.8/hour to 0.77/hour.
Novel Exploration Techniques (NETs) for Malaria Policy Interventions
Authors: Oliver Bent, Sekou L. Remy, Stephen Roberts, Aisha Walcott-Bryant
Date: 2017-12-01 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

The task of decision-making under uncertainty is daunting, especially for
problems which have significant complexity. Healthcare policy makers across the
globe are facing problems under challenging constraints, with limited tools to
help them make data driven decisions. In this work we frame the process of
finding an optimal malaria policy as a stochastic multi-armed bandit problem,
and implement three agent based strategies to explore the policy space. We
apply a Gaussian Process regression to the findings of each agent, both for
comparison and to account for stochastic results from simulating the spread of
malaria in a fixed population. The generated policy spaces are compared with
published results to give a direct reference with human expert decisions for
the same simulated population. Our novel approach provides a powerful resource
for policy makers, and a platform which can be readily extended to capture
future more nuanced policy spaces.
Unsupervised Classification of PolSAR Data Using a Scattering Similarity Measure Derived from a Geodesic Distance
Authors: Debanshu Ratha, Avik Bhattacharya, Alejandro C. Frery
Date: 2017-12-01 00:00:00
Tasks: General Classification

In this letter, we propose a novel technique for obtaining scattering
components from Polarimetric Synthetic Aperture Radar (PolSAR) data using the
geodesic distance on the unit sphere. This geodesic distance is obtained
between an elementary target and the observed Kennaugh matrix, and it is
further utilized to compute a similarity measure between scattering mechanisms.
The normalized similarity measure for each elementary target is then modulated
with the total scattering power (Span). This measure is used to categorize
pixels into three categories i.e. odd-bounce, double-bounce and volume,
depending on which of the above scattering mechanisms dominate. Then the
maximum likelihood classifier of [J.-S. Lee, M. R. Grunes, E. Pottier, and L.
Ferro-Famil, Unsupervised terrain classification preserving polarimetric
scattering characteristics, IEEE Trans. Geos. Rem. Sens., vol. 42, no. 4, pp.
722731, April 2004.] based on the complex Wishart distribution is iteratively
used for each category. Dominant scattering mechanisms are thus preserved in
this classification scheme. We show results for L-band AIRSAR and ALOS-2
datasets acquired over San Francisco and Mumbai, respectively. The scattering
mechanisms are better preserved using the proposed methodology than the
unsupervised classification results using the Freeman-Durden scattering powers
on an orientation angle (OA) corrected PolSAR image. Furthermore, (1) the
scattering similarity is a completely non-negative quantity unlike the negative
powers that might occur in double- bounce and odd-bounce scattering component
under Freeman Durden decomposition (FDD), and (2) the methodology can be
extended to more canonical targets as well as for bistatic scattering.
Lexical and Derivational Meaning in Vector-Based Models of Relativisation
Authors: Michael Moortgat, Gijs Wijnholds
Date: 2017-11-30 00:00:00
Tasks: Object

Sadrzadeh et al (2013) present a compositional distributional analysis of
relative clauses in English in terms of the Frobenius algebraic structure of
finite dimensional vector spaces. The analysis relies on distinct type
assignments and lexical recipes for subject vs object relativisation. The
situation for Dutch is different: because of the verb final nature of Dutch,
relative clauses are ambiguous between a subject vs object relativisation
reading. Using an extended version of Lambek calculus, we present a
compositional distributional framework that accounts for this derivational
ambiguity, and that allows us to give a single meaning recipe for the relative
pronoun reconciling the Frobenius semantics with the demands of Dutch
derivational syntax.
The reparameterization trick for acquisition functions
Authors: James T. Wilson, Riccardo Moriconi, Frank Hutter, Marc Peter Deisenroth
Date: 2017-12-01 00:00:00
Tasks: Bayesian Optimization, global-optimization

Bayesian optimization is a sample-efficient approach to solving global
optimization problems. Along with a surrogate model, this approach relies on
theoretically motivated value heuristics (acquisition functions) to guide the
search process. Maximizing acquisition functions yields the best performance;
unfortunately, this ideal is difficult to achieve since optimizing acquisition
functions per se is frequently non-trivial. This statement is especially true
in the parallel setting, where acquisition functions are routinely non-convex,
high-dimensional, and intractable. Here, we demonstrate how many popular
acquisition functions can be formulated as Gaussian integrals amenable to the
reparameterization trick and, ensuingly, gradient-based optimization. Further,
we use this reparameterized representation to derive an efficient Monte Carlo
estimator for the upper confidence bound acquisition function in the context of
parallel selection.
Deep Learning Scaling is Predictable, Empirically
Authors: Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, Yanqi Zhou
Date: 2017-12-01 00:00:00
Tasks: Deep Learning, Language Modeling, Language Modelling, Machine Translation, Neural Architecture Search, speech-recognition, Speech Recognition

