pwc-archive/papers-with-abstracts huggingface dataset:
Diverse Online Feature Selection
Authors: Chapman Siu, Richard Yi Da Xu
Date: 2018-06-12 00:00:00
Tasks: Diversity, feature selection, Point Processes

Online feature selection has been an active research area in recent years. We
propose a novel diverse online feature selection method based on Determinantal
Point Processes (DPP). Our model aims to provide diverse features which can be
composed in either a supervised or unsupervised framework. The framework aims
to promote diversity based on the kernel produced on a feature level, through
at most three stages: feature sampling, local criteria and global criteria for
feature selection. In the feature sampling, we sample incoming stream of
features using conditional DPP. The local criteria is used to assess and select
streamed features (i.e. only when they arrive), we use unsupervised scale
invariant methods to remove redundant features and optionally supervised
methods to introduce label information to assess relevant features. Lastly, the
global criteria uses regularization methods to select a global optimal subset
of features. This three stage procedure continues until there are no more
features arriving or some predefined stopping condition is met. We demonstrate
based on experiments conducted on that this approach yields better compactness,
is comparable and in some instances outperforms other state-of-the-art online
feature selection methods.
Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks
Authors: Kede Ma, Huan Fu, Tongliang Liu, Zhou Wang, DaCheng Tao
Date: 2016-12-05 00:00:00
Tasks: Vocal Bursts Intensity Prediction

The human visual system excels at detecting local blur of visual images, but
the underlying mechanism is not well understood. Traditional views of blur such
as reduction in energy at high frequencies and loss of phase coherence at
localized features have fundamental limitations. For example, they cannot well
discriminate flat regions from blurred ones. Here we propose that high-level
semantic information is critical in successfully identifying local blur.
Therefore, we resort to deep neural networks that are proficient at learning
high-level features and propose the first end-to-end local blur mapping
algorithm based on a fully convolutional network. By analyzing various
architectures with different depths and design philosophies, we empirically
show that high-level features of deeper layers play a more important role than
low-level features of shallower layers in resolving challenging ambiguities for
this task. We test the proposed method on a standard blur detection benchmark
and demonstrate that it significantly advances the state-of-the-art (ODS
F-score of 0.853). Furthermore, we explore the use of the generated blur maps
in three applications, including blur region segmentation, blur degree
estimation, and blur magnification.
Model-Free Information Extraction in Enriched Nonlinear Phase-Space
Authors: Bin Li, Yueheng Lan, Weisi Guo, Chenglin Zhao
Date: 2018-04-14 00:00:00

Detecting anomalies and discovering driving signals is an essential component
of scientific research and industrial practice. Often the underlying mechanism
is highly complex, involving hidden evolving nonlinear dynamics and noise
contamination. When representative physical models and large labeled data sets
are unavailable, as is the case with most real-world applications,
model-dependent Bayesian approaches would yield misleading results, and most
supervised learning machines would also fail to reliably resolve the
intricately evolving systems. Here, we propose an unsupervised machine-learning
approach that operates in a well-constructed function space, whereby the
evolving nonlinear dynamics are captured through a linear functional
representation determined by the Koopman operator. This breakthrough leverages
on the time-feature embedding and the ensuing reconstruction of a phase-space
representation of the dynamics, thereby permitting the reliable identification
of critical global signatures from the whole trajectory. This dramatically
improves over commonly used static local features, which are vulnerable to
unknown transitions or noise. Thanks to its data-driven nature, our method
excludes any prior models and training corpus. We benchmark the astonishing
accuracy of our method on three diverse and challenging problems in: biology,
medicine, and engineering. In all cases, it outperforms existing
state-of-the-art methods. As a new unsupervised information processing
paradigm, it is suitable for ubiquitous nonlinear dynamical systems or
end-users with little expertise, which permits an unbiased excavation of
underlying working principles or intrinsic correlations submerged in unlabeled
data flows.
Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition
Authors: Yaming Wang, Vlad I. Morariu, Larry S. Davis
Date: 2016-11-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Representation Learning

Compared to earlier multistage frameworks using CNN features, recent
end-to-end deep approaches for fine-grained recognition essentially enhance the
mid-level learning capability of CNNs. Previous approaches achieve this by
introducing an auxiliary network to infuse localization information into the
main classification network, or a sophisticated feature encoding method to
capture higher order feature statistics. We show that mid-level representation
learning can be enhanced within the CNN framework, by learning a bank of
convolutional filters that capture class-specific discriminative patches
without extra part or bounding box annotations. Such a filter bank is well
structured, properly initialized and discriminatively learned through a novel
asymmetric multi-stream architecture with convolutional filter supervision and
a non-random layer initialization. Experimental results show that our approach
achieves state-of-the-art on three publicly available fine-grained recognition
datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft). Ablation studies and
visualizations are provided to understand our approach.
Findings of the Second Workshop on Neural Machine Translation and Generation
Authors: Alexandra Birch, Andrew Finch, Minh-Thang Luong, Graham Neubig, Yusuke Oda
Date: 2018-06-08 00:00:00
Proceeding: WS 2018 7
Tasks: Data Augmentation, Domain Adaptation, Machine Translation, Translation

