pwc-archive/papers-with-abstracts huggingface dataset:
Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval
Authors: Qing Liu, Lingxi Xie, Huiyu Wang, Alan Yuille
Date: 2019-04-05 00:00:00
Proceeding: ICCV 2019 10
Tasks: Diagnostic, Domain Adaptation, Image Retrieval, Retrieval, Sketch-Based Image Retrieval, Zero-Shot Learning

Sketch-based image retrieval (SBIR) is widely recognized as an important vision problem which implies a wide range of real-world applications. Recently, research interests arise in solving this problem under the more realistic and challenging setting of zero-shot learning. In this paper, we investigate this problem from the viewpoint of domain adaptation which we show is critical in improving feature embedding in the zero-shot scenario. Based on a framework which starts with a pre-trained model on ImageNet and fine-tunes it on the training set of SBIR benchmark, we advocate the importance of preserving previously acquired knowledge, e.g., the rich discriminative features learned from ImageNet, to improve the model's transfer ability. For this purpose, we design an approach named Semantic-Aware Knowledge prEservation (SAKE), which fine-tunes the pre-trained model in an economical way and leverages semantic information, e.g., inter-class relationship, to achieve the goal of knowledge preservation. Zero-shot experiments on two extended SBIR datasets, TU-Berlin and Sketchy, verify the superior performance of our approach. Extensive diagnostic experiments validate that knowledge preserved benefits SBIR in zero-shot settings, as a large fraction of the performance gain is from the more properly structured feature embedding for photo images. Code is available at: https://github.com/qliu24/SAKE.
Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?
Authors: Rameen Abdal, Yipeng Qin, Peter Wonka
Date: 2019-04-05 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Morphing, Style Transfer

We propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
Authors: Afra Alishahi, Grzegorz Chrupała, Tal Linzen
Date: 2019-04-05 00:00:00

The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques
specifically developed for analyzing and understanding the inner-workings and
representations acquired by neural models of language. Approaches included:
systematic manipulation of input to neural networks and investigating the
impact on their performance, testing whether interpretable knowledge can be
decoded from intermediate representations acquired by neural networks,
proposing modifications to neural network architectures to make their knowledge
state or generated output more explainable, and examining the performance of
networks on simplified or formal languages. Here we review a number of
representative studies in each category.
Abusive Language Detection with Graph Convolutional Networks
Authors: Pushkar Mishra, Marco del Tredici, Helen Yannakoudakis, Ekaterina Shutova
Date: 2019-04-05 00:00:00
Proceeding: NAACL 2019 6
Tasks: Abuse Detection

Abuse on the Internet represents a significant societal problem of our time.
Previous research on automated abusive language detection in Twitter has shown
that community-based profiling of users is a promising technique for this task.
However, existing approaches only capture shallow properties of online
communities by modeling follower-following relationships. In contrast, working
with graph convolutional networks (GCNs), we present the first approach that
captures not only the structure of online communities but also the linguistic
behavior of the users within them. We show that such a heterogeneous
graph-structured modeling of communities significantly advances the current
state of the art in abusive language detection.
Deep Learning Sentiment Analysis of Amazon.com Reviews and Ratings
Authors: Nishit Shrestha, Fatma Nasoz
Date: 2019-04-04 00:00:00
Tasks: Deep Learning, Sentiment Analysis

Our study employs sentiment analysis to evaluate the compatibility of
Amazon.com reviews with their corresponding ratings. Sentiment analysis is the
task of identifying and classifying the sentiment expressed in a piece of text
as being positive or negative. On e-commerce websites such as Amazon.com,
consumers can submit their reviews along with a specific polarity rating. In
some instances, there is a mismatch between the review and the rating. To
identify the reviews with mismatched ratings we performed sentiment analysis
using deep learning on Amazon.com product review data. Product reviews were
converted to vectors using paragraph vector, which then was used to train a
recurrent neural network with gated recurrent unit. Our model incorporated both
semantic relationship of review text and product information. We also developed
a web service application that predicts the rating score for a submitted review
using the trained model and if there is a mismatch between predicted rating
score and submitted rating score, it provides feedback to the reviewer.
A Many Objective Optimization Approach for Transfer Learning in EEG Classification
Authors: Monalisa Pal, Sanghamitra Bandyopadhyay, Saugat Bhattacharyya
Date: 2019-04-04 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, Motor Imagery, Transfer Learning

