pwc-archive/papers-with-abstracts huggingface dataset:
Learning Loss Functions for Semi-supervised Learning via Discriminative Adversarial Networks
Authors: Cicero Nogueira dos Santos, Kahini Wadhawan, Bo-Wen Zhou
Date: 2017-07-07 00:00:00

We propose discriminative adversarial networks (DAN) for semi-supervised
learning and loss function learning. Our DAN approach builds upon generative
adversarial networks (GANs) and conditional GANs but includes the key
differentiator of using two discriminators instead of a generator and a
discriminator. DAN can be seen as a framework to learn loss functions for
predictors that also implements semi-supervised learning in a straightforward
manner. We propose instantiations of DAN for two different prediction tasks:
classification and ranking. Our experimental results on three datasets of
different tasks demonstrate that DAN is a promising framework for both
semi-supervised learning and learning loss functions for predictors. For all
tasks, the semi-supervised capability of DAN can significantly boost the
predictor performance for small labeled sets with minor architecture changes
across tasks. Moreover, the loss functions automatically learned by DANs are
very competitive and usually outperform the standard pairwise and negative
log-likelihood loss functions for both semi-supervised and supervised learning.
Adversarially Tuned Scene Generation
Authors: V. S. R. Veeravasarapu, Constantin Rothkopf, Ramesh Visvanathan
Date: 2017-01-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: Scene Generation

Generalization performance of trained computer vision systems that use
computer graphics (CG) generated data is not yet effective due to the concept
of 'domain-shift' between virtual and real data. Although simulated data
augmented with a few real world samples has been shown to mitigate domain shift
and improve transferability of trained models, guiding or bootstrapping the
virtual data generation with the distributions learnt from target real world
domain is desired, especially in the fields where annotating even few real
images is laborious (such as semantic labeling, and intrinsic images etc.). In
order to address this problem in an unsupervised manner, our work combines
recent advances in CG (which aims to generate stochastic scene layouts coupled
with large collections of 3D object models) and generative adversarial training
(which aims train generative models by measuring discrepancy between generated
and real data in terms of their separability in the space of a deep
discriminatively-trained classifier). Our method uses iterative estimation of
the posterior density of prior distributions for a generative graphical model.
This is done within a rejection sampling framework. Initially, we assume
uniform distributions as priors on the parameters of a scene described by a
generative graphical model. As iterations proceed the prior distributions get
updated to distributions that are closer to the (unknown) distributions of
target data. We demonstrate the utility of adversarially tuned scene generation
on two real-world benchmark datasets (CityScapes and CamVid) for traffic scene
semantic labeling with a deep convolutional net (DeepLab). We realized
performance improvements by 2.28 and 3.14 points (using the IoU metric) between
the DeepLab models trained on simulated sets prepared from the scene generation
models before and after tuning to CityScapes and CamVid respectively.
A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising
Authors: Sohrab Ferdowsi, Slava Voloshynovskiy, Dimche Kostadinov
Date: 2017-07-07 00:00:00
Tasks: Denoising, Quantization

A learning-based framework for representation of domain-specific images is
proposed where joint compression and denoising can be done using a VQ-based
multi-layer network. While it learns to compress the images from a training
set, the compression performance is very well generalized on images from a test
set. Moreover, when fed with noisy versions of the test set, since it has
priors from clean images, the network also efficiently denoises the test images
during the reconstruction. The proposed framework is a regularized version of
the Residual Quantization (RQ) where at each stage, the quantization error from
the previous stage is further quantized. Instead of codebook learning from the
k-means which over-trains for high-dimensional vectors, we show that only
generating the codewords from a random, but properly regularized distribution
suffices to compress the images globally and without the need to resort to
patch-based division of images. The experiments are done on the
\textit{CroppedYale-B} set of facial images and the method is compared with the
JPEG-2000 codec for compression and BM3D for denoising, showing promising
results.
An Interactive Tool for Natural Language Processing on Clinical Text
Authors: Gaurav Trivedi, Phuong Pham, Wendy Chapman, Rebecca Hwa, Janyce Wiebe, Harry Hochheiser
Date: 2017-07-06 00:00:00
Tasks: BIG-bench Machine Learning