Deep learning (DL) creates impactful advances following a virtuous recipe:
model architecture search, creating large training data sets, and scaling
computation. It is widely believed that growing training sets and models should
improve accuracy and result in better products. As DL application domains grow,
we would like a deeper understanding of the relationships between training set
size, computational scale, and model accuracy improvements to advance the
state-of-the-art.
  This paper presents a large scale empirical characterization of
generalization error and model size growth as training sets grow. We introduce
a methodology for this measurement and test four machine learning domains:
machine translation, language modeling, image processing, and speech
recognition. Our empirical results show power-law generalization error scaling
across a breadth of factors, resulting in power-law exponents---the "steepness"
of the learning curve---yet to be explained by theoretical work. Further, model
improvements only shift the error but do not appear to affect the power-law
exponent. We also show that model size scales sublinearly with data size. These
scaling relationships have significant implications on deep learning research,
practice, and systems. They can assist model debugging, setting accuracy
targets, and decisions about data set growth. They can also guide computing
system design and underscore the importance of continued computational scaling.
Automatic Discovery and Geotagging of Objects from Street View Imagery
Authors: Vladimir A. Krylov, Eamonn Kenny, Rozenn Dahyot
Date: 2017-08-28 00:00:00
Tasks: Autonomous Navigation, Depth Estimation, Monocular Depth Estimation

Many applications such as autonomous navigation, urban planning and asset
monitoring, rely on the availability of accurate information about objects and
their geolocations. In this paper we propose to automatically detect and
compute the GPS coordinates of recurring stationary objects of interest using
street view imagery. Our processing pipeline relies on two fully convolutional
neural networks: the first segments objects in the images while the second
estimates their distance from the camera. To geolocate all the detected objects
coherently we propose a novel custom Markov Random Field model to perform
objects triangulation. The novelty of the resulting pipeline is the combined
use of monocular depth estimation and triangulation to enable automatic mapping
of complex scenes with multiple visually similar objects of interest. We
validate experimentally the effectiveness of our approach on two object
classes: traffic lights and telegraph poles. The experiments report high object
recall rates and GPS accuracy within 2 meters, which is comparable with the
precision of single-frequency GPS receivers.
DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning
Authors: Mengwei Xu, Feng Qian, Mengze Zhu, Feifan Huang, Saumay Pushp, Xuanzhe Liu
Date: 2017-12-01 00:00:00
Tasks: Deep Learning

Due to their on-body and ubiquitous nature, wearables can generate a wide range of unique sensor data creating countless opportunities for deep learning tasks. We propose DeepWear, a deep learning (DL) framework for wearable devices to improve the performance and reduce the energy footprint. DeepWear strategically offloads DL tasks from a wearable device to its paired handheld device through local network. Compared to the remote-cloud-based offloading, DeepWear requires no Internet connectivity, consumes less energy, and is robust to privacy breach. DeepWear provides various novel techniques such as context-aware offloading, strategic model partition, and pipelining support to efficiently utilize the processing capacity from nearby paired handhelds. Deployed as a user-space library, DeepWear offers developer-friendly APIs that are as simple as those in traditional DL libraries such as TensorFlow. We have implemented DeepWear on the Android OS and evaluated it on COTS smartphones and smartwatches with real DL models. DeepWear brings up to 5.08X and 23.0X execution speedup, as well as 53.5% and 85.5% energy saving compared to wearable-only and handheld-only strategies, respectively.
Embodied Question Answering
Authors: Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, Dhruv Batra
Date: 2017-11-30 00:00:00
Proceeding: CVPR 2018 6
Tasks: Embodied Question Answering, Navigate, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where
an agent is spawned at a random location in a 3D environment and asked a
question ("What color is the car?"). In order to answer, the agent must first
intelligently navigate to explore the environment, gather information through
first-person (egocentric) vision, and then answer the question ("orange").
  This challenging task requires a range of AI skills -- active perception,
language understanding, goal-driven navigation, commonsense reasoning, and
grounding of language into actions. In this work, we develop the environments,
end-to-end-trained reinforcement learning agents, and evaluation protocols for
EmbodiedQA.
DeepCache: Principled Cache for Mobile Deep Vision
Authors: Mengwei Xu, Mengze Zhu, Yunxin Liu, Felix Xiaozhu Lin, Xuanzhe Liu
Date: 2017-12-01 00:00:00
Tasks: Video Compression

We present DeepCache, a principled cache design for deep learning inference in continuous mobile vision. DeepCache benefits model execution efficiency by exploiting temporal locality in input video streams. It addresses a key challenge raised by mobile vision: the cache must operate under video scene variation, while trading off among cacheability, overhead, and loss in model accuracy. At the input of a model, DeepCache discovers video temporal locality by exploiting the video's internal structure, for which it borrows proven heuristics from video compression; into the model, DeepCache propagates regions of reusable results by exploiting the model's internal structure. Notably, DeepCache eschews applying video heuristics to model internals which are not pixels but high-dimensional, difficult-to-interpret data. Our implementation of DeepCache works with unmodified deep learning models, requires zero developer's manual effort, and is therefore immediately deployable on off-the-shelf mobile devices. Our experiments show that DeepCache saves inference execution time by 18% on average and up to 47%. DeepCache reduces system energy consumption by 20% on average.
Learning to Segment Moving Objects
Authors: Pavel Tokmakov, Cordelia Schmid, Karteek Alahari
Date: 2017-12-01 00:00:00
Tasks: Motion Estimation, Motion Segmentation, Object, Object Recognition, Optical Flow Estimation, Unsupervised Video Object Segmentation

