pwc-archive/papers-with-abstracts huggingface dataset:
Animating Arbitrary Objects via Deep Motion Transfer
Authors: Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, Nicu Sebe
Date: 2018-12-20 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Animation, motion prediction, Object, Video Generation

This paper introduces a novel deep learning framework for image animation. Given an input image with a target object and a driving video sequence depicting a moving object, our framework generates a video in which the target object is animated according to the driving sequence. This is achieved through a deep architecture that decouples appearance and motion information. Our framework consists of three main modules: (i) a Keypoint Detector unsupervisely trained to extract object keypoints, (ii) a Dense Motion prediction network for generating dense heatmaps from sparse keypoints, in order to better encode motion information and (iii) a Motion Transfer Network, which uses the motion heatmaps and appearance information extracted from the input image to synthesize the output frames. We demonstrate the effectiveness of our method on several benchmark datasets, spanning a wide variety of object appearances, and show that our approach outperforms state-of-the-art image animation and video generation methods. Our source code is publicly available.
A Scale Invariant Approach for Sparse Signal Recovery
Authors: Yaghoub Rahimi, Chao Wang, Hongbo Dong, Yifei Lou
Date: 2018-12-20 00:00:00
Tasks: MRI Reconstruction

In this paper, we study the ratio of the $L_1 $ and $L_2 $ norms, denoted as $L_1/L_2$, to promote sparsity. Due to the non-convexity and non-linearity, there has been little attention to this scale-invariant model. Compared to popular models in the literature such as the $L_p$ model for $p\in(0,1)$ and the transformed $L_1$ (TL1), this ratio model is parameter free. Theoretically, we present a strong null space property (sNSP) and prove that any sparse vector is a local minimizer of the $L_1 /L_2 $ model provided with this sNSP condition. Computationally, we focus on a constrained formulation that can be solved via the alternating direction method of multipliers (ADMM). Experiments show that the proposed approach is comparable to the state-of-the-art methods in sparse recovery. In addition, a variant of the $L_1/L_2$ model to apply on the gradient is also discussed with a proof-of-concept example of the MRI reconstruction.
Three Dimensional Reconstruction of Botanical Trees with Simulatable Geometry
Authors: Ed Quigley, Winnie Lin, Yilin Zhu, Ronald Fedkiw
Date: 2018-12-20 00:00:00

We tackle the challenging problem of creating full and accurate three
dimensional reconstructions of botanical trees with the topological and
geometric accuracy required for subsequent physical simulation, e.g. in
response to wind forces. Although certain aspects of our approach would benefit
from various improvements, our results exceed the state of the art especially
in geometric and topological complexity and accuracy. Starting with two
dimensional RGB image data acquired from cameras attached to drones, we create
point clouds, textured triangle meshes, and a simulatable and skinned
cylindrical articulated rigid body model. We discuss the pros and cons of each
step of our pipeline, and in order to stimulate future research we make the raw
and processed data from every step of the pipeline as well as the final
geometric reconstructions publicly available.
SMILER: Saliency Model Implementation Library for Experimental Research
Authors: Calden Wloka, Toni Kunić, Iuliia Kotseruba, Ramin Fahimi, Nicholas Frosst, Neil D. B. Bruce, John K. Tsotsos
Date: 2018-12-20 00:00:00
Tasks: model

The Saliency Model Implementation Library for Experimental Research (SMILER)
is a new software package which provides an open, standardized, and extensible
framework for maintaining and executing computational saliency models. This
work drastically reduces the human effort required to apply saliency algorithms
to new tasks and datasets, while also ensuring consistency and procedural
correctness for results and conclusions produced by different parties. At its
launch SMILER already includes twenty three saliency models (fourteen models
based in MATLAB and nine supported through containerization), and the open
design of SMILER encourages this number to grow with future contributions from
the community. The project may be downloaded and contributed to through its
GitHub page: https://github.com/tsotsoslab/smiler
Decentralized Decision-Making Over Multi-Task Networks
Authors: Sahar Khawatmi, Abdelhak M. Zoubir, Ali H. Sayed
Date: 2018-12-20 00:00:00
Tasks: Decision Making