Natural Language Processing (NLP) systems often make use of machine learning
techniques that are unfamiliar to end-users who are interested in analyzing
clinical records. Although NLP has been widely used in extracting information
from clinical text, current systems generally do not support model revision
based on feedback from domain experts.
  We present a prototype tool that allows end users to visualize and review the
outputs of an NLP system that extracts binary variables from clinical text. Our
tool combines multiple visualizations to help the users understand these
results and make any necessary corrections, thus forming a feedback loop and
helping improve the accuracy of the NLP models. We have tested our prototype in
a formative think-aloud user study with clinicians and researchers involved in
colonoscopy research. Results from semi-structured interviews and a System
Usability Scale (SUS) analysis show that the users are able to quickly start
refining NLP models, despite having very little or no experience with machine
learning. Observations from these sessions suggest revisions to the interface
to better support review workflow and interpretation of results.
Methods for finding leader--follower equilibria with multiple followers
Authors: Nicola Basilico, Stefano Coniglio, Nicola Gatti
Date: 2017-07-07 00:00:00
Tasks: global-optimization

The concept of leader--follower (or Stackelberg) equilibrium plays a central
role in a number of real--world applications of game theory. While the case
with a single follower has been thoroughly investigated, results with multiple
followers are only sporadic and the problem of designing and evaluating
computationally tractable equilibrium-finding algorithms is still largely open.
In this work, we focus on the fundamental case where multiple followers play a
Nash equilibrium once the leader has committed to a strategy---as we
illustrate, the corresponding equilibrium finding problem can be easily shown
to be $\mathcal{FNP}$--hard and not in Poly--$\mathcal{APX}$ unless
$\mathcal{P} = \mathcal{NP}$ and therefore it is one among the hardest problems
to solve and approximate. We propose nonconvex mathematical programming
formulations and global optimization methods to find both exact and approximate
equilibria, as well as a heuristic black box algorithm. All the methods and
formulations that we introduce are thoroughly evaluated computationally.
Deep Character-Level Click-Through Rate Prediction for Sponsored Search
Authors: Bora Edizel, Amin Mantrach, Xiao Bai
Date: 2017-07-07 00:00:00
Tasks: Click-Through Rate Prediction

Predicting the click-through rate of an advertisement is a critical component
of online advertising platforms. In sponsored search, the click-through rate
estimates the probability that a displayed advertisement is clicked by a user
after she submits a query to the search engine. Commercial search engines
typically rely on machine learning models trained with a large number of
features to make such predictions. This is inevitably requires a lot of
engineering efforts to define, compute, and select the appropriate features. In
this paper, we propose two novel approaches (one working at character level and
the other working at word level) that use deep convolutional neural networks to
predict the click-through rate of a query-advertisement pair. Specially, the
proposed architectures only consider the textual content appearing in a
query-advertisement pair as input, and produce as output a click-through rate
prediction. By comparing the character-level model with the word-level model,
we show that language representation can be learnt from scratch at character
level when trained on enough data. Through extensive experiments using billions
of query-advertisement pairs of a popular commercial search engine, we
demonstrate that both approaches significantly outperform a baseline model
built on well-selected text features and a state-of-the-art word2vec-based
approach. Finally, by combining the predictions of the deep models introduced
in this study with the prediction of the model in production of the same
commercial search engine, we significantly improve the accuracy and the
calibration of the click-through rate prediction of the production system.
Note Value Recognition for Piano Transcription Using Markov Random Fields
Authors: Eita Nakamura, Kazuyoshi Yoshii, Simon Dixon
Date: 2017-03-23 00:00:00
Tasks: Music Transcription

