pwc-archive/papers-with-abstracts huggingface dataset:
Locally-Supervised Deep Hybrid Model for Scene Recognition
Authors: Sheng Guo, Weilin Huang, Li-Min Wang, Yu Qiao
Date: 2016-01-27 00:00:00
Tasks: General Classification, image-classification, Image Classification, model, Scene Recognition

Convolutional neural networks (CNN) have recently achieved remarkable
successes in various image classification and understanding tasks. The deep
features obtained at the top fully-connected layer of the CNN (FC-features)
exhibit rich global semantic information and are extremely effective in image
classification. On the other hand, the convolutional features in the middle
layers of the CNN also contain meaningful local information, but are not fully
explored for image representation. In this paper, we propose a novel
Locally-Supervised Deep Hybrid Model (LS-DHM) that effectively enhances and
explores the convolutional features for scene recognition. Firstly, we notice
that the convolutional features capture local objects and fine structures of
scene images, which yield important cues for discriminating ambiguous scenes,
whereas these features are significantly eliminated in the highly-compressed FC
representation. Secondly, we propose a new Local Convolutional Supervision
(LCS) layer to enhance the local structure of the image by directly propagating
the label information to the convolutional layers. Thirdly, we propose an
efficient Fisher Convolutional Vector (FCV) that successfully rescues the
orderless mid-level semantic information (e.g. objects and textures) of scene
image. The FCV encodes the large-sized convolutional maps into a fixed-length
mid-level representation, and is demonstrated to be strongly complementary to
the high-level FC-features. Finally, both the FCV and FC-features are
collaboratively employed in the LSDHM representation, which achieves
outstanding performance in our experiments. It obtains 83.75% and 67.56%
accuracies respectively on the heavily benchmarked MIT Indoor67 and SUN397
datasets, advancing the stat-of-the-art substantially.
A Simple Approach to Multilingual Polarity Classification in Twitter
Authors: Eric S. Tellez, Sabino Miranda Jiménez, Mario Graff, Daniela Moctezuma, Ranyart R. Suárez, Oscar S. Siordia
Date: 2016-12-15 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis

Recently, sentiment analysis has received a lot of attention due to the
interest in mining opinions of social media users. Sentiment analysis consists
in determining the polarity of a given text, i.e., its degree of positiveness
or negativeness. Traditionally, Sentiment Analysis algorithms have been
tailored to a specific language given the complexity of having a number of
lexical variations and errors introduced by the people generating content. In
this contribution, our aim is to provide a simple to implement and easy to use
multilingual framework, that can serve as a baseline for sentiment analysis
contests, and as starting point to build new sentiment analysis systems. We
compare our approach in eight different languages, three of them have important
international contests, namely, SemEval (English), TASS (Spanish), and
SENTIPOLC (Italian). Within the competitions our approach reaches from medium
to high positions in the rankings; whereas in the remaining languages our
approach outperforms the reported results.
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
Authors: Franck Dernoncourt, Ji Young Lee, Peter Szolovits
Date: 2016-12-15 00:00:00
Proceeding: EACL 2017 4
Tasks: General Classification, Sentence, Sentence Classification, Structured Prediction

Existing models based on artificial neural networks (ANNs) for sentence
classification often do not incorporate the context in which sentences appear,
and classify sentences individually. However, traditional sentence
classification approaches have been shown to greatly benefit from jointly
classifying subsequent sentences, such as with conditional random fields. In
this work, we present an ANN architecture that combines the effectiveness of
typical ANN models to classify sentences in isolation, with the strength of
structured prediction. Our model achieves state-of-the-art results on two
different datasets for sequential sentence classification in medical abstracts.
Data Driven Resource Allocation for Distributed Learning
Authors: Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria Florina Balcan, Alex Smola
Date: 2015-12-15 00:00:00

In distributed machine learning, data is dispatched to multiple machines for
processing. Motivated by the fact that similar data points often belong to the
same or similar classes, and more generally, classification rules of high
accuracy tend to be "locally simple but globally complex" (Vapnik & Bottou
1993), we propose data dependent dispatching that takes advantage of such
structure. We present an in-depth analysis of this model, providing new
algorithms with provable worst-case guarantees, analysis proving existing
scalable heuristics perform well in natural non worst-case conditions, and
techniques for extending a dispatching rule from a small sample to the entire
distribution. We overcome novel technical challenges to satisfy important
conditions for accurate distributed learning, including fault tolerance and
balancedness. We empirically compare our approach with baselines based on
random partitioning, balanced partition trees, and locality sensitive hashing,
showing that we achieve significantly higher accuracy on both synthetic and
real world image and advertising datasets. We also demonstrate that our
technique strongly scales with the available computing power.
Private Learning on Networks
Authors: Shripad Gade, Nitin H. Vaidya
Date: 2016-12-15 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Optimization, Privacy Preserving