This document describes the findings of the Second Workshop on Neural Machine
Translation and Generation, held in concert with the annual conference of the
Association for Computational Linguistics (ACL 2018). First, we summarize the
research trends of papers presented in the proceedings, and note that there is
particular interest in linguistic structure, domain adaptation, data
augmentation, handling inadequate resources, and analysis of models. Second, we
describe the results of the workshop's shared task on efficient neural machine
translation, where participants were tasked with creating MT systems that are
both accurate and efficient.
Object detection and tracking benchmark in industry based on improved correlation filter
Authors: Shangzhen Luan, Yan Li, Xiaodi Wang, Baochang Zhang
Date: 2018-06-11 00:00:00
Tasks: Diversity, object-detection, Object Detection, Real-Time Object Detection

Real-time object detection and tracking have shown to be the basis of
intelligent production for industrial 4.0 applications. It is a challenging
task because of various distorted data in complex industrial setting. The
correlation filter (CF) has been used to trade off the low-cost computation and
high performance. However, traditional CF training strategy can not get
satisfied performance for the various industrial data; because the simple
sampling(bagging) during training process will not find the exact solutions in
a data space with a large diversity. In this paper, we propose
Dijkstra-distance based correlation filters (DBCF), which establishes a new
learning framework that embeds distribution-related constraints into the
multi-channel correlation filters (MCCF). DBCF is able to handle the huge
variations existing in the industrial data by improving those constraints based
on the shortest path among all solutions. To evaluate DBCF, we build a new
dataset as the benchmark for industrial 4.0 application. Extensive experiments
demonstrate that DBCF produces high performance and exceeds the
state-of-the-art methods. The dataset and source code can be found at
https://github.com/bczhangbczhang
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing---and Back
Authors: Elliot Meyerson, Risto Miikkulainen
Date: 2018-03-11 00:00:00
Proceeding: ICML 2018

Deep multitask learning boosts performance by sharing learned structure
across related tasks. This paper adapts ideas from deep multitask learning to
the setting where only a single task is available. The method is formalized as
pseudo-task augmentation, in which models are trained with multiple decoders
for each task. Pseudo-tasks simulate the effect of training towards
closely-related tasks drawn from the same universe. In a suite of experiments,
pseudo-task augmentation is shown to improve performance on single-task
learning problems. When combined with multitask learning, further improvements
are achieved, including state-of-the-art performance on the CelebA dataset,
showing that pseudo-task augmentation and multitask learning have complementary
value. All in all, pseudo-task augmentation is a broadly applicable and
efficient way to boost performance in deep learning systems.
Challenges of language technologies for the indigenous languages of the Americas
Authors: Manuel Mager, Ximena Gutierrez-Vasques, Gerardo Sierra, Ivan Meza
Date: 2018-06-12 00:00:00
Proceeding: COLING 2018 8

Indigenous languages of the American continent are highly diverse. However,
they have received little attention from the technological perspective. In this
paper, we review the research, the digital resources and the available NLP
systems that focus on these languages. We present the main challenges and
research questions that arise when distant languages and low-resource scenarios
are faced. We would like to encourage NLP research in linguistically rich and
diverse areas like the Americas.
DPatch: An Adversarial Patch Attack on Object Detectors
Authors: Xin Liu, Huanrui Yang, Ziwei Liu, Linghao Song, Hai Li, Yiran Chen
Date: 2018-06-05 00:00:00
Tasks: Object

Object detectors have emerged as an indispensable module in modern computer
vision systems. In this work, we propose DPatch -- a black-box
adversarial-patch-based attack towards mainstream object detectors (i.e. Faster
R-CNN and YOLO). Unlike the original adversarial patch that only manipulates
image-level classifier, our DPatch simultaneously attacks the bounding box
regression and object classification so as to disable their predictions.
Compared to prior works, DPatch has several appealing properties: (1) DPatch
can perform both untargeted and targeted effective attacks, degrading the mAP
of Faster R-CNN and YOLO from 75.10% and 65.7% down to below 1%, respectively.
(2) DPatch is small in size and its attacking effect is location-independent,
making it very practical to implement real-world attacks. (3) DPatch
demonstrates great transferability among different detectors as well as
training datasets. For example, DPatch that is trained on Faster R-CNN can
effectively attack YOLO, and vice versa. Extensive evaluations imply that
DPatch can perform effective attacks under black-box setup, i.e., even without
the knowledge of the attacked network's architectures and parameters.
Successful realization of DPatch also illustrates the intrinsic vulnerability
of the modern detector architectures to such patch-based adversarial attacks.
Twin Regularization for online speech recognition
Authors: Mirco Ravanelli, Dmitriy Serdyuk, Yoshua Bengio
Date: 2018-04-15 00:00:00
Tasks: speech-recognition, Speech Recognition

