pwc-archive/papers-with-abstracts huggingface dataset:
A Method For Dynamic Ensemble Selection Based on a Filter and an Adaptive Distance to Improve the Quality of the Regions of Competence
Authors: Rafael M. O. Cruz, George D. C. Cavalcanti, Tsang Ing Ren
Date: 2018-11-01 00:00:00
Tasks: General Classification

Dynamic classifier selection systems aim to select a group of classifiers
that is most adequate for a specific query pattern. This is done by defining a
region around the query pattern and analyzing the competence of the classifiers
in this region. However, the regions are often surrounded by noise which can
difficult the classifier selection. This fact makes the performance of most
dynamic selection systems no better than static selections. In this paper, we
demonstrate that the performance dynamic selection systems end up limited by
the quality of the regions extracted. Thereafter, we propose a new dynamic
classifier selection that improves the regions of competence in order to
achieve higher recognition rates. obtained from several classification
databases show the proposed method not only increase the recognition
performance but also decreases the computational cost.
Introduction to the 1st Place Winning Model of OpenImages Relationship Detection Challenge
Authors: Ji Zhang, Kevin Shih, Andrew Tao, Bryan Catanzaro, Ahmed Elgammal
Date: 2018-11-01 00:00:00
Tasks: Relationship Detection, Visual Relationship Detection

This article describes the model we built that achieved 1st place in the
OpenImage Visual Relationship Detection Challenge on Kaggle. Three key factors
contribute the most to our success: 1) language bias is a powerful baseline for
this task. We build the empirical distribution $P(predicate|subject,object)$ in
the training set and directly use that in testing. This baseline achieved the
2nd place when submitted; 2) spatial features are as important as visual
features, especially for spatial relationships such as "under" and "inside of";
3) It is a very effective way to fuse different features by first building
separate modules for each of them, then adding their output logits before the
final softmax layer. We show in ablation study that each factor can improve the
performance to a non-trivial extent, and the model reaches optimal when all of
them are combined.
Exposing Deep Fakes Using Inconsistent Head Poses
Authors: Xin Yang, Yuezun Li, Siwei Lyu
Date: 2018-11-01 00:00:00
Tasks: General Classification

In this paper, we propose a new method to expose AI-generated fake face
images or videos (commonly known as the Deep Fakes). Our method is based on the
observations that Deep Fakes are created by splicing synthesized face region
into the original image, and in doing so, introducing errors that can be
revealed when 3D head poses are estimated from the face images. We perform
experiments to demonstrate this phenomenon and further develop a classification
method based on this cue. Using features based on this cue, an SVM classifier
is evaluated using a set of real face images and Deep Fakes.
Implicit Regularization of Stochastic Gradient Descent in Natural Language Processing: Observations and Implications
Authors: Deren Lei, Zichen Sun, Yijun Xiao, William Yang Wang
Date: 2018-11-01 00:00:00

Deep neural networks with remarkably strong generalization performances are
usually over-parameterized. Despite explicit regularization strategies are used
for practitioners to avoid over-fitting, the impacts are often small. Some
theoretical studies have analyzed the implicit regularization effect of
stochastic gradient descent (SGD) on simple machine learning models with
certain assumptions. However, how it behaves practically in state-of-the-art
models and real-world datasets is still unknown. To bridge this gap, we study
the role of SGD implicit regularization in deep learning systems. We show pure
SGD tends to converge to minimas that have better generalization performances
in multiple natural language processing (NLP) tasks. This phenomenon coexists
with dropout, an explicit regularizer. In addition, neural network's finite
learning capability does not impact the intrinsic nature of SGD's implicit
regularization effect. Specifically, under limited training samples or with
certain corrupted labels, the implicit regularization effect remains strong. We
further analyze the stability by varying the weight initialization range. We
corroborate these experimental findings with a decision boundary visualization
using a 3-layer neural network for interpretation. Altogether, our work enables
a deepened understanding on how implicit regularization affects the deep
learning model and sheds light on the future study of the over-parameterized
model's generalization ability.
Exposing DeepFake Videos By Detecting Face Warping Artifacts
Authors: Yuezun Li, Siwei Lyu
Date: 2018-11-01 00:00:00
Tasks: Face Swapping

