pwc-archive/papers-with-abstracts huggingface dataset:
Active Learning for Network Intrusion Detection
Authors: Amir Ziai
Date: 2019-04-02 00:00:00
Tasks: Active Learning, Anomaly Detection, Intrusion Detection, Network Intrusion Detection

Network operators are generally aware of common attack vectors that they
defend against. For most networks the vast majority of traffic is legitimate.
However new attack vectors are continually designed and attempted by bad actors
which bypass detection and go unnoticed due to low volume. One strategy for
finding such activity is to look for anomalous behavior. Investigating
anomalous behavior requires significant time and resources. Collecting a large
number of labeled examples for training supervised models is both prohibitively
expensive and subject to obsoletion as new attacks surface. A purely
unsupervised methodology is ideal; however, research has shown that even a very
small number of labeled examples can significantly improve the quality of
anomaly detection. A methodology that minimizes the number of required labels
while maximizing the quality of detection is desirable. False positives in this
context result in wasted effort or blockage of legitimate traffic and false
negatives translate to undetected attacks. We propose a general active learning
framework and experiment with different choices of learners and sampling
strategies.
Learning Algorithms via Neural Logic Networks
Authors: Ali Payani, Faramarz Fekri
Date: 2019-04-02 00:00:00
Tasks: Inductive logic programming

We propose a novel learning paradigm for Deep Neural Networks (DNN) by using
Boolean logic algebra. We first present the basic differentiable operators of a
Boolean system such as conjunction, disjunction and exclusive-OR and show how
these elementary operators can be combined in a simple and meaningful way to
form Neural Logic Networks (NLNs). We examine the effectiveness of the proposed
NLN framework in learning Boolean functions and discrete-algorithmic tasks. We
demonstrate that, in contrast to the implicit learning in MLP approach, the
proposed neural logic networks can learn the logical functions explicitly that
can be verified and interpreted by human. In particular, we propose a new
framework for learning the inductive logic programming (ILP) problems by
exploiting the explicit representational power of NLN. We show the proposed
neural ILP solver is capable of feats such as predicate invention and recursion
and can outperform the current state of the art neural ILP solvers using a
variety of benchmark tasks such as decimal addition and multiplication, and
sorting on ordered list.
Understanding language-elicited EEG data by predicting it from a fine-tuned language model
Authors: Dan Schwartz, Tom Mitchell
Date: 2019-04-02 00:00:00
Proceeding: NAACL 2019 6
Tasks: EEG, Electroencephalogram (EEG), ERP, Language Modeling, Language Modelling

Electroencephalography (EEG) recordings of brain activity taken while
participants read or listen to language are widely used within the cognitive
neuroscience and psycholinguistics communities as a tool to study language
comprehension. Several time-locked stereotyped EEG responses to
word-presentations -- known collectively as event-related potentials (ERPs) --
are thought to be markers for semantic or syntactic processes that take place
during comprehension. However, the characterization of each individual ERP in
terms of what features of a stream of language trigger the response remains
controversial. Improving this characterization would make ERPs a more useful
tool for studying language comprehension. We take a step towards better
understanding the ERPs by fine-tuning a language model to predict them. This
new approach to analysis shows for the first time that all of the ERPs are
predictable from embeddings of a stream of language. Prior work has only found
two of the ERPs to be predictable. In addition to this analysis, we examine
which ERPs benefit from sharing parameters during joint training. We find that
two pairs of ERPs previously identified in the literature as being related to
each other benefit from joint training, while several other pairs of ERPs that
benefit from joint training are suggestive of potential relationships.
Extensions of this analysis that further examine what kinds of information in
the model embeddings relate to each ERP have the potential to elucidate the
processes involved in human language comprehension.
Augmented Utilitarianism for AGI Safety
Authors: Nadisha-Marie Aliman, Leon Kester
Date: 2019-04-02 00:00:00

In the light of ongoing progresses of research on artificial intelligent
systems exhibiting a steadily increasing problem-solving ability, the
identification of practicable solutions to the value alignment problem in AGI
Safety is becoming a matter of urgency. In this context, one preeminent
challenge that has been addressed by multiple researchers is the adequate
formulation of utility functions or equivalents reliably capturing human
ethical conceptions. However, the specification of suitable utility functions
harbors the risk of "perverse instantiation" for which no final consensus on
responsible proactive countermeasures has been achieved so far. Amidst this
background, we propose a novel socio-technological ethical framework denoted
Augmented Utilitarianism which directly alleviates the perverse instantiation
problem. We elaborate on how augmented by AI and more generally science and
technology, it might allow a society to craft and update ethical utility
functions while jointly undergoing a dynamical ethical enhancement. Further, we
elucidate the need to consider embodied simulations in the design of utility
functions for AGIs aligned with human values. Finally, we discuss future
prospects regarding the usage of the presented scientifically grounded ethical
framework and mention possible challenges.
Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset
Authors: Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, Rynson Lau
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Rain Removal, Single Image Deraining, Vocal Bursts Intensity Prediction