We study the problem of segmenting moving objects in unconstrained videos.
Given a video, the task is to segment all the objects that exhibit independent
motion in at least one frame. We formulate this as a learning problem and
design our framework with three cues: (i) independent object motion between a
pair of frames, which complements object recognition, (ii) object appearance,
which helps to correct errors in motion estimation, and (iii) temporal
consistency, which imposes additional constraints on the segmentation. The
framework is a two-stream neural network with an explicit memory module. The
two streams encode appearance and motion cues in a video sequence respectively,
while the memory module captures the evolution of objects over time, exploiting
the temporal consistency. The motion stream is a convolutional neural network
trained on synthetic videos to segment independently moving objects in the
optical flow field. The module to build a 'visual memory' in video, i.e., a
joint representation of all the video frames, is realized with a convolutional
recurrent unit learned from a small number of training video sequences.
  For every pixel in a frame of a test video, our approach assigns an object or
background label based on the learned spatio-temporal features as well as the
'visual memory' specific to the video. We evaluate our method extensively on
three benchmarks, DAVIS, Freiburg-Berkeley motion segmentation dataset and
SegTrack. In addition, we provide an extensive ablation study to investigate
both the choice of the training data and the influence of each component in the
proposed framework.
The mind as a computational system
Authors: Christoph Adami
Date: 2017-12-01 00:00:00
Tasks: Philosophy

The present document is an excerpt of an essay that I wrote as part of my
application material to graduate school in Computer Science (with a focus on
Artificial Intelligence), in 1986. I was not invited by any of the schools that
received it, so I became a theoretical physicist instead. The essay's full
title was "Some Topics in Philosophy and Computer Science". I am making this
text (unchanged from 1985, preserving the typesetting as much as possible)
available now in memory of Jerry Fodor, whose writings had influenced me
significantly at the time (even though I did not always agree).
Probabilistic Adaptive Computation Time
Authors: Michael Figurnov, Artem Sobolev, Dmitry Vetrov
Date: 2017-12-01 00:00:00

We present a probabilistic model with discrete latent variables that control
the computation time in deep learning models such as ResNets and LSTMs. A prior
on the latent variables expresses the preference for faster computation. The
amount of computation for an input is determined via amortized maximum a
posteriori (MAP) inference. MAP inference is performed using a novel stochastic
variational optimization method. The recently proposed Adaptive Computation
Time mechanism can be seen as an ad-hoc relaxation of this model. We
demonstrate training using the general-purpose Concrete relaxation of discrete
variables. Evaluation on ResNet shows that our method matches the
speed-accuracy trade-off of Adaptive Computation Time, while allowing for
evaluation with a simple deterministic procedure that has a lower memory
footprint.
Deep Neural Network Detects Quantum Phase Transition
Authors: Shunta Arai, Masayuki Ohzeki, kazuyuki Tanaka
Date: 2017-12-01 00:00:00

We detect the quantum phase transition of a quantum many-body system by
mapping the observed results of the quantum state onto a neural network. In the
present study, we utilized the simplest case of a quantum many-body system,
namely a one-dimensional chain of Ising spins with the transverse Ising model.
We prepared several spin configurations, which were obtained using repeated
observations of the model for a particular strength of the transverse field, as
input data for the neural network. Although the proposed method can be employed
using experimental observations of quantum many-body systems, we tested our
technique with spin configurations generated by a quantum Monte Carlo
simulation without initial relaxation. The neural network successfully
classified the strength of transverse field only from the spin configurations,
leading to consistent estimations of the critical point of our model $\Gamma_c
=J$.
Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning
Authors: Adrien Lagrange, Mathieu Fauvel, Stéphane May, Nicolas Dobigeon
Date: 2017-12-01 00:00:00
Tasks: Classification, Clustering, Dimensionality Reduction, General Classification

Within a supervised classification framework, labeled data are used to learn
classifier parameters. Prior to that, it is generally required to perform
dimensionality reduction via feature extraction. These preprocessing steps have
motivated numerous research works aiming at recovering latent variables in an
unsupervised context. This paper proposes a unified framework to perform
classification and low-level modeling jointly. The main objective is to use the
estimated latent variables as features for classification and to incorporate
simultaneously supervised information to help latent variable extraction. The
proposed hierarchical Bayesian model is divided into three stages: a first
low-level modeling stage to estimate latent variables, a second stage
clustering these features into statistically homogeneous groups and a last
classification stage exploiting the (possibly badly) labeled data. Performance
of the model is assessed in the specific context of hyperspectral image
interpretation, unifying two standard analysis techniques, namely unmixing and
classification.