This paper presents a statistical method for use in music transcription that
can estimate score times of note onsets and offsets from polyphonic MIDI
performance signals. Because performed note durations can deviate largely from
score-indicated values, previous methods had the problem of not being able to
accurately estimate offset score times (or note values) and thus could only
output incomplete musical scores. Based on observations that the pitch context
and onset score times are influential on the configuration of note values, we
construct a context-tree model that provides prior distributions of note values
using these features and combine it with a performance model in the framework
of Markov random fields. Evaluation results show that our method reduces the
average error rate by around 40 percent compared to existing/simple methods. We
also confirmed that, in our model, the score model plays a more important role
than the performance model, and it automatically captures the voice structure
by unsupervised learning.
Simple Online and Realtime Tracking
Authors: Alex Bewley, ZongYuan Ge, Lionel Ott, Fabio Ramos, Ben Upcroft
Date: 2016-02-02 00:00:00
Tasks: Multi-Object Tracking, Multiple Object Tracking

This paper explores a pragmatic approach to multiple object tracking where
the main focus is to associate objects efficiently for online and realtime
applications. To this end, detection quality is identified as a key factor
influencing tracking performance, where changing the detector can improve
tracking by up to 18.9%. Despite only using a rudimentary combination of
familiar techniques such as the Kalman Filter and Hungarian algorithm for the
tracking components, this approach achieves an accuracy comparable to
state-of-the-art online trackers. Furthermore, due to the simplicity of our
tracking method, the tracker updates at a rate of 260 Hz which is over 20x
faster than other state-of-the-art trackers.
A case study of Empirical Bayes in User-Movie Recommendation system
Authors: Arabin Kumar Dey, Raghav Somani, Sreangsu Acharyya
Date: 2017-07-07 00:00:00
Tasks: Movie Recommendation

In this article we provide a formulation of empirical bayes described by
Atchade (2011) to tune the hyperparameters of priors used in bayesian set up of
collaborative filter. We implement the same in MovieLens small dataset. We see
that it can be used to get a good initial choice for the parameters. It can
also be used to guess an initial choice for hyper-parameters in grid search
procedure even for the datasets where MCMC oscillates around the true value or
takes long time to converge.
Mobile Quantification and Therapy Course Tracking for Gait Rehabilitation
Authors: Javier Conte Alcaraz, Sanam Moghaddamnia, Jürgen Peissig
Date: 2017-07-07 00:00:00

This paper presents a novel autonomous quality metric to quantify the
rehabilitations progress of subjects with knee/hip operations. The presented
method supports digital analysis of human gait patterns using smartphones. The
algorithm related to the autonomous metric utilizes calibrated acceleration,
gyroscope and magnetometer signals from seven Inertial Measurement Unit
attached on the lower body in order to classify and generate the grading system
values. The developed Android application connects the seven Inertial
Measurement Units via Bluetooth and performs the data acquisition and
processing in real-time. In total nine features per acceleration direction and
lower body joint angle are calculated and extracted in real-time to achieve a
fast feedback to the user. We compare the classification accuracy and
quantification capabilities of Linear Discriminant Analysis, Principal
Component Analysis and Naive Bayes algorithms. The presented system is able to
classify patients and control subjects with an accuracy of up to 100\%. The
outcomes can be saved on the device or transmitted to treating physicians for
later control of the subject's improvements and the efficiency of physiotherapy
treatments in motor rehabilitation. The proposed autonomous quality metric
solution bears great potential to be used and deployed to support digital
healthcare and therapy.
Deep Discrete Hashing with Self-supervised Pairwise Labels
Authors: Jingkuan Song, Tao He, Hangbo Fan, Lianli Gao
Date: 2017-07-07 00:00:00
Tasks: Deep Hashing, General Classification, Image Retrieval, Object Recognition, Retrieval