In Brain-Computer Interfacing (BCI), due to inter-subject non-stationarities
of electroencephalogram (EEG), classifiers are trained and tested using EEG
from the same subject. When physical disabilities bottleneck the natural
modality of performing a task, acquisition of ample training data is difficult
which practically obstructs classifier training. Previous works have tackled
this problem by generalizing the feature space amongst multiple subjects
including the test subject. This work aims at knowledge transfer to classify
EEG of the target subject using a classifier trained with the EEG of another
unit source subject. A many-objective optimization framework is proposed where
optimal weights are obtained for projecting features in another dimension such
that single source-trained target EEG classification performance is maximized
with the modified features. To validate the approach, motor imagery tasks from
the BCI Competition III Dataset IVa are classified using power spectral density
based features and linear support vector machine. Several performance metrics,
improvement in accuracy, sensitivity to the dimension of the projected space,
assess the efficacy of the proposed approach. Addressing single-source training
promotes independent living of differently-abled individuals by reducing
assistance from others. The proposed approach eliminates the requirement of EEG
from multiple source subjects and is applicable to any existing feature
extractors and classifiers. Source code is available at
http://worksupplements.droppages.com/tlbci.html.
Revisiting Adversarial Autoencoder for Unsupervised Word Translation with Cycle Consistency and Improved Training
Authors: Tasnim Mohiuddin, Shafiq Joty
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Translation, Word Translation

Adversarial training has shown impressive success in learning bilingual
dictionary without any parallel data by mapping monolingual embeddings to a
shared space. However, recent work has shown superior performance for
non-adversarial methods in more challenging language pairs. In this work, we
revisit adversarial autoencoder for unsupervised word translation and propose
two novel extensions to it that yield more stable training and improved
results. Our method includes regularization terms to enforce cycle consistency
and input reconstruction, and puts the target encoders as an adversary against
the corresponding discriminator. Extensive experimentations with European,
non-European and low-resource languages show that our method is more robust and
achieves better performance than recently proposed adversarial and
non-adversarial approaches.
Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation
Authors: Weijia Xu, Xing Niu, Marine Carpuat
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, Translation

Despite some empirical success at correcting exposure bias in machine translation, scheduled sampling algorithms suffer from a major drawback: they incorrectly assume that words in the reference translations and in sampled sequences are aligned at each time step. Our new differentiable sampling algorithm addresses this issue by optimizing the probability that the reference can be aligned with the sampled output, based on a soft alignment predicted by the model itself. As a result, the output distribution at each time step is evaluated with respect to the whole predicted sequence. Experiments on IWSLT translation tasks show that our approach improves BLEU compared to maximum likelihood and scheduled sampling baselines. In addition, our approach is simpler to train with no need for sampling schedule and yields models that achieve larger improvements with smaller beam sizes.
Simple Question Answering with Subgraph Ranking and Joint-Scoring
Authors: Wenbo Zhao, Tagyoung Chung, Anuj Goyal, Angeliki Metallinou
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Fact Selection, Question Answering, Relation

Knowledge graph based simple question answering (KBSQA) is a major area of
research within question answering. Although only dealing with simple
questions, i.e., questions that can be answered through a single knowledge base
(KB) fact, this task is neither simple nor close to being solved. Targeting on
the two main steps, subgraph selection and fact selection, the research
community has developed sophisticated approaches. However, the importance of
subgraph ranking and leveraging the subject--relation dependency of a KB fact
have not been sufficiently explored. Motivated by this, we present a unified
framework to describe and analyze existing approaches. Using this framework as
a starting point, we focus on two aspects: improving subgraph selection through
a novel ranking method and leveraging the subject--relation dependency by
proposing a joint scoring CNN model with a novel loss function that enforces
the well-order of scores. Our methods achieve a new state of the art (85.44% in
accuracy) on the SimpleQuestions dataset.
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings
Authors: Thomas Manzini, Yao Chong Lim, Yulia Tsvetkov, Alan W. black
Date: 2019-04-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: BIG-bench Machine Learning, Word Embeddings

