pwc-archive/papers-with-abstracts huggingface dataset:
Character-Level Models versus Morphology in Semantic Role Labeling
Authors: Gözde Gül Şahin, Mark Steedman
Date: 2018-05-30 00:00:00
Proceeding: ACL 2018 7
Tasks: Semantic Role Labeling

Character-level models have become a popular approach specially for their
accessibility and ability to handle unseen data. However, little is known on
their ability to reveal the underlying morphological structure of a word, which
is a crucial skill for high-level semantic analysis tasks, such as semantic
role labeling (SRL). In this work, we train various types of SRL models that
use word, character and morphology level information and analyze how
performance of characters compare to words and morphology for several
languages. We conduct an in-depth error analysis for each morphological
typology and analyze the strengths and limitations of character-level models
that relate to out-of-domain data, training data size, long range dependencies
and model complexity. Our exhaustive analyses shed light on important
characteristics of character-level models and their semantic capability.
Transformer for Emotion Recognition
Authors: Jean-Benoit Delbrouck
Date: 2018-05-03 00:00:00
Tasks: Emotion Recognition

This paper describes the UMONS solution for the OMG-Emotion Challenge. We
explore a context-dependent architecture where the arousal and valence of an
utterance are predicted according to its surrounding context (i.e. the
preceding and following utterances of the video). We report an improvement when
taking into account context for both unimodal and multimodal predictions.
Learning to Generate Facial Depth Maps
Authors: Stefano Pini, Filippo Grazioli, Guido Borghi, Roberto Vezzani, Rita Cucchiara
Date: 2018-05-30 00:00:00
Tasks: Face Verification, Generative Adversarial Network

In this paper, an adversarial architecture for facial depth map estimation
from monocular intensity images is presented. By following an image-to-image
approach, we combine the advantages of supervised learning and adversarial
training, proposing a conditional Generative Adversarial Network that
effectively learns to translate intensity face images into the corresponding
depth maps. Two public datasets, namely Biwi database and Pandora dataset, are
exploited to demonstrate that the proposed model generates high-quality
synthetic depth images, both in terms of visual appearance and informative
content. Furthermore, we show that the model is capable of predicting
distinctive facial details by testing the generated depth maps through a deep
model trained on authentic depth maps for the face verification task.
A survey of dimensionality reduction techniques based on random projection
Authors: Haozhe Xie, Jie Li, Hanqing Xue
Date: 2017-06-14 00:00:00
Tasks: Dimensionality Reduction, Survey

Dimensionality reduction techniques play important roles in the analysis of
big data. Traditional dimensionality reduction approaches, such as principal
component analysis (PCA) and linear discriminant analysis (LDA), have been
studied extensively in the past few decades. However, as the dimensionality of
data increases, the computational cost of traditional dimensionality reduction
methods grows exponentially, and the computation becomes prohibitively
intractable. These drawbacks have triggered the development of random
projection (RP) techniques, which map high-dimensional data onto a
low-dimensional subspace with extremely reduced time cost. However, the RP
transformation matrix is generated without considering the intrinsic structure
of the original data and usually leads to relatively high distortion.
Therefore, in recent years, methods based on RP have been proposed to address
this problem. In this paper, we summarize the methods used in different
situations to help practitioners to employ the proper techniques for their
specific applications. Meanwhile, we enumerate the benefits and limitations of
the various methods and provide further references for researchers to develop
novel RP-based approaches.
Additive Margin Softmax for Face Verification
Authors: Feng Wang, Weiyang Liu, Haijun Liu, Jian Cheng
Date: 2018-01-17 00:00:00
Tasks: Face Verification, Metric Learning