In this work, we describe a new deep learning based method that can effectively distinguish AI-generated fake videos (referred to as {\em DeepFake} videos hereafter) from real videos. Our method is based on the observations that current DeepFake algorithm can only generate images of limited resolutions, which need to be further warped to match the original faces in the source video. Such transforms leave distinctive artifacts in the resulting DeepFake videos, and we show that they can be effectively captured by convolutional neural networks (CNNs). Compared to previous methods which use a large amount of real and DeepFake generated images to train CNN classifier, our method does not need DeepFake generated images as negative training examples since we target the artifacts in affine face warping as the distinctive feature to distinguish real and fake images. The advantages of our method are two-fold: (1) Such artifacts can be simulated directly using simple image processing operations on a image to make it as negative example. Since training a DeepFake model to generate negative examples is time-consuming and resource-demanding, our method saves a plenty of time and resources in training data collection; (2) Since such artifacts are general existed in DeepFake videos from different sources, our method is more robust compared to others. Our method is evaluated on two sets of DeepFake video datasets for its effectiveness in practice.
Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities
Authors: Matthias Rottmann, Pascal Colling, Thomas-Paul Hack, Robin Chan, Fabian Hüger, Peter Schlicht, Hanno Gottschalk
Date: 2018-11-01 00:00:00
Tasks: General Classification, regression, Semantic Segmentation

We present a method that "meta" classifies whether seg-ments predicted by a semantic segmentation neural networkintersect with the ground truth. For this purpose, we employ measures of dispersion for predicted pixel-wise class probability distributions, like classification entropy, that yield heat maps of the input scene's size. We aggregate these dispersion measures segment-wise and derive metrics that are well-correlated with the segment-wise IoU of prediction and ground truth. This procedure yields an almost plug and play post-processing tool to rate the prediction quality of semantic segmentation networks on segment level. This is especially relevant for monitoring neural networks in online applications like automated driving or medical imaging where reliability is of utmost importance. In our tests, we use publicly available state-of-the-art networks trained on the Cityscapes dataset and the BraTS2017 dataset and analyze the predictive power of different metrics as well as different sets of metrics. To this end, we compute logistic LASSO regression fits for the task of classifying IoU=0 vs. IoU>0 per segment and obtain AUROC values of up to 91.55%. We complement these tests with linear regression fits to predict the segment-wise IoU and obtain prediction standard deviations of down to 0.130 as well as $R^2$ values of up to 84.15%. We show that these results clearly outperform standard approaches.
Analyzing and learning the language for different types of harassment
Authors: Mohammadreza Rezvan, Saeedeh Shekarpour, Faisal Alshargi, Krishnaprasad Thirunarayan, Valerie L. Shalin, Amit Sheth
Date: 2018-11-01 00:00:00
Tasks: Vocal Bursts Type Prediction