Online texts -- across genres, registers, domains, and styles -- are riddled with human stereotypes, expressed in overt or subtle ways. Word embeddings, trained on these texts, perpetuate and amplify these stereotypes, and propagate biases to machine learning models that use word embeddings as features. In this work, we propose a method to debias word embeddings in multiclass settings such as race and religion, extending the work of (Bolukbasi et al., 2016) from the binary setting, such as binary gender. Next, we propose a novel methodology for the evaluation of multiclass debiasing. We demonstrate that our multiclass debiasing is robust and maintains the efficacy in standard NLP tasks.
Evaluating KGR10 Polish word embeddings in the recognition of temporal expressions using BiLSTM-CRF
Authors: Jan Kocoń, Michał Gawor
Date: 2019-04-03 00:00:00
Tasks: Word Embeddings

The article introduces a new set of Polish word embeddings, built using KGR10
corpus, which contains more than 4 billion words. These embeddings are
evaluated in the problem of recognition of temporal expressions (timexes) for
the Polish language. We described the process of KGR10 corpus creation and a
new approach to the recognition problem using Bidirectional Long-Short Term
Memory (BiLSTM) network with additional CRF layer, where specific embeddings
are essential. We presented experiments and conclusions drawn from them.
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations
Authors: Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre Tartakovsky
Date: 2019-04-02 00:00:00

We investigate the use of discrete and continuous versions of
physics-informed neural network methods for learning unknown dynamics or
constitutive relations of a dynamical system. For the case of unknown dynamics,
we represent all the dynamics with a deep neural network (DNN). When the
dynamics of the system are known up to the specification of constitutive
relations (that can depend on the state of the system), we represent these
constitutive relations with a DNN. The discrete versions combine classical
multistep discretization methods for dynamical systems with neural network
based machine learning methods. On the other hand, the continuous versions
utilize deep neural networks to minimize the residual function for the
continuous governing equations. We use the case of a fedbatch bioreactor system
to study the effectiveness of these approaches and discuss conditions for their
applicability. Our results indicate that the accuracy of the trained neural
network models is much higher for the cases where we only have to learn a
constitutive relation instead of the whole dynamics. This finding corroborates
the well-known fact from scientific computing that building as much structural
information is available into an algorithm can enhance its efficiency and/or
accuracy.
Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm Detection
Authors: Mattia Antonino Di Gangi, Giosué Lo Bosco, Giovanni Pilato
Date: 2019-04-02 00:00:00
Tasks: Sarcasm Detection

Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding. Many labeled corpora have been extracted from several sources to accomplish this task, and it seems that sarcasm is conveyed in different ways for different domains. Nonetheless, very little work has been done for comparing different methods among the available corpora. Furthermore, usually, each author collects and uses their own datasets to evaluate his own method. In this paper, we show that sarcasm detection can be tackled by applying classical machine learning algorithms to input texts sub-symbolically represented in a Latent Semantic space. The main consequence is that our studies establish both reference datasets and baselines for the sarcasm detection problem that could serve the scientific community to test newly proposed methods.
Task Oriented Channel State Information Quantization
Authors: Hang Zou, Chao Zhang, Samson Lasaulce
Date: 2019-04-02 00:00:00
Tasks: Quantization

In this paper, we propose a new perspective for quantizing a signal and more
specifically the channel state information (CSI). The proposed point of view is
fully relevant for a receiver which has to send a quantized version of the
channel state to the transmitter. Roughly, the key idea is that the receiver
sends the right amount of information to the transmitter so that the latter be
able to take its (resource allocation) decision. More formally, the decision
task of the transmitter is to maximize an utility function u(x;g) with respect
to x (e.g., a power allocation vector) given the knowledge of a quantized
version of the function parameters g. We exhibit a special case of an
energy-efficient power control (PC) problem for which the optimal task oriented
CSI quantizer (TOCQ) can be found analytically. For more general utility
functions, we propose to use neural networks (NN) based learning. Simulations
show that the compression rate obtained by adapting the feedback information
rate to the function to be optimized may be significantly increased.
Probabilistic Regression of Rotations using Quaternion Averaging and a Deep Multi-Headed Network
Authors: Valentin Peretroukhin, Brandon Wagstaff, Matthew Giamou, Jonathan Kelly
Date: 2019-04-01 00:00:00
Tasks: Motion Estimation, regression, Visual Odometry