Hashing methods have been widely used for applications of large-scale image
retrieval and classification. Non-deep hashing methods using handcrafted
features have been significantly outperformed by deep hashing methods due to
their better feature representation and end-to-end learning framework. However,
the most striking successes in deep hashing have mostly involved discriminative
models, which require labels. In this paper, we propose a novel unsupervised
deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image
retrieval and classification. In the proposed framework, we address two main
problems: 1) how to directly learn discrete binary codes? 2) how to equip the
binary representation with the ability of accurate image retrieval and
classification in an unsupervised way? We resolve these problems by introducing
an intermediate variable and a loss function steering the learning process,
which is based on the neighborhood structure in the original space.
Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17)
demonstrate that our DDH significantly outperforms existing hashing methods by
large margin in terms of~mAP for image retrieval and object recognition. Code
is available at \url{https://github.com/htconquer/ddh}.
ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network
Authors: Abhijit Guha Roy, Sailesh Conjeti, Sri Phani Krishna Karri, Debdoot Sheet, Amin Katouzian, Christian Wachinger, Nassir Navab
Date: 2017-04-07 00:00:00
Tasks: Segmentation, Semantic Segmentation

Optical coherence tomography (OCT) is used for non-invasive diagnosis of
diabetic macular edema assessing the retinal layers. In this paper, we propose
a new fully convolutional deep architecture, termed ReLayNet, for end-to-end
segmentation of retinal layers and fluid masses in eye OCT scans. ReLayNet uses
a contracting path of convolutional blocks (encoders) to learn a hierarchy of
contextual features, followed by an expansive path of convolutional blocks
(decoders) for semantic segmentation. ReLayNet is trained to optimize a joint
loss function comprising of weighted logistic regression and Dice overlap loss.
The framework is validated on a publicly available benchmark dataset with
comparisons against five state-of-the-art segmentation methods including two
deep learning based approaches to substantiate its effectiveness.
Unifying task specification in reinforcement learning
Authors: Martha White
Date: 2016-09-07 00:00:00
Proceeding: ICML 2017 8
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning tasks are typically specified as Markov decision processes. This formalism has been highly successful, though specifications often couple the dynamics of the environment and the learning objective. This lack of modularity can complicate generalization of the task specification, as well as obfuscate connections between different task settings, such as episodic and continuing. In this work, we introduce the RL task formalism, that provides a unification through simple constructs including a generalization to transition-based discounting. Through a series of examples, we demonstrate the generality and utility of this formalism. Finally, we extend standard learning constructs, including Bellman operators, and extend some seminal theoretical results, including approximation errors bounds. Overall, we provide a well-understood and sound formalism on which to build theoretical results and simplify algorithm use and development.
Bayesian Models of Data Streams with Hierarchical Power Priors
Authors: Andres Masegosa, Thomas D. Nielsen, Helge Langseth, Dario Ramos-Lopez, Antonio Salmeron, Anders L. Madsen
Date: 2017-07-07 00:00:00
Proceeding: ICML 2017 8
Tasks: Computational Efficiency, Variational Inference

Making inferences from data streams is a pervasive problem in many modern
data analysis applications. But it requires to address the problem of
continuous model updating and adapt to changes or drifts in the underlying data
generating distribution. In this paper, we approach these problems from a
Bayesian perspective covering general conjugate exponential models. Our
proposal makes use of non-conjugate hierarchical priors to explicitly model
temporal changes of the model parameters. We also derive a novel variational
inference scheme which overcomes the use of non-conjugate priors while
maintaining the computational efficiency of variational methods over conjugate
models. The approach is validated on three real data sets over three latent
variable models.
On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis
Authors: Jose Camacho-Collados, Mohammad Taher Pilehvar
Date: 2017-07-06 00:00:00
Proceeding: WS 2018 11
Tasks: Sentiment Analysis, Text Categorization, Text Classification, Word Embeddings