Disclaimer: This paper is concerned with violent online harassment. To describe the subject at an adequate level of realism, examples of our collected tweets involve violent, threatening, vulgar and hateful speech language in the context of racial, sexual, political, appearance and intellectual harassment. The presence of a significant amount of harassment in user-generated content and its negative impact calls for robust automatic detection approaches. This requires that we can identify different forms or types of harassment. Earlier work has classified harassing language in terms of hurtfulness, abusiveness, sentiment, and profanity. However, to identify and understand harassment more accurately, it is essential to determine the context that represents the interrelated conditions in which they occur. In this paper, we introduce the notion of contextual type to harassment involving five categories: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual and (v) political. We utilize an annotated corpus from Twitter distinguishing these types of harassment. To study the context for each type that sheds light on the linguistic meaning, interpretation, and distribution, we conduct two lines of investigation: an extensive linguistic analysis, and a statistical distribution of unigrams. We then build type-ware classifiers to automate the identification of type-specific harassment. Our experiments demonstrate that these classifiers provide competitive accuracy for identifying and analyzing harassment on social media. We present extensive discussion and major observations about the effectiveness of type-aware classifiers using a detailed comparison setup providing insight into the role of type-dependent features.
Online Embedding Compression for Text Classification using Low Rank Matrix Factorization
Authors: Anish Acharya, Rahul Goel, Angeliki Metallinou, Inderjit Dhillon
Date: 2018-11-01 00:00:00
Tasks: General Classification, Quantization, Sentence, Sentence Classification, text-classification, Text Classification

Deep learning models have become state of the art for natural language
processing (NLP) tasks, however deploying these models in production system
poses significant memory constraints. Existing compression methods are either
lossy or introduce significant latency. We propose a compression method that
leverages low rank matrix factorization during training,to compress the word
embedding layer which represents the size bottleneck for most NLP models. Our
models are trained, compressed and then further re-trained on the downstream
task to recover accuracy while maintaining the reduced size. Empirically, we
show that the proposed method can achieve 90% compression with minimal impact
in accuracy for sentence classification tasks, and outperforms alternative
methods like fixed-point quantization or offline word embedding compression. We
also analyze the inference time and storage space for our method through FLOP
calculations, showing that we can compress DNN models by a configurable ratio
and regain accuracy loss without introducing additional latency compared to
fixed point quantization. Finally, we introduce a novel learning rate schedule,
the Cyclically Annealed Learning Rate (CALR), which we empirically demonstrate
to outperform other popular adaptive learning rate algorithms on a sentence
classification benchmark.
Stochastic Normalizations as Bayesian Learning
Authors: Alexander Shekhovtsov, Boris Flach
Date: 2018-11-01 00:00:00

In this work we investigate the reasons why Batch Normalization (BN) improves
the generalization performance of deep networks. We argue that one major
reason, distinguishing it from data-independent normalization methods, is
randomness of batch statistics. This randomness appears in the parameters
rather than in activations and admits an interpretation as a practical Bayesian
learning. We apply this idea to other (deterministic) normalization techniques
that are oblivious to the batch size. We show that their generalization
performance can be improved significantly by Bayesian learning of the same
form. We obtain test performance comparable to BN and, at the same time, better
validation losses suitable for subsequent output uncertainty estimation through
approximate Bayesian posterior.
Spectral Signatures in Backdoor Attacks
Authors: Brandon Tran, Jerry Li, Aleksander Madry
Date: 2018-11-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Data Poisoning

A recent line of work has uncovered a new form of data poisoning: so-called
\emph{backdoor} attacks. These attacks are particularly dangerous because they
do not affect a network's behavior on typical, benign data. Rather, the network
only deviates from its expected output when triggered by a perturbation planted
by an adversary.
  In this paper, we identify a new property of all known backdoor attacks,
which we call \emph{spectral signatures}. This property allows us to utilize
tools from robust statistics to thwart the attacks. We demonstrate the efficacy
of these signatures in detecting and removing poisoned examples on real image
sets and state of the art neural network architectures. We believe that
understanding spectral signatures is a crucial first step towards designing ML
systems secure against such backdoor attacks
Embedding Individual Table Columns for Resilient SQL Chatbots
Authors: Bojan Petrovski, Ignacio Aguado, Andreea Hossmann, Michael Baeriswyl, Claudiu Musat
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 10
Tasks: Sql Chatbots