In important applications involving multi-task networks with multiple
objectives, agents in the network need to decide between these multiple
objectives and reach an agreement about which single objective to follow for
the network. In this work we propose a distributed decision-making algorithm.
The agents are assumed to observe data that may be generated by different
models. Through localized interactions, the agents reach agreement about which
model to track and interact with each other in order to enhance the network
performance. We investigate the approach for both static and mobile networks.
The simulations illustrate the performance of the proposed strategies.
A General Approach to Domain Adaptation with Applications in Astronomy
Authors: Ricardo Vilalta, Kinjal Dhar Gupta, Dainis Boumber, Mikhail M. Meskhi
Date: 2018-12-20 00:00:00
Tasks: Active Learning, Astronomy, Domain Adaptation, Transfer Learning

The ability to build a model on a source task and subsequently adapt such
model on a new target task is a pervasive need in many astronomical
applications. The problem is generally known as transfer learning in machine
learning, where domain adaptation is a popular scenario. An example is to build
a predictive model on spectroscopic data to identify Supernovae IA, while
subsequently trying to adapt such model on photometric data. In this paper we
propose a new general approach to domain adaptation that does not rely on the
proximity of source and target distributions. Instead we simply assume a strong
similarity in model complexity across domains, and use active learning to
mitigate the dependency on source examples. Our work leads to a new formulation
for the likelihood as a function of empirical error using a theoretical
learning bound; the result is a novel mapping from generalization error to a
likelihood estimation. Results using two real astronomical problems, Supernova
Ia classification and identification of Mars landforms, show two main
advantages with our approach: increased accuracy performance and substantial
savings in computational cost.
Reducing Sampling Ratios Improves Bagging in Sparse Regression
Authors: Luoluo Liu, Sang Peter Chin, Trac. D. Tran
Date: 2018-12-20 00:00:00
Tasks: regression

Bagging, a powerful ensemble method from machine learning, improves the
performance of unstable predictors. Although the power of Bagging has been
shown mostly in classification problems, we demonstrate the success of
employing Bagging in sparse regression over the baseline method (L1
minimization). The framework employs the generalized version of the original
Bagging with various bootstrap ratios. The performance limits associated with
different choices of bootstrap sampling ratio L/m and number of estimates K is
analyzed theoretically. Simulation shows that the proposed method yields
state-of-the-art recovery performance, outperforming L1 minimization and
Bolasso in the challenging case of low levels of measurements. A lower L/m
ratio (60% - 90%) leads to better performance, especially with a small number
of measurements. With the reduced sampling rate, SNR improves over the original
Bagging by up to 24%. With a properly chosen sampling ratio, a reasonably small
number of estimates K = 30 gives satisfying result, even though increasing K is
discovered to always improve or at least maintain the performance.
An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning Process
Authors: Yuanzhi Huang, Eamonn Ahearne, Szymon Baron, Andrew Parnell
Date: 2018-12-20 00:00:00
Tasks: Anomaly Detection

We examined the use of three conventional anomaly detection methods and
assess their potential for on-line tool wear monitoring. Through efficient data
processing and transformation of the algorithm proposed here, in a real-time
environment, these methods were tested for fast evaluation of cutting tools on
CNC machines. The three-dimensional force data streams we used were extracted
from a turning experiment of 21 runs for which a tool was run until it
generally satisfied an end-of-life criterion. Our real-time anomaly detection
algorithm was scored and optimised according to how precisely it can predict
the progressive wear of the tool flank. Most of our tool wear predictions were
accurate and reliable as illustrated in our off-line simulation results.
Particularly when the multivariate analysis was applied, the algorithm we
develop was found to be very robust across different scenarios and against
parameter changes. It shall be reasonably easy to apply our approach elsewhere
for real-time tool wear analytics.
A Primal-dual Learning Algorithm for Personalized Dynamic Pricing with an Inventory Constraint
Authors: Ningyuan Chen, Guillermo Gallego
Date: 2018-12-20 00:00:00
Tasks: Vocal Bursts Type Prediction

We consider the problem of a firm seeking to use personalized pricing to sell an exogenously given stock of a product over a finite selling horizon to different consumer types. We assume that the type of an arriving consumer can be observed but the demand function associated with each type is initially unknown. The firm sets personalized prices dynamically for each type and attempts to maximize the revenue over the season. We provide a learning algorithm that is near-optimal when the demand and capacity scale in proportion. The algorithm utilizes the primal-dual formulation of the problem and learns the dual optimal solution explicitly. It allows the algorithm to overcome the curse of dimensionality (the rate of regret is independent of the number of types) and sheds light on novel algorithmic designs for learning problems with resource constraints.
A Smart Security System with Face Recognition
Authors: Trung Nguyen, Barth Lakshmanan, Weihua Sheng
Date: 2018-12-03 00:00:00
Tasks: Face Recognition