Online speech recognition is crucial for developing natural human-machine
interfaces. This modality, however, is significantly more challenging than
off-line ASR, since real-time/low-latency constraints inevitably hinder the use
of future information, that is known to be very helpful to perform robust
predictions. A popular solution to mitigate this issue consists of feeding
neural acoustic models with context windows that gather some future frames.
This introduces a latency which depends on the number of employed look-ahead
features. This paper explores a different approach, based on estimating the
future rather than waiting for it. Our technique encourages the hidden
representations of a unidirectional recurrent network to embed some useful
information about the future. Inspired by a recently proposed technique called
Twin Networks, we add a regularization term that forces forward hidden states
to be as close as possible to cotemporal backward ones, computed by a "twin"
neural network running backwards in time. The experiments, conducted on a
number of datasets, recurrent architectures, input features, and acoustic
conditions, have shown the effectiveness of this approach. One important
advantage is that our method does not introduce any additional computation at
test time if compared to standard unidirectional recurrent networks.
iParaphrasing: Extracting Visually Grounded Paraphrases via an Image
Authors: Chenhui Chu, Mayu Otani, Yuta Nakashima
Date: 2018-06-12 00:00:00
Proceeding: COLING 2018 8
Tasks: Image Captioning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

A paraphrase is a restatement of the meaning of a text in other words.
Paraphrases have been studied to enhance the performance of many natural
language processing tasks. In this paper, we propose a novel task iParaphrasing
to extract visually grounded paraphrases (VGPs), which are different phrasal
expressions describing the same visual concept in an image. These extracted
VGPs have the potential to improve language and image multimodal tasks such as
visual question answering and image captioning. How to model the similarity
between VGPs is the key of iParaphrasing. We apply various existing methods as
well as propose a novel neural network-based method with image attention, and
report the results of the first attempt toward iParaphrasing.
Distance-Free Modeling of Multi-Predicate Interactions in End-to-End Japanese Predicate-Argument Structure Analysis
Authors: Yuichiroh Matsubayashi, Kentaro Inui
Date: 2018-06-11 00:00:00
Proceeding: COLING 2018 8

Capturing interactions among multiple predicate-argument structures (PASs) is
a crucial issue in the task of analyzing PAS in Japanese. In this paper, we
propose new Japanese PAS analysis models that integrate the label prediction
information of arguments in multiple PASs by extending the input and last
layers of a standard deep bidirectional recurrent neural network (bi-RNN)
model. In these models, using the mechanisms of pooling and attention, we aim
to directly capture the potential interactions among multiple PASs, without
being disturbed by the word order and distance. Our experiments show that the
proposed models improve the prediction accuracy specifically for cases where
the predicate and argument are in an indirect dependency relation and achieve a
new state of the art in the overall $F_1$ on a standard benchmark corpus.
Sharp Analysis of a Simple Model for Random Forests
Authors: Jason M. Klusowski
Date: 2018-05-07 00:00:00
Tasks: regression

Random forests have become an important tool for improving accuracy in regression and classification problems since their inception by Leo Breiman in 2001. In this paper, we revisit a historically important random forest model originally proposed by Breiman in 2004 and later studied by G\'erard Biau in 2012, where a feature is selected at random and the splits occurs at the midpoint of the node along the chosen feature. If the regression function is Lipschitz and depends only on a small subset of $ S $ out of $ d $ features, we show that, given access to $ n $ observations and properly tuned split probabilities, the mean-squared prediction error is $ O((n(\log n)^{(S-1)/2})^{-\frac{1}{S\log2+1}}) $. This positively answers an outstanding question of Biau about whether the rate of convergence for this random forest model could be improved. Furthermore, by a refined analysis of the approximation and estimation errors for linear models, we show that this rate cannot be improved in general. Finally, we generalize our analysis and improve extant prediction error bounds for another random forest model in which each tree is constructed from subsampled data and the splits are performed at the empirical median along a chosen feature.
The NES Music Database: A multi-instrumental dataset with expressive performance attributes
Authors: Chris Donahue, Huanru Henry Mao, Julian McAuley
Date: 2018-06-12 00:00:00
Tasks: Music Generation