Removing rain streaks from a single image has been drawing considerable attention as rain streaks can severely degrade the image quality and affect the performance of existing outdoor vision tasks. While recent CNN-based derainers have reported promising performances, deraining remains an open problem for two reasons. First, existing synthesized rain datasets have only limited realism, in terms of modeling real rain characteristics such as rain shape, direction and intensity. Second, there are no public benchmarks for quantitative comparisons on real rain images, which makes the current evaluation less objective. The core challenge is that real world rain/clean image pairs cannot be captured at the same time. In this paper, we address the single image rain removal problem in two ways. First, we propose a semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images. Using this method, we construct a large-scale dataset of $\sim$$29.5K$ rain/rain-free image pairs that covers a wide range of natural rain scenes. Second, to better cover the stochastic distribution of real rain streaks, we propose a novel SPatial Attentive Network (SPANet) to remove rain streaks in a local-to-global manner. Extensive experiments demonstrate that our network performs favorably against the state-of-the-art deraining methods.
Speech denoising by parametric resynthesis
Authors: Soumi Maiti, Michael I Mandel
Date: 2019-04-02 00:00:00
Tasks: Denoising, Resynthesis, Speech Denoising, Speech Enhancement, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

This work proposes the use of clean speech vocoder parameters as the target
for a neural network performing speech enhancement. These parameters have been
designed for text-to-speech synthesis so that they both produce high-quality
resyntheses and also are straightforward to model with neural networks, but
have not been utilized in speech enhancement until now. In comparison to a
matched text-to-speech system that is given the ground truth transcripts of the
noisy speech, our model is able to produce more natural speech because it has
access to the true prosody in the noisy speech. In comparison to two denoising
systems, the oracle Wiener mask and a DNN-based mask predictor, our model
equals the oracle Wiener mask in subjective quality and intelligibility and
surpasses the realistic system. A vocoder-based upper bound shows that there is
still room for improvement with this approach beyond the oracle Wiener mask. We
test speaker-dependence with two speakers and show that a single model can be
used for multiple speakers.
Convergence rates for the stochastic gradient descent method for non-convex objective functions
Authors: Benjamin Fehrman, Benjamin Gess, Arnulf Jentzen
Date: 2019-04-02 00:00:00
Tasks: BIG-bench Machine Learning

We prove the local convergence to minima and estimates on the rate of convergence for the stochastic gradient descent method in the case of not necessarily globally convex nor contracting objective functions. In particular, the results are applicable to simple objective functions arising in machine learning.
Data driven approximation of parametrized PDEs by Reduced Basis and Neural Networks
Authors: Niccolò Dal Santo, Simone Deparis, Luca Pegolotti
Date: 2019-04-02 00:00:00
Tasks: Decoder, Network Embedding

We are interested in the approximation of partial differential equations with a data-driven approach based on the reduced basis method and machine learning. We suppose that the phenomenon of interest can be modeled by a parametrized partial differential equation, but that the value of the physical parameters is unknown or difficult to be directly measured. Our method allows to estimate fields of interest, for instance temperature of a sample of material or velocity of a fluid, given data at a handful of points in the domain. We propose to accomplish this task with a neural network embedding a reduced basis solver as exotic activation function in the last layer. The reduced basis solver accounts for the underlying physical phenomenonon and it is constructed from snapshots obtained from randomly selected values of the physical parameters during an expensive offline phase. The same full order solutions are then employed for the training of the neural network. As a matter of fact, the chosen architecture resembles an asymmetric autoencoder in which the decoder is the reduced basis solver and as such it does not contain trainable parameters. The resulting latent space of our autoencoder includes parameter-dependent quantities feeding the reduced basis solver, which -- depending on the considered partial differential equation -- are the values of the physical parameters themselves or the affine decomposition coefficients of the differential operators.
FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D Facial Animation
Authors: Yanfu Yan, Ke Lu, Jian Xue, Pengcheng Gao, Jiayi Lyu
Date: 2019-04-02 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction, regression