Web-based technology has improved drastically in the past decade. As a
result, security technology has become a major help to protect our daily life.
In this paper, we propose a robust security based on face recognition system
(SoF). In particular, we develop this system to giving access into a home for
authenticated users. The classifier is trained by using a new adaptive learning
method. The training data are initially collected from social networks. The
accuracy of the classifier is incrementally improved as the user starts using
the system. A novel method has been introduced to improve the classifier model
by human interaction and social media. By using a deep learning framework -
TensorFlow, it will be easy to reuse the framework to adopt with many devices
and applications.
Privacy-Preserving Distributed Parameter Estimation for Probability Distribution of Wind Power Forecast Error
Authors: Mengshuo Jia, Shaowei Huang, Zhiwen Wang, Chen Shen
Date: 2018-12-17 00:00:00
Tasks: Decision Making, parameter estimation, Privacy Preserving

Building the conditional probability distribution of wind power forecast errors benefits both wind farms (WFs) and independent system operators (ISOs). Establishing the joint probability distribution of wind power and the corresponding forecast data of spatially correlated WFs is the foundation for deriving the conditional probability distribution. Traditional parameter estimation methods for probability distributions require the collection of historical data of all WFs. However, in the context of multi-regional interconnected grids, neither regional ISOs nor WFs can collect the raw data of WFs in other regions due to privacy or competition considerations. Therefore, based on the Gaussian mixture model, this paper first proposes a privacy-preserving distributed expectation-maximization algorithm to estimate the parameters of the joint probability distribution. This algorithm consists of two original methods: (1) a privacy-preserving distributed summation algorithm and (2) a privacy-preserving distributed inner product algorithm. Then, we derive each WF's conditional probability distribution of forecast error from the joint one. By the proposed algorithms, WFs only need local calculations and privacy-preserving neighboring communications to achieve the whole parameter estimation. These algorithms are verified using the wind integration data set published by the NREL.
Machine Learning for Wireless Link Quality Estimation: A Survey
Authors: Gregor Cerar, Halil Yetgin, Mihael Mohorčič, Carolina Fortuna
Date: 2018-12-07 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, Link Quality Estimation, Survey

Since the emergence of wireless communication networks, a plethora of research papers focus their attention on the quality aspects of wireless links. The analysis of the rich body of existing literature on link quality estimation using models developed from data traces indicates that the techniques used for modeling link quality estimation are becoming increasingly sophisticated. A number of recent estimators leverage machine learning (ML) techniques that require a sophisticated design and development process, each of which has a great potential to significantly affect the overall model performance. In this paper, we provide a comprehensive survey on link quality estimators developed from empirical data and then focus on the subset that use ML algorithms. We analyze ML-based link quality estimation (LQE) models from two perspectives using performance data. Firstly, we focus on how they address quality requirements that are important from the perspective of the applications they serve. Secondly, we analyze how they approach the standard design steps commonly used in the ML community. Having analyzed the scientific body of the survey, we review existing open source datasets suitable for LQE research. Finally, we round up our survey with the lessons learned and design guidelines for ML-based LQE development and dataset collection.
It's going to be okay: Measuring Access to Support in Online Communities
Authors: Zijian Wang, David Jurgens
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10

People use online platforms to seek out support for their informational and emotional needs. Here, we ask what effect does revealing one{'}s gender have on receiving support. To answer this, we create (i) a new dataset and method for identifying supportive replies and (ii) new methods for inferring gender from text and name. We apply these methods to create a new massive corpus of 102M online interactions with gender-labeled users, each rated by degree of supportiveness. Our analysis shows wide-spread and consistent disparity in support: identifying as a woman is associated with higher rates of support - but also higher rates of disparagement.
Associative Multichannel Autoencoder for Multimodal Word Representation
Authors: Shaonan Wang, Jiajun Zhang, Cheng-qing Zong
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10