Continual data collection and widespread deployment of machine learning
algorithms, particularly the distributed variants, have raised new privacy
challenges. In a distributed machine learning scenario, the dataset is stored
among several machines and they solve a distributed optimization problem to
collectively learn the underlying model. We present a secure multi-party
computation inspired privacy preserving distributed algorithm for optimizing a
convex function consisting of several possibly non-convex functions. Each
individual objective function is privately stored with an agent while the
agents communicate model parameters with neighbor machines connected in a
network. We show that our algorithm can correctly optimize the overall
objective function and learn the underlying model accurately. We further prove
that under a vertex connectivity condition on the topology, our algorithm
preserves privacy of individual objective functions. We establish limits on the
what a coalition of adversaries can learn by observing the messages and states
shared over a network.
Visual Compiler: Synthesizing a Scene-Specific Pedestrian Detector and Pose Estimator
Authors: Namhoon Lee, Xinshuo Weng, Vishnu Naresh Boddeti, Yu Zhang, Fares Beainy, Kris Kitani, Takeo Kanade
Date: 2016-12-15 00:00:00
Tasks: Human Detection, Pose Estimation

We introduce the concept of a Visual Compiler that generates a scene specific
pedestrian detector and pose estimator without any pedestrian observations.
Given a single image and auxiliary scene information in the form of camera
parameters and geometric layout of the scene, the Visual Compiler first infers
geometrically and photometrically accurate images of humans in that scene
through the use of computer graphics rendering. Using these renders we learn a
scene-and-region specific spatially-varying fully convolutional neural network,
for simultaneous detection, pose estimation and segmentation of pedestrians. We
demonstrate that when real human annotated data is scarce or non-existent, our
data generation strategy can provide an excellent solution for bootstrapping
human detection and pose estimation. Experimental results show that our
approach outperforms off-the-shelf state-of-the-art pedestrian detectors and
pose estimators that are trained on real data.
Building a robust sentiment lexicon with (almost) no resource
Authors: Mickael Rouvier, Benoit Favre
Date: 2016-12-15 00:00:00
Tasks: General Classification, Machine Translation, Translation, Word Embeddings

Creating sentiment polarity lexicons is labor intensive. Automatically
translating them from resourceful languages requires in-domain machine
translation systems, which rely on large quantities of bi-texts. In this paper,
we propose to replace machine translation by transferring words from the
lexicon through word embeddings aligned across languages with a simple linear
transform. The approach leads to no degradation, compared to machine
translation, when tested on sentiment polarity classification on tweets from
four languages.
Vision-based Traffic Flow Prediction using Dynamic Texture Model and Gaussian Process
Authors: Bin Liu, Hao Ji, Yi Dai
Date: 2016-07-14 00:00:00
Tasks: Motion Segmentation, regression, Segmentation

In this paper, we describe work in progress towards a real-time vision-based
traffic flow prediction (TFP) system. The proposed method consists of three
elemental operators, that are dynamic texture model based motion segmentation,
feature extraction and Gaussian process (GP) regression. The objective of
motion segmentation is to recognize the target regions covering the moving
vehicles in the sequence of visual processes. The feature extraction operator
aims to extract useful features from the target regions. The extracted features
are then mapped to the number of vehicles through the operator of GP
regression. A training stage using historical visual data is required for
determining the parameter values of the GP. Using a low-resolution visual data
set, we performed preliminary evaluations on the performance of the proposed
method. The results show that our method beats a benchmark solution based on
Gaussian mixture model, and has the potential to be developed into qualified
and practical solutions to real-time TFP.
On the Potential of Simple Framewise Approaches to Piano Transcription
Authors: Rainer Kelz, Matthias Dorfer, Filip Korzeniowski, Sebastian Böck, Andreas Arzt, Gerhard Widmer
Date: 2016-12-15 00:00:00

In an attempt at exploring the limitations of simple approaches to the task
of piano transcription (as usually defined in MIR), we conduct an in-depth
analysis of neural network-based framewise transcription. We systematically
compare different popular input representations for transcription systems to
determine the ones most suitable for use with neural networks. Exploiting
recent advances in training techniques and new regularizers, and taking into
account hyper-parameter tuning, we show that it is possible, by simple
bottom-up frame-wise processing, to obtain a piano transcriber that outperforms
the current published state of the art on the publicly available MAPS dataset
-- without any complex post-processing steps. Thus, we propose this simple
approach as a new baseline for this dataset, for future transcription research
to build on and improve.
Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks
Authors: Jefferson Ryan Medel, Andreas Savakis
Date: 2016-12-01 00:00:00
Tasks: Anomaly Detection