Facial expression analysis based on machine learning requires large number of
well-annotated data to reflect different changes in facial motion. Publicly
available datasets truly help to accelerate research in this area by providing
a benchmark resource, but all of these datasets, to the best of our knowledge,
are limited to rough annotations for action units, including only their
absence, presence, or a five-level intensity according to the Facial Action
Coding System. To meet the need for videos labeled in great detail, we present
a well-annotated dataset named FEAFA for Facial Expression Analysis and 3D
Facial Animation. One hundred and twenty-two participants, including children,
young adults and elderly people, were recorded in real-world conditions. In
addition, 99,356 frames were manually labeled using Expression Quantitative
Tool developed by us to quantify 9 symmetrical FACS action units, 10
asymmetrical (unilateral) FACS action units, 2 symmetrical FACS action
descriptors and 2 asymmetrical FACS action descriptors, and each action unit or
action descriptor is well-annotated with a floating point number between 0 and
1. To provide a baseline for use in future research, a benchmark for the
regression of action unit values based on Convolutional Neural Networks are
presented. We also demonstrate the potential of our FEAFA dataset for 3D facial
animation. Almost all state-of-the-art algorithms for facial animation are
achieved based on 3D face reconstruction. We hence propose a novel method that
drives virtual characters only based on action unit value regression of the 2D
video frames of source actors.
Guided Super-Resolution as Pixel-to-Pixel Transformation
Authors: Riccardo de Lutio, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler
Date: 2019-04-02 00:00:00
Proceeding: ICCV 2019 10
Tasks: Super-Resolution

Guided super-resolution is a unifying framework for several computer vision tasks where the inputs are a low-resolution source image of some target quantity (e.g., perspective depth acquired with a time-of-flight camera) and a high-resolution guide image from a different domain (e.g., a grey-scale image from a conventional camera); and the target output is a high-resolution version of the source (in our example, a high-res depth map). The standard way of looking at this problem is to formulate it as a super-resolution task, i.e., the source image is upsampled to the target resolution, while transferring the missing high-frequency details from the guide. Here, we propose to turn that interpretation on its head and instead see it as a pixel-to-pixel mapping of the guide image to the domain of the source image. The pixel-wise mapping is parametrised as a multi-layer perceptron, whose weights are learned by minimising the discrepancies between the source image and the downsampled target image. Importantly, our formulation makes it possible to regularise only the mapping function, while avoiding regularisation of the outputs; thus producing crisp, natural-looking images. The proposed method is unsupervised, using only the specific source and guide images to fit the mapping. We evaluate our method on two different tasks, super-resolution of depth maps and of tree height maps. In both cases, we clearly outperform recent baselines in quantitative comparisons, while delivering visually much sharper outputs.
Neural Vector Conceptualization for Word Vector Space Interpretation
Authors: Robert Schwarzenberg, Lisa Raithel, David Harbecke
Date: 2019-04-02 00:00:00
Proceeding: WS 2019 6

Distributed word vector spaces are considered hard to interpret which hinders
the understanding of natural language processing (NLP) models. In this work, we
introduce a new method to interpret arbitrary samples from a word vector space.
To this end, we train a neural model to conceptualize word vectors, which means
that it activates higher order concepts it recognizes in a given vector.
Contrary to prior approaches, our model operates in the original vector space
and is capable of learning non-linear relations between word vectors and
concepts. Furthermore, we show that it produces considerably less entropic
concept activation profiles than the popular cosine similarity.
Synthetic learner: model-free inference on treatments over time
Authors: Davide Viviano, Jelena Bradic
Date: 2019-04-02 00:00:00
Tasks: counterfactual, Marketing, valid

Understanding the effect of a particular treatment or a policy pertains to many areas of interest, ranging from political economics, marketing to healthcare. In this paper, we develop a non-parametric algorithm for detecting the effects of treatment over time in the context of Synthetic Controls. The method builds on counterfactual predictions from many algorithms without necessarily assuming that the algorithms correctly capture the model. We introduce an inferential procedure for detecting treatment effects and show that the testing procedure is asymptotically valid for stationary, beta mixing processes without imposing any restriction on the set of base algorithms under consideration. We discuss consistency guarantees for average treatment effect estimates and derive regret bounds for the proposed methodology. The class of algorithms may include Random Forest, Lasso, or any other machine-learning estimator. Numerical studies and an application illustrate the advantages of the method.
Unveiling phase transitions with machine learning
Authors: Askery Canabarro, Felipe Fernandes Fanchini, André Luiz Malvezzi, Rodrigo Pereira, Rafael Chaves
Date: 2019-04-02 00:00:00
Tasks: BIG-bench Machine Learning, Transfer Learning