In this paper, we propose a conceptually simple and geometrically
interpretable objective function, i.e. additive margin Softmax (AM-Softmax),
for deep face verification. In general, the face verification task can be
viewed as a metric learning problem, so learning large-margin face features
whose intra-class variation is small and inter-class difference is large is of
great importance in order to achieve good performance. Recently, Large-margin
Softmax and Angular Softmax have been proposed to incorporate the angular
margin in a multiplicative manner. In this work, we introduce a novel additive
angular margin for the Softmax loss, which is intuitively appealing and more
interpretable than the existing works. We also emphasize and discuss the
importance of feature normalization in the paper. Most importantly, our
experiments on LFW BLUFR and MegaFace show that our additive margin softmax
loss consistently performs better than the current state-of-the-art methods
using the same network architecture and training dataset. Our code has also
been made available at https://github.com/happynear/AMSoftmax
Multiple Manifolds Metric Learning with Application to Image Set Classification
Authors: Rui Wang, Xiao-Jun Wu, Kai-Xuan Chen, Josef Kittler
Date: 2018-05-30 00:00:00
Tasks: Classification, Face Recognition, General Classification, Metric Learning, Object Categorization

In image set classification, a considerable advance has been made by modeling
the original image sets by second order statistics or linear subspace, which
typically lie on the Riemannian manifold. Specifically, they are Symmetric
Positive Definite (SPD) manifold and Grassmann manifold respectively, and some
algorithms have been developed on them for classification tasks. Motivated by
the inability of existing methods to extract discriminatory features for data
on Riemannian manifolds, we propose a novel algorithm which combines multiple
manifolds as the features of the original image sets. In order to fuse these
manifolds, the well-studied Riemannian kernels have been utilized to map the
original Riemannian spaces into high dimensional Hilbert spaces. A metric
Learning method has been devised to embed these kernel spaces into a lower
dimensional common subspace for classification. The state-of-the-art results
achieved on three datasets corresponding to two different classification tasks,
namely face recognition and object categorization, demonstrate the
effectiveness of the proposed method.
The Dynamics of Learning: A Random Matrix Approach
Authors: Zhenyu Liao, Romain Couillet
Date: 2018-05-30 00:00:00
Proceeding: ICML 2018 7
Tasks: Binary Classification, General Classification

Understanding the learning dynamics of neural networks is one of the key
issues for the improvement of optimization algorithms as well as for the
theoretical comprehension of why deep neural nets work so well today. In this
paper, we introduce a random matrix-based framework to analyze the learning
dynamics of a single-layer linear network on a binary classification problem,
for data of simultaneously large dimension and size, trained by gradient
descent. Our results provide rich insights into common questions in neural
nets, such as overfitting, early stopping and the initialization of training,
thereby opening the door for future studies of more elaborate structures and
models appearing in today's neural networks.
Unsupervised detection of diachronic word sense evolution
Authors: Jean-François Delpech
Date: 2018-05-29 00:00:00
Tasks: Time Series, Time Series Analysis, Word Embeddings

Most words have several senses and connotations which evolve in time due to
semantic shift, so that closely related words may gain different or even
opposite meanings over the years. This evolution is very relevant to the study
of language and of cultural changes, but the tools currently available for
diachronic semantic analysis have significant, inherent limitations and are not
suitable for real-time analysis. In this article, we demonstrate how the
linearity of random vectors techniques enables building time series of
congruent word embeddings (or semantic spaces) which can then be compared and
combined linearly without loss of precision over any time period to detect
diachronic semantic shifts. We show how this approach yields time trajectories
of polysemous words such as amazon or apple, enables following semantic drifts
and gender bias across time, reveals the shifting instantiations of stable
concepts such as hurricane or president. This very fast, linear approach can
easily be distributed over many processors to follow in real time streams of
social media such as Twitter or Facebook; the resulting, time-dependent
semantic spaces can then be combined at will by simple additions or
subtractions.
On the Spectrum of Random Features Maps of High Dimensional Data
Authors: Zhenyu Liao, Romain Couillet
Date: 2018-05-30 00:00:00
Proceeding: ICML 2018 7
Tasks: BIG-bench Machine Learning, Vocal Bursts Intensity Prediction