Automating the detection of anomalous events within long video sequences is
challenging due to the ambiguity of how such events are defined. We approach
the problem by learning generative models that can identify anomalies in videos
using limited supervision. We propose end-to-end trainable composite
Convolutional Long Short-Term Memory (Conv-LSTM) networks that are able to
predict the evolution of a video sequence from a small number of input frames.
Regularity scores are derived from the reconstruction errors of a set of
predictions with abnormal video sequences yielding lower regularity scores as
they diverge further from the actual sequence over time. The models utilize a
composite structure and examine the effects of conditioning in learning more
meaningful representations. The best model is chosen based on the
reconstruction and prediction accuracy. The Conv-LSTM models are evaluated both
qualitatively and quantitatively, demonstrating competitive results on anomaly
detection datasets. Conv-LSTM units are shown to be an effective tool for
modeling and predicting video sequences.
Transition-based Parsing with Context Enhancement and Future Reward Reranking
Authors: Fugen Zhou, Fuxiang Wu, Zhengchen Zhang, Minghui Dong
Date: 2016-12-15 00:00:00
Tasks: ARC, Reranking

This paper presents a novel reranking model, future reward reranking, to
re-score the actions in a transition-based parser by using a global scorer.
Different to conventional reranking parsing, the model searches for the best
dependency tree in all feasible trees constraining by a sequence of actions to
get the future reward of the sequence. The scorer is based on a first-order
graph-based parser with bidirectional LSTM, which catches different parsing
view compared with the transition-based parser. Besides, since context
enhancement has shown substantial improvement in the arc-stand transition-based
parsing over the parsing accuracy, we implement context enhancement on an
arc-eager transition-base parser with stack LSTMs, the dynamic oracle and
dropout supporting and achieve further improvement. With the global scorer and
context enhancement, the results show that UAS of the parser increases as much
as 1.20% for English and 1.66% for Chinese, and LAS increases as much as 1.32%
for English and 1.63% for Chinese. Moreover, we get state-of-the-art LASs,
achieving 87.58% for Chinese and 93.37% for English.
A Fully Convolutional Deep Auditory Model for Musical Chord Recognition
Authors: Filip Korzeniowski, Gerhard Widmer
Date: 2016-12-15 00:00:00
Tasks: Chord Recognition

Chord recognition systems depend on robust feature extraction pipelines.
While these pipelines are traditionally hand-crafted, recent advances in
end-to-end machine learning have begun to inspire researchers to explore
data-driven methods for such tasks. In this paper, we present a chord
recognition system that uses a fully convolutional deep auditory model for
feature extraction. The extracted features are processed by a Conditional
Random Field that decodes the final chord sequence. Both processing stages are
trained automatically and do not require expert knowledge for optimising
parameters. We show that the learned auditory system extracts musically
interpretable features, and that the proposed chord recognition system achieves
results on par or better than state-of-the-art algorithms.
Towards End-to-End Audio-Sheet-Music Retrieval
Authors: Matthias Dorfer, Andreas Arzt, Gerhard Widmer
Date: 2016-12-15 00:00:00
Tasks: Retrieval

This paper demonstrates the feasibility of learning to retrieve short
snippets of sheet music (images) when given a short query excerpt of music
(audio) -- and vice versa --, without any symbolic representation of music or
scores. This would be highly useful in many content-based musical retrieval
scenarios. Our approach is based on Deep Canonical Correlation Analysis (DCCA)
and learns correlated latent spaces allowing for cross-modality retrieval in
both directions. Initial experiments with relatively simple monophonic music
show promising results.
Feature Learning for Chord Recognition: The Deep Chroma Extractor
Authors: Filip Korzeniowski, Gerhard Widmer
Date: 2016-12-15 00:00:00
Tasks: Chord Recognition

We explore frame-level audio feature learning for chord recognition using
artificial neural networks. We present the argument that chroma vectors
potentially hold enough information to model harmonic content of audio for
chord recognition, but that standard chroma extractors compute too noisy
features. This leads us to propose a learned chroma feature extractor based on
artificial neural networks. It is trained to compute chroma features that
encode harmonic information important for chord recognition, while being robust
to irrelevant interferences. We achieve this by feeding the network an audio
spectrum with context instead of a single frame as input. This way, the network
can learn to selectively compensate noise and resolve harmonic ambiguities.
  We compare the resulting features to hand-crafted ones by using a simple
linear frame-wise classifier for chord recognition on various data sets. The
results show that the learned feature extractor produces superior chroma
vectors for chord recognition.
Beyond Holistic Object Recognition: Enriching Image Understanding with Part States
Authors: Cewu Lu, Hao Su, Yongyi Lu, Li Yi, Chi-Keung Tang, Leonidas Guibas
Date: 2016-12-15 00:00:00
Proceeding: CVPR 2018 6
Tasks: Human-Object Interaction Detection, Image Captioning, Object Recognition