Most of the world's data is stored in relational databases. Accessing these
requires specialized knowledge of the Structured Query Language (SQL), putting
them out of the reach of many people. A recent research thread in Natural
Language Processing (NLP) aims to alleviate this problem by automatically
translating natural language questions into SQL queries. While the proposed
solutions are a great start, they lack robustness and do not easily generalize:
the methods require high quality descriptions of the database table columns,
and the most widely used training dataset, WikiSQL, is heavily biased towards
using those descriptions as part of the questions.
  In this work, we propose solutions to both problems: we entirely eliminate
the need for column descriptions, by relying solely on their contents, and we
augment the WikiSQL dataset by paraphrasing column names to reduce bias. We
show that the accuracy of existing methods drops when trained on our augmented,
column-agnostic dataset, and that our own method reaches state of the art
accuracy, while relying on column contents only.
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning
Authors: Zois Boukouvalas, Daniel C. Elton, Peter W. Chung, Mark D. Fuge
Date: 2018-11-01 00:00:00
Tasks: BIG-bench Machine Learning, Drug Discovery, feature selection, Molecular Property Prediction, Prediction, Property Prediction

Due to its high computational speed and accuracy compared to ab-initio
quantum chemistry and forcefield modeling, the prediction of molecular
properties using machine learning has received great attention in the fields of
materials design and drug discovery. A main ingredient required for machine
learning is a training dataset consisting of molecular features\textemdash for
example fingerprint bits, chemical descriptors, etc. that adequately
characterize the corresponding molecules. However, choosing features for any
application is highly non-trivial. No "universal" method for feature selection
exists. In this work, we propose a data fusion framework that uses Independent
Vector Analysis to exploit underlying complementary information contained in
different molecular featurization methods, bringing us a step closer to
automated feature generation. Our approach takes an arbitrary number of
individual feature vectors and automatically generates a single, compact (low
dimensional) set of molecular features that can be used to enhance the
prediction performance of regression models. At the same time our methodology
retains the possibility of interpreting the generated features to discover
relationships between molecular structures and properties. We demonstrate this
on the QM7b dataset for the prediction of several properties such as
atomization energy, polarizability, frontier orbital eigenvalues, ionization
potential, electron affinity, and excitation energies. In addition, we show how
our method helps improve the prediction of experimental binding affinities for
a set of human BACE-1 inhibitors. To encourage more widespread use of IVA we
have developed the PyIVA Python package, an open source code which is available
for download on Github.
Incorporating Structured Commonsense Knowledge in Story Completion
Authors: Jiaao Chen, Jianshu Chen, Zhou Yu
Date: 2018-11-01 00:00:00
Tasks: Story Completion

The ability to select an appropriate story ending is the first step towards
perfect narrative comprehension. Story ending prediction requires not only the
explicit clues within the context, but also the implicit knowledge (such as
commonsense) to construct a reasonable and consistent story. However, most
previous approaches do not explicitly use background commonsense knowledge. We
present a neural story ending selection model that integrates three types of
information: narrative sequence, sentiment evolution and commonsense knowledge.
Experiments show that our model outperforms state-of-the-art approaches on a
public dataset, ROCStory Cloze Task , and the performance gain from adding the
additional commonsense knowledge is significant.
Improving Adversarial Robustness by Encouraging Discriminative Features
Authors: Chirag Agarwal, Anh Nguyen, Dan Schonfeld
Date: 2018-11-01 00:00:00
Tasks: Adversarial Robustness

Deep neural networks (DNNs) have achieved state-of-the-art results in various pattern recognition tasks. However, they perform poorly on out-of-distribution adversarial examples i.e. inputs that are specifically crafted by an adversary to cause DNNs to misbehave, questioning the security and reliability of applications. In this paper, we encourage DNN classifiers to learn more discriminative features by imposing a center loss in addition to the regular softmax cross-entropy loss. Intuitively, the center loss encourages DNNs to simultaneously learns a center for the deep features of each class, and minimize the distances between the intra-class deep features and their corresponding class centers. We hypothesize that minimizing distances between intra-class features and maximizing the distances between inter-class features at the same time would improve a classifier's robustness to adversarial examples. Our results on state-of-the-art architectures on MNIST, CIFAR-10, and CIFAR-100 confirmed that intuition and highlight the importance of discriminative features.
Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction
Authors: Hongyuan Zhan, Gabriel Gomes, Xiaoye S. Li, Kamesh Madduri, Kesheng Wu
Date: 2018-11-01 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency, Hyperparameter Optimization, Prediction, regression, Time Series, Time Series Analysis, Time Series Prediction, Traffic Prediction