Random feature maps are ubiquitous in modern statistical machine learning,
where they generalize random projections by means of powerful, yet often
difficult to analyze nonlinear operators. In this paper, we leverage the
"concentration" phenomenon induced by random matrix theory to perform a
spectral analysis on the Gram matrix of these random feature maps, here for
Gaussian mixture models of simultaneously large dimension and size. Our results
are instrumental to a deeper understanding on the interplay of the nonlinearity
and the statistics of the data, thereby allowing for a better tuning of random
feature-based techniques.
BRUNO: A Deep Recurrent Model for Exchangeable Data
Authors: Iryna Korshunova, Jonas Degrave, Ferenc Huszár, Yarin Gal, Arthur Gretton, Joni Dambre
Date: 2018-02-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Anomaly Detection, Bayesian Inference, Conditional Image Generation, Few-Shot Learning, Image Generation, model

We present a novel model architecture which leverages deep learning tools to
perform exact Bayesian inference on sets of high dimensional, complex
observations. Our model is provably exchangeable, meaning that the joint
distribution over observations is invariant under permutation: this property
lies at the heart of Bayesian inference. The model does not require variational
approximations to train, and new samples can be generated conditional on
previous samples, with cost linear in the size of the conditioning set. The
advantages of our architecture are demonstrated on learning tasks that require
generalisation from short observed sequences while modelling sequence
variability, such as conditional image generation, few-shot learning, and
anomaly detection.
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
Authors: Ylva Jansson, Tony Lindeberg
Date: 2017-10-13 00:00:00
Tasks: Descriptive, Dynamic Texture Recognition, Object Recognition

This work presents a first evaluation of using spatio-temporal receptive
fields from a recently proposed time-causal spatio-temporal scale-space
framework as primitives for video analysis. We propose a new family of video
descriptors based on regional statistics of spatio-temporal receptive field
responses and evaluate this approach on the problem of dynamic texture
recognition. Our approach generalises a previously used method, based on joint
histograms of receptive field responses, from the spatial to the
spatio-temporal domain and from object recognition to dynamic texture
recognition. The time-recursive formulation enables computationally efficient
time-causal recognition. The experimental evaluation demonstrates competitive
performance compared to state-of-the-art. Especially, it is shown that binary
versions of our dynamic texture descriptors achieve improved performance
compared to a large range of similar methods using different primitives either
handcrafted or learned from data. Further, our qualitative and quantitative
investigation into parameter choices and the use of different sets of receptive
fields highlights the robustness and flexibility of our approach. Together,
these results support the descriptive power of this family of time-causal
spatio-temporal receptive fields, validate our approach for dynamic texture
recognition and point towards the possibility of designing a range of video
analysis methods based on these new time-causal spatio-temporal primitives.
Needle Tip Force Estimation using an OCT Fiber and a Fused convGRU-CNN Architecture
Authors: Nils Gessert, Torben Priegnitz, Thore Saathoff, Sven-Thomas Antoni, David Meyer, Moritz Franz Hamann, Klaus-Peter Jünemann, Christoph Otte, Alexander Schlaefer
Date: 2018-05-30 00:00:00

Needle insertion is common during minimally invasive interventions such as
biopsy or brachytherapy. During soft tissue needle insertion, forces acting at
the needle tip cause tissue deformation and needle deflection. Accurate needle
tip force measurement provides information on needle-tissue interaction and
helps detecting and compensating potential misplacement. For this purpose we
introduce an image-based needle tip force estimation method using an optical
fiber imaging the deformation of an epoxy layer below the needle tip over time.
For calibration and force estimation, we introduce a novel deep learning-based
fused convolutional GRU-CNN model which effectively exploits the
spatio-temporal data structure. The needle is easy to manufacture and our model
achieves a mean absolute error of 1.76 +- 1.5 mN with a cross-correlation
coefficient of 0.9996, clearly outperforming other methods. We test needles
with different materials to demonstrate that the approach can be adapted for
different sensitivities and force ranges. Furthermore, we validate our approach
in an ex-vivo prostate needle insertion scenario.
Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms
Authors: Marco Scutari, Catharina Elisabeth Graafland, José Manuel Gutiérrez
Date: 2018-05-30 00:00:00

Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence tests to learn the dependence structure of the data; score-based algorithms, which use goodness-of-fit scores as objective functions to maximise; and hybrid algorithms that combine both approaches. Constraint-based and score-based algorithms have been shown to learn the same structures when conditional independence and goodness of fit are both assessed using entropy and the topological ordering of the network is known (Cowell, 2001). In this paper, we investigate how these three classes of algorithms perform outside the assumptions above in terms of speed and accuracy of network reconstruction for both discrete and Gaussian Bayesian networks. We approach this question by recognising that structure learning is defined by the combination of a statistical criterion and an algorithm that determines how the criterion is applied to the data. Removing the confounding effect of different choices for the statistical criterion, we find using both simulated and real-world complex data that constraint-based algorithms are often less accurate than score-based algorithms, but are seldom faster (even at large sample sizes); and that hybrid algorithms are neither faster nor more accurate than constraint-based algorithms. This suggests that commonly held beliefs on structure learning in the literature are strongly influenced by the choice of particular statistical criteria rather than just by the properties of the algorithms themselves.
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
Authors: Giulia Luise, Alessandro Rudi, Massimiliano Pontil, Carlo Ciliberto
Date: 2018-05-30 00:00:00
Proceeding: NeurIPS 2018 12

Applications of optimal transport have recently gained remarkable attention
thanks to the computational advantages of entropic regularization. However, in
most situations the Sinkhorn approximation of the Wasserstein distance is
replaced by a regularized version that is less accurate but easy to
differentiate. In this work we characterize the differential properties of the
original Sinkhorn distance, proving that it enjoys the same smoothness as its
regularized version and we explicitly provide an efficient algorithm to compute
its gradient. We show that this result benefits both theory and applications:
on one hand, high order smoothness confers statistical guarantees to learning
with Wasserstein approximations. On the other hand, the gradient formula allows
us to efficiently solve learning and optimization problems in practice.
Promising preliminary experiments complement our analysis.
Note on Representing attribute reduction and concepts in concepts lattice using graphs
Authors: Jan Konecny
Date: 2017-11-15 00:00:00
Tasks: Attribute

Mao H. (2017, Representing attribute reduction and concepts in concept
lattice using graphs. Soft Computing 21(24):7293--7311) claims to make
contributions to the study of reduction of attributes in concept lattices by
using graph theory. We show that her results are either trivial or already
well-known and all three algorithms proposed in the paper are incorrect.
Tile2Vec: Unsupervised representation learning for spatially distributed data
Authors: Neal Jean, Sherrie Wang, Anshul Samar, George Azzari, David Lobell, Stefano Ermon
Date: 2018-05-08 00:00:00
Tasks: General Classification, Representation Learning, Visual Analogies

Geospatial analysis lacks methods like the word vector representations and
pre-trained networks that significantly boost performance across a wide range
of natural language and computer vision tasks. To fill this gap, we introduce
Tile2Vec, an unsupervised representation learning algorithm that extends the
distributional hypothesis from natural language -- words appearing in similar
contexts tend to have similar meanings -- to spatially distributed data. We
demonstrate empirically that Tile2Vec learns semantically meaningful
representations on three datasets. Our learned representations significantly
improve performance in downstream classification tasks and, similar to word
vectors, visual analogies can be obtained via simple arithmetic in the latent
space.
Predictive Performance Modeling for Distributed Computing using Black-Box Monitoring and Machine Learning
Authors: Carl Witt, Marc Bux, Wladislaw Gusew, Ulf Leser
Date: 2018-05-30 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Computing, Scheduling