Important high-level vision tasks such as human-object interaction, image
captioning and robotic manipulation require rich semantic descriptions of
objects at part level. Based upon previous work on part localization, in this
paper, we address the problem of inferring rich semantics imparted by an object
part in still images. We propose to tokenize the semantic space as a discrete
set of part states. Our modeling of part state is spatially localized,
therefore, we formulate the part state inference problem as a pixel-wise
annotation problem. An iterative part-state inference neural network is
specifically designed for this task, which is efficient in time and accurate in
performance. Extensive experiments demonstrate that the proposed method can
effectively predict the semantic states of parts and simultaneously correct
localization errors, thus benefiting a few visual understanding applications.
The other contribution of this paper is our part state dataset which contains
rich part-level semantic annotations.
Online and Differentially-Private Tensor Decomposition
Authors: Yining Wang, Animashree Anandkumar
Date: 2016-06-20 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Tensor Decomposition

In this paper, we resolve many of the key algorithmic questions regarding
robustness, memory efficiency, and differential privacy of tensor
decomposition. We propose simple variants of the tensor power method which
enjoy these strong properties. We present the first guarantees for online
tensor power method which has a linear memory requirement. Moreover, we present
a noise calibrated tensor power method with efficient privacy guarantees. At
the heart of all these guarantees lies a careful perturbation analysis derived
in this paper which improves up on the existing results significantly.
Graphical RNN Models
Authors: Ashish Bora, Sugato Basu, Joydeep Ghosh
Date: 2016-12-15 00:00:00
Tasks: Time Series, Time Series Analysis

Many time series are generated by a set of entities that interact with one
another over time. This paper introduces a broad, flexible framework to learn
from multiple inter-dependent time series generated by such entities. Our
framework explicitly models the entities and their interactions through time.
It achieves this by building on the capabilities of Recurrent Neural Networks,
while also offering several ways to incorporate domain knowledge/constraints
into the model architecture. The capabilities of our approach are showcased
through an application to weather prediction, which shows gains over strong
baselines.
Expectation Propagation performs a smoothed gradient descent
Authors: Guillaume P. Dehaene
Date: 2016-12-15 00:00:00
Tasks: Bayesian Inference

Bayesian inference is a popular method to build learning algorithms but it is
hampered by the fact that its key object, the posterior probability
distribution, is often uncomputable. Expectation Propagation (EP) (Minka
(2001)) is a popular algorithm that solves this issue by computing a parametric
approximation (e.g: Gaussian) to the density of the posterior. However, while
it is known empirically to quickly compute fine approximations, EP is extremely
poorly understood which prevents it from being adopted by a larger fraction of
the community.
  The object of the present article is to shed intuitive light on EP, by
relating it to other better understood methods. More precisely, we link it to
using gradient descent to compute the Laplace approximation of a target
probability distribution. We show that EP is exactly equivalent to performing
gradient descent on a smoothed energy landscape: i.e: the original energy
landscape convoluted with some smoothing kernel. This also relates EP to
algorithms that compute the Gaussian approximation which minimizes the reverse
KL divergence to the target distribution, a link that has been conjectured
before but has not been proved rigorously yet. These results can help
practitioners to get a better feel for how EP works, as well as lead to other
new results on this important method.
Towards Score Following in Sheet Music Images
Authors: Matthias Dorfer, Andreas Arzt, Gerhard Widmer
Date: 2016-12-15 00:00:00
Tasks: Position

This paper addresses the matching of short music audio snippets to the
corresponding pixel location in images of sheet music. A system is presented
that simultaneously learns to read notes, listens to music and matches the
currently played music to its corresponding notes in the sheet. It consists of
an end-to-end multi-modal convolutional neural network that takes as input
images of sheet music and spectrograms of the respective audio snippets. It
learns to predict, for a given unseen audio snippet (covering approximately one
bar of music), the corresponding position in the respective score line. Our
results suggest that with the use of (deep) neural networks -- which have
proven to be powerful image processing models -- working with sheet music
becomes feasible and a promising future research direction.
Network-Guided Biomarker Discovery
Authors: Chloé-Agathe Azencott
Date: 2016-07-27 00:00:00
Tasks: Diagnostic, feature selection

Identifying measurable genetic indicators (or biomarkers) of a specific
condition of a biological system is a key element of precision medicine. Indeed
it allows to tailor diagnostic, prognostic and treatment choice to individual
characteristics of a patient. In machine learning terms, biomarker discovery
can be framed as a feature selection problem on whole-genome data sets.
However, classical feature selection methods are usually underpowered to
process these data sets, which contain orders of magnitude more features than
samples. This can be addressed by making the assumption that genetic features
that are linked on a biological network are more likely to work jointly towards
explaining the phenotype of interest. We review here three families of methods
for feature selection that integrate prior knowledge in the form of networks.