Text preprocessing is often the first step in the pipeline of a Natural
Language Processing (NLP) system, with potential impact in its final
performance. Despite its importance, text preprocessing has not received much
attention in the deep learning literature. In this paper we investigate the
impact of simple text preprocessing decisions (particularly tokenizing,
lemmatizing, lowercasing and multiword grouping) on the performance of a
standard neural text classifier. We perform an extensive evaluation on standard
benchmarks from text categorization and sentiment analysis. While our
experiments show that a simple tokenization of input text is generally
adequate, they also highlight significant degrees of variability across
preprocessing techniques. This reveals the importance of paying attention to
this usually-overlooked step in the pipeline, particularly when comparing
different models. Finally, our evaluation provides insights into the best
preprocessing practices for training word embeddings.
Imagination improves Multimodal Translation
Authors: Desmond Elliott, Ákos Kádár
Date: 2017-05-11 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Decoder, Prediction, Translation

We decompose multimodal translation into two sub-tasks: learning to translate
and learning visually grounded representations. In a multitask learning
framework, translations are learned in an attention-based encoder-decoder, and
grounded representations are learned through image representation prediction.
Our approach improves translation performance compared to the state of the art
on the Multi30K dataset. Furthermore, it is equally effective if we train the
image prediction task on the external MS COCO dataset, and we find improvements
if we train the translation model on the external News Commentary parallel
text.
Multi-Label Learning with Label Enhancement
Authors: Ruifeng Shao, Ning Xu, Xin Geng
Date: 2017-06-26 00:00:00
Tasks: Multi-Label Learning

The task of multi-label learning is to predict a set of relevant labels for
the unseen instance. Traditional multi-label learning algorithms treat each
class label as a logical indicator of whether the corresponding label is
relevant or irrelevant to the instance, i.e., +1 represents relevant to the
instance and -1 represents irrelevant to the instance. Such label represented
by -1 or +1 is called logical label. Logical label cannot reflect different
label importance. However, for real-world multi-label learning problems, the
importance of each possible label is generally different. For the real
applications, it is difficult to obtain the label importance information
directly. Thus we need a method to reconstruct the essential label importance
from the logical multilabel data. To solve this problem, we assume that each
multi-label instance is described by a vector of latent real-valued labels,
which can reflect the importance of the corresponding labels. Such label is
called numerical label. The process of reconstructing the numerical labels from
the logical multi-label data via utilizing the logical label information and
the topological structure in the feature space is called Label Enhancement. In
this paper, we propose a novel multi-label learning framework called LEMLL,
i.e., Label Enhanced Multi-Label Learning, which incorporates regression of the
numerical labels and label enhancement into a unified framework. Extensive
comparative studies validate that the performance of multi-label learning can
be improved significantly with label enhancement and LEMLL can effectively
reconstruct latent label importance information from logical multi-label data.
External Evaluation of Event Extraction Classifiers for Automatic Pathway Curation: An extended study of the mTOR pathway
Authors: Wojciech Kusa, Michael Spranger
Date: 2017-07-07 00:00:00
Proceeding: WS 2017 8
Tasks: BIG-bench Machine Learning, Event Extraction

This paper evaluates the impact of various event extraction systems on
automatic pathway curation using the popular mTOR pathway. We quantify the
impact of training data sets as well as different machine learning classifiers
and show that some improve the quality of automatically extracted pathways.
Image Segmentation Algorithms Overview
Authors: Song Yuheng, Yan Hao
Date: 2017-07-07 00:00:00
Tasks: Clustering, Edge Detection, Face Recognition, Image Segmentation, Pedestrian Detection, Segmentation, Semantic Segmentation, Weakly-supervised Learning

The technology of image segmentation is widely used in medical image
processing, face recognition pedestrian detection, etc. The current image
segmentation techniques include region-based segmentation, edge detection
segmentation, segmentation based on clustering, segmentation based on
weakly-supervised learning in CNN, etc. This paper analyzes and summarizes
these algorithms of image segmentation, and compares the advantages and
disadvantages of different algorithms. Finally, we make a prediction of the
development trend of image segmentation with the combination of these
algorithms.