Accurate estimates of rotation are crucial to vision-based motion estimation in augmented reality and robotics. In this work, we present a method to extract probabilistic estimates of rotation from deep regression models. First, we build on prior work and argue that a multi-headed network structure we name HydraNet provides better calibrated uncertainty estimates than methods that rely on stochastic forward passes. Second, we extend HydraNet to targets that belong to the rotation group, SO(3), by regressing unit quaternions and using the tools of rotation averaging and uncertainty injection onto the manifold to produce three-dimensional covariances. Finally, we present results and analysis on a synthetic dataset, learn consistent orientation estimates on the 7-Scenes dataset, and show how we can use our learned covariances to fuse deep estimates of relative orientation with classical stereo visual odometry to improve localization on the KITTI dataset.
Robust Alignment for Panoramic Stitching via an Exact Rank Constraint
Authors: Yuelong Li, Mohammad Tofighi, Vishal Monga
Date: 2019-04-01 00:00:00

We study the problem of image alignment for panoramic stitching. Unlike most
existing approaches that are feature-based, our algorithm works on pixels
directly, and accounts for errors across the whole images globally.
Technically, we formulate the alignment problem as rank-1 and sparse matrix
decomposition over transformed images, and develop an efficient algorithm for
solving this challenging non-convex optimization problem. The algorithm reduces
to solving a sequence of subproblems, where we analytically establish exact
recovery conditions, convergence and optimality, together with convergence rate
and complexity. We generalize it to simultaneously align multiple images and
recover multiple homographies, extending its application scope towards vast
majority of practical scenarios. Experimental results demonstrate that the
proposed algorithm is capable of more accurately aligning the images and
generating higher quality stitched images than state-of-the-art methods.
Adaptation of Hierarchical Structured Models for Speech Act Recognition in Asynchronous Conversation
Authors: Tasnim Mohiuddin, Thanh-Tung Nguyen, Shafiq Joty
Date: 2019-04-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Word Embeddings

We address the problem of speech act recognition (SAR) in asynchronous
conversations (forums, emails). Unlike synchronous conversations (e.g.,
meetings, phone), asynchronous domains lack large labeled datasets to train an
effective SAR model. In this paper, we propose methods to effectively leverage
abundant unlabeled conversational data and the available labeled data from
synchronous domains. We carry out our research in three main steps. First, we
introduce a neural architecture based on hierarchical LSTMs and conditional
random fields (CRF) for SAR, and show that our method outperforms existing
methods when trained on in-domain data only. Second, we improve our initial SAR
models by semi-supervised learning in the form of pretrained word embeddings
learned from a large unlabeled conversational corpus. Finally, we employ
adversarial training to improve the results further by leveraging the labeled
data from synchronous domains and by explicitly modeling the distributional
shift in two domains.
MLSys: The New Frontier of Machine Learning Systems
Authors: Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael. I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
Date: 2019-03-29 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two.
Import2vec - Learning Embeddings for Software Libraries
Authors: Bart Theeten, Frederik Vandeputte, Tom Van Cutsem
Date: 2019-03-27 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

We consider the problem of developing suitable learning representations
(embeddings) for library packages that capture semantic similarity among
libraries. Such representations are known to improve the performance of
downstream learning tasks (e.g. classification) or applications such as
contextual search and analogical reasoning.
  We apply word embedding techniques from natural language processing (NLP) to
train embeddings for library packages ("library vectors"). Library vectors
represent libraries by similar context of use as determined by import
statements present in source code. Experimental results obtained from training
such embeddings on three large open source software corpora reveals that
library vectors capture semantically meaningful relationships among software
libraries, such as the relationship between frameworks and their plug-ins and
libraries commonly used together within ecosystems such as big data
infrastructure projects (in Java), front-end and back-end web development
frameworks (in JavaScript) and data science toolkits (in Python).
Anticipating cryptocurrency prices using machine learning
Authors: Laura Alessandretti, Abeer ElBahrawy, Luca Maria Aiello, Andrea Baronchelli
Date: 2018-05-22 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning and AI-assisted trading have attracted growing interest for
the past few years. Here, we use this approach to test the hypothesis that the
inefficiency of the cryptocurrency market can be exploited to generate abnormal
profits. We analyse daily data for $1,681$ cryptocurrencies for the period
between Nov. 2015 and Apr. 2018. We show that simple trading strategies
assisted by state-of-the-art machine learning algorithms outperform standard
benchmarks. Our results show that nontrivial, but ultimately simple,
algorithmic mechanisms can help anticipate the short-term evolution of the
cryptocurrency market.