In this paper we address the problem of learning multimodal word representations by integrating textual, visual and auditory inputs. Inspired by the re-constructive and associative nature of human memory, we propose a novel associative multichannel autoencoder (AMA). Our model first learns the associations between textual and perceptual modalities, so as to predict the missing perceptual information of concepts. Then the textual and predicted perceptual representations are fused through reconstructing their original and associated embeddings. Using a gating mechanism our model assigns different weights to each modality according to the different concepts. Results on six benchmark concept similarity tests show that the proposed method significantly outperforms strong unimodal baselines and state-of-the-art multimodal models.
Temporally Grounding Natural Sentence in Video
Authors: Jingyuan Chen, Xinpeng Chen, Lin Ma, Zequn Jie, Tat-Seng Chua
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence, Video Captioning

We introduce an effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences. Specifically, a novel Temporal GroundNet (TGN) is proposed to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence. TGN sequentially scores a set of temporal candidates ended at each frame based on the exploited frame-by-word interactions, and finally grounds the segment corresponding to the sentence. Unlike traditional methods treating the overlapping segments separately in a sliding window fashion, TGN aggregates the historical information and generates the final grounding result in one single pass. We extensively evaluate our proposed TGN on three public datasets with significant improvements over the state-of-the-arts. We further show the consistent effectiveness and efficiency of TGN through an ablation study and a runtime test.
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism
Authors: Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Shengping Liu
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Chinese Named Entity Recognition, Chinese Word Segmentation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning

Named entity recognition (NER) is an important task in natural language processing area, which needs to determine entities boundaries and classify them into pre-defined categories. For Chinese NER task, there is only a very small amount of annotated data available. Chinese NER task and Chinese word segmentation (CWS) task have many similar word boundaries. There are also specificities in each task. However, existing methods for Chinese NER either do not exploit word boundary information from CWS or cannot filter the specific information of CWS. In this paper, we propose a novel adversarial transfer learning framework to make full use of task-shared boundaries information and prevent the task-specific features of CWS. Besides, since arbitrary character can provide important cues when predicting entity type, we exploit self-attention to explicitly capture long range dependencies between two tokens. Experimental results on two different widely used datasets show that our proposed model significantly and consistently outperforms other state-of-the-art methods.
Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal Guidance
Authors: Yftah Ziser, Roi Reichart
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Word Embeddings

While cross-domain and cross-language transfer have long been prominent topics in NLP research, their combination has hardly been explored. In this work we consider this problem, and propose a framework that builds on pivot-based learning, structure-aware Deep Neural Networks (particularly LSTMs and CNNs) and bilingual word embeddings, with the goal of training a model on labeled data from one (language, domain) pair so that it can be effectively applied to another (language, domain) pair. We consider two setups, differing with respect to the unlabeled data available for model training. In the full setup the model has access to unlabeled data from both pairs, while in the lazy setup, which is more realistic for truly resource-poor languages, unlabeled data is available for both domains but only for the source language. We design our model for the lazy setup so that for a given target domain, it can train once on the source language and then be applied to any target language without re-training. In experiments with nine English-German and nine English-French domain pairs our best model substantially outperforms previous models even when it is trained in the lazy setup and previous models are trained in the full setup.
On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling
Authors: Daniela Gerz, Ivan Vuli{\'c}, Edoardo Maria Ponti, Roi Reichart, Anna Korhonen
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Modeling, Language Modelling, Relation

A key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. However, this ambition is largely hampered by the variation in structural and semantic properties, i.e. the typological profiles of the world{'}s languages. In this work, we analyse the implications of this variation on the language modeling (LM) task. We present a large-scale study of state-of-the art n-gram based and neural language models on 50 typologically diverse languages covering a wide variety of morphological systems. Operating in the full vocabulary LM setup focused on word-level prediction, we demonstrate that a coarse typology of morphological systems is predictive of absolute LM performance. Moreover, fine-grained typological features such as exponence, flexivity, fusion, and inflectional synthesis are borne out to be responsible for the proliferation of low-frequency phenomena which are organically difficult to model by statistical architectures, or for the meaning ambiguity of character n-grams. Our study strongly suggests that these features have to be taken into consideration during the construction of next-level language-agnostic LM architectures, capable of handling morphologically complex languages such as Tamil or Korean.
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning
Authors: Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: General Classification, Multi-Task Learning, Sentiment Analysis, Sentiment Classification

Sentiment expression in microblog posts can be affected by user{'}s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user{'}s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem. Results on English and Chinese microblog datasets confirm that our method outperforms state-of-the-art baseline algorithms with large margins.