Computational efficiency is an important consideration for deploying machine
learning models for time series prediction in an online setting. Machine
learning algorithms adjust model parameters automatically based on the data,
but often require users to set additional parameters, known as hyperparameters.
Hyperparameters can significantly impact prediction accuracy. Traffic
measurements, typically collected online by sensors, are serially correlated.
Moreover, the data distribution may change gradually. A typical adaptation
strategy is periodically re-tuning the model hyperparameters, at the cost of
computational burden. In this work, we present an efficient and principled
online hyperparameter optimization algorithm for Kernel Ridge regression
applied to traffic prediction problems. In tests with real traffic measurement
data, our approach requires as little as one-seventh of the computation time of
other tuning methods, while achieving better or similar prediction accuracy.
Exploring Semantic Incrementality with Dynamic Syntax and Vector Space Semantics
Authors: Mehrnoosh Sadrzadeh, Matthew Purver, Julian Hough, Ruth Kempson
Date: 2018-11-01 00:00:00

One of the fundamental requirements for models of semantic processing in
dialogue is incrementality: a model must reflect how people interpret and
generate language at least on a word-by-word basis, and handle phenomena such
as fragments, incomplete and jointly-produced utterances. We show that the
incremental word-by-word parsing process of Dynamic Syntax (DS) can be assigned
a compositional distributional semantics, with the composition operator of DS
corresponding to the general operation of tensor contraction from multilinear
algebra. We provide abstract semantic decorations for the nodes of DS trees, in
terms of vectors, tensors, and sums thereof; using the latter to model the
underspecified elements crucial to assigning partial representations during
incremental processing. As a working example, we give an instantiation of this
theory using plausibility tensors of compositional distributional semantics,
and show how our framework can incrementally assign a semantic plausibility
measure as it parses phrases and sentences.
Shifting the Baseline: Single Modality Performance on Visual Navigation & QA
Authors: Jesse Thomason, Daniel Gordon, Yonatan Bisk
Date: 2018-11-01 00:00:00
Tasks: Question Answering, Visual Navigation

We demonstrate the surprising strength of unimodal baselines in multimodal
domains, and make concrete recommendations for best practices in future
research. Where existing work often compares against random or majority class
baselines, we argue that unimodal approaches better capture and reflect dataset
biases and therefore provide an important comparison when assessing the
performance of multimodal techniques. We present unimodal ablations on three
recent datasets in visual navigation and QA, seeing an up to 29% absolute gain
in performance over published baselines.
DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval
Authors: Zhiwen Tang, Grace Hui Yang
Date: 2018-11-01 00:00:00
Tasks: Ad-Hoc Information Retrieval, Document Ranking, Information Retrieval, Retrieval, Text Retrieval

Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching. Inspired by TileBars, a classical term distribution visualization method, in this paper, we propose a novel Neu-IR model that handles query-to-document matching at the subtopic and higher levels. Our system first splits the documents into topical segments, "visualizes" the matchings between the query and the segments, and then feeds an interaction matrix into a Neu-IR model, DeepTileBars, to obtain the final ranking scores. DeepTileBars models the relevance signals occurring at different granularities in a document's topic hierarchy. It better captures the discourse structure of a document and thus the matching patterns. Although its design and implementation are light-weight, DeepTileBars outperforms other state-of-the-art Neu-IR models on benchmark datasets including the Text REtrieval Conference (TREC) 2010-2012 Web Tracks and LETOR 4.0.