In many domains, the previous decade was characterized by increasing data
volumes and growing complexity of computational workloads, creating new demands
for highly data-parallel computing in distributed systems. Effective operation
of these systems is challenging when facing uncertainties about the performance
of jobs and tasks under varying resource configurations, e.g., for scheduling
and resource allocation. We survey predictive performance modeling (PPM)
approaches to estimate performance metrics such as execution duration, required
memory or wait times of future jobs and tasks based on past performance
observations. We focus on non-intrusive methods, i.e., methods that can be
applied to any workload without modification, since the workload is usually a
black-box from the perspective of the systems managing the computational
infrastructure. We classify and compare sources of performance variation,
predicted performance metrics, required training data, use cases, and the
underlying prediction techniques. We conclude by identifying several open
problems and pressing research needs in the field.
EmotionLines: An Emotion Corpus of Multi-Party Conversations
Authors: Sheng-Yeh Chen, Chao-Chun Hsu, Chuan-Chun Kuo, Ting-Hao, Huang, Lun-Wei Ku
Date: 2018-02-23 00:00:00
Proceeding: LREC 2018 5

Feeling emotion is a critical characteristic to distinguish people from
machines. Among all the multi-modal resources for emotion detection, textual
datasets are those containing the least additional information in addition to
semantics, and hence are adopted widely for testing the developed systems.
However, most of the textual emotional datasets consist of emotion labels of
only individual words, sentences or documents, which makes it challenging to
discuss the contextual flow of emotions. In this paper, we introduce
EmotionLines, the first dataset with emotions labeling on all utterances in
each dialogue only based on their textual content. Dialogues in EmotionLines
are collected from Friends TV scripts and private Facebook messenger dialogues.
Then one of seven emotions, six Ekman's basic emotions plus the neutral
emotion, is labeled on each utterance by 5 Amazon MTurkers. A total of 29,245
utterances from 2,000 dialogues are labeled in EmotionLines. We also provide
several strong baselines for emotion detection models on EmotionLines in this
paper.
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data
Authors: Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
Date: 2017-06-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Time Series, Time Series Analysis

What learning algorithms can be run directly on compressively-sensed data? In this work, we consider the question of accurately and efficiently computing low-rank matrix or tensor factorizations given data compressed via random projections. We examine the approach of first performing factorization in the compressed domain, and then reconstructing the original high-dimensional factors from the recovered (compressed) factors. In both the matrix and tensor settings, we establish conditions under which this natural approach will provably recover the original factors. While it is well-known that random projections preserve a number of geometric properties of a dataset, our work can be viewed as showing that they can also preserve certain solutions of non-convex, NP-Hard problems like non-negative matrix factorization. We support these theoretical results with experiments on synthetic data and demonstrate the practical applicability of compressed factorization on real-world gene expression and EEG time series datasets.
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection
Authors: Sahil Swami, Ankush Khandelwal, Vinay Singh, Syed Sarfaraz Akhtar, Manish Shrivastava
Date: 2018-05-30 00:00:00
Tasks: Opinion Mining, Sarcasm Detection, Sentiment Analysis, TAG

Social media platforms like twitter and facebook have be- come two of the
largest mediums used by people to express their views to- wards different
topics. Generation of such large user data has made NLP tasks like sentiment
analysis and opinion mining much more important. Using sarcasm in texts on
social media has become a popular trend lately. Using sarcasm reverses the
meaning and polarity of what is implied by the text which poses challenge for
many NLP tasks. The task of sarcasm detection in text is gaining more and more
importance for both commer- cial and security services. We present the first
English-Hindi code-mixed dataset of tweets marked for presence of sarcasm and
irony where each token is also annotated with a language tag. We present a
baseline su- pervised classification system developed using the same dataset
which achieves an average F-score of 78.4 after using random forest classifier
and performing 10-fold cross validation.