Existing research on music generation focuses on composition, but often
ignores the expressive performance characteristics required for plausible
renditions of resultant pieces. In this paper, we introduce the Nintendo
Entertainment System Music Database (NES-MDB), a large corpus allowing for
separate examination of the tasks of composition and performance. NES-MDB
contains thousands of multi-instrumental songs composed for playback by the
compositionally-constrained NES audio synthesizer. For each song, the dataset
contains a musical score for four instrument voices as well as expressive
attributes for the dynamics and timbre of each voice. Unlike datasets comprised
of General MIDI files, NES-MDB includes all of the information needed to render
exact acoustic performances of the original compositions. Alongside the
dataset, we provide a tool that renders generated compositions as NES-style
audio by emulating the device's audio processor. Additionally, we establish
baselines for the tasks of composition, which consists of learning the
semantics of composing for the NES synthesizer, and performance, which involves
finding a mapping between a composition and realistic expressive attributes.
Generalized Zero-Shot Learning via Synthesized Examples
Authors: Vinay Kumar Verma, Gundeep Arora, Ashish Mishra, Piyush Rai
Date: 2017-12-11 00:00:00
Proceeding: CVPR 2018 6
Tasks: Attribute, Decoder, General Classification, Generalized Zero-Shot Learning, Zero-Shot Learning

We present a generative framework for generalized zero-shot learning where
the training and test classes are not necessarily disjoint. Built upon a
variational autoencoder based architecture, consisting of a probabilistic
encoder and a probabilistic conditional decoder, our model can generate novel
exemplars from seen/unseen classes, given their respective class attributes.
These exemplars can subsequently be used to train any off-the-shelf
classification model. One of the key aspects of our encoder-decoder
architecture is a feedback-driven mechanism in which a discriminator (a
multivariate regressor) learns to map the generated exemplars to the
corresponding class attribute vectors, leading to an improved generator. Our
model's ability to generate and leverage examples from unseen classes to train
the classification model naturally helps to mitigate the bias towards
predicting seen classes in generalized zero-shot learning settings. Through a
comprehensive set of experiments, we show that our model outperforms several
state-of-the-art methods, on several benchmark datasets, for both standard as
well as generalized zero-shot learning.
Learning Multilingual Topics from Incomparable Corpus
Authors: Shudong Hao, Michael J. Paul
Date: 2018-06-11 00:00:00
Tasks: Topic Models, Transfer Learning

Multilingual topic models enable crosslingual tasks by extracting consistent
topics from multilingual corpora. Most models require parallel or comparable
training corpora, which limits their ability to generalize. In this paper, we
first demystify the knowledge transfer mechanism behind multilingual topic
models by defining an alternative but equivalent formulation. Based on this
analysis, we then relax the assumption of training data required by most
existing models, creating a model that only requires a dictionary for training.
Experiments show that our new method effectively learns coherent multilingual
topics from partially and fully incomparable corpora with limited amounts of
dictionary resources.
Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks
Authors: Clemens Seibold, Wojciech Samek, Anna Hilsmann, Peter Eisert
Date: 2018-06-11 00:00:00
Tasks: Decision Making, Position

Artificial neural networks tend to learn only what they need for a task. A
manipulation of the training data can counter this phenomenon. In this paper,
we study the effect of different alterations of the training data, which limit
the amount and position of information that is available for the decision
making. We analyze the accuracy and robustness against semantic and black box
attacks on the networks that were trained on different training data
modifications for the particular example of morphing attacks. A morphing attack
is an attack on a biometric facial recognition system where the system is
fooled to match two different individuals with the same synthetic face image.
Such a synthetic image can be created by aligning and blending images of the
two individuals that should be matched with this image.
Smoothed Action Value Functions for Learning Gaussian Policies
Authors: Ofir Nachum, Mohammad Norouzi, George Tucker, Dale Schuurmans
Date: 2018-03-06 00:00:00
Proceeding: ICML 2018 7
Tasks: continuous-control, Continuous Control, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

State-action value functions (i.e., Q-values) are ubiquitous in reinforcement
learning (RL), giving rise to popular algorithms such as SARSA and Q-learning.
We propose a new notion of action value defined by a Gaussian smoothed version
of the expected Q-value. We show that such smoothed Q-values still satisfy a
Bellman equation, making them learnable from experience sampled from an
environment. Moreover, the gradients of expected reward with respect to the
mean and covariance of a parameterized Gaussian policy can be recovered from
the gradient and Hessian of the smoothed Q-value function. Based on these
relationships, we develop new algorithms for training a Gaussian policy
directly from a learned smoothed Q-value approximator. The approach is
additionally amenable to proximal optimization by augmenting the objective with
a penalty on KL-divergence from a previous policy. We find that the ability to
learn both a mean and covariance during training leads to significantly
improved results on standard continuous control benchmarks.
Group Normalization
Authors: Yuxin Wu, Kaiming He
Date: 2018-03-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object, object-detection, Object Detection, Video Classification

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.