The classification of phase transitions is a central and challenging task in
condensed matter physics. Typically, it relies on the identification of order
parameters and the analysis of singularities in the free energy and its
derivatives. Here, we propose an alternative framework to identify quantum
phase transitions, employing both unsupervised and supervised machine learning
techniques. Using the axial next-nearest neighbor Ising (ANNNI) model as a
benchmark, we show how unsupervised learning can detect three phases
(ferromagnetic, paramagnetic, and a cluster of the antiphase with the floating
phase) as well as two distinct regions within the paramagnetic phase. Employing
supervised learning we show that transfer learning becomes possible: a machine
trained only with nearest-neighbour interactions can learn to identify a new
type of phase occurring when next-nearest-neighbour interactions are
introduced. All our results rely on few and low dimensional input data (up to
twelve lattice sites), thus providing a computational friendly and general
framework for the study of phase transitions in many-body systems.
Are Query-Based Ontology Debuggers Really Helping Knowledge Engineers?
Authors: Patrick Rodler, Dietmar Jannach, Konstantin Schekotihin, Philipp Fleiss
Date: 2019-04-02 00:00:00

Real-world semantic or knowledge-based systems, e.g., in the biomedical domain, can become large and complex. Tool support for the localization and repair of faults within knowledge bases of such systems can therefore be essential for their practical success. Correspondingly, a number of knowledge base debugging approaches, in particular for ontology-based systems, were proposed throughout recent years. Query-based debugging is a comparably recent interactive approach that localizes the true cause of an observed problem by asking knowledge engineers a series of questions. Concrete implementations of this approach exist, such as the OntoDebug plug-in for the ontology editor Prot\'eg\'e. To validate that a newly proposed method is favorable over an existing one, researchers often rely on simulation-based comparisons. Such an evaluation approach however has certain limitations and often cannot fully inform us about a method's true usefulness. We therefore conducted different user studies to assess the practical value of query-based ontology debugging. One main insight from the studies is that the considered interactive approach is indeed more efficient than an alternative algorithmic debugging based on test cases. We also observed that users frequently made errors in the process, which highlights the importance of a careful design of the queries that users need to answer.
Semantics Disentangling for Text-to-Image Generation
Authors: Guojun Yin, Bin Liu, Lu Sheng, Nenghai Yu, Xiaogang Wang, Jing Shao
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Diversity, Image Generation, Text to Image Generation, Text-to-Image Generation

Synthesizing photo-realistic images from text descriptions is a challenging
problem. Previous studies have shown remarkable progresses on visual quality of
the generated images. In this paper, we consider semantics from the input text
descriptions in helping render photo-realistic images. However, diverse
linguistic expressions pose challenges in extracting consistent semantics even
they depict the same thing. To this end, we propose a novel photo-realistic
text-to-image generation model that implicitly disentangles semantics to both
fulfill the high-level semantic consistency and low-level semantic diversity.
To be specific, we design (1) a Siamese mechanism in the discriminator to learn
consistent high-level semantics, and (2) a visual-semantic embedding strategy
by semantic-conditioned batch normalization to find diverse low-level
semantics. Extensive experiments and ablation studies on CUB and MS-COCO
datasets demonstrate the superiority of the proposed method in comparison to
state-of-the-art methods.
Good News, Everyone! Context driven entity-aware captioning for news images
Authors: Ali Furkan Biten, Lluis Gomez, Marçal Rusiñol, Dimosthenis Karatzas
Date: 2019-04-02 00:00:00
Proceeding: CVPR 2019 6
Tasks: Articles, Descriptive, Image Captioning

Current image captioning systems perform at a merely descriptive level,
essentially enumerating the objects in the scene and their relations. Humans,
on the contrary, interpret images by integrating several sources of prior
knowledge of the world. In this work, we aim to take a step closer to producing
captions that offer a plausible interpretation of the scene, by integrating
such contextual information into the captioning pipeline. For this we focus on
the captioning of images used to illustrate news articles. We propose a novel
captioning method that is able to leverage contextual information provided by
the text of news articles associated with an image. Our model is able to
selectively draw information from the article guided by visual cues, and to
dynamically extend the output dictionary to out-of-vocabulary named entities
that appear in the context source. Furthermore we introduce `GoodNews', the
largest news image captioning dataset in the literature and demonstrate
state-of-the-art results.
BCMA-ES II: revisiting Bayesian CMA-ES
Authors: Eric Benhamou, David Saltiel, Beatrice Guez, Nicolas Paris
Date: 2019-04-02 00:00:00

This paper revisits the Bayesian CMA-ES and provides updates for normal
Wishart. It emphasizes the difference between a normal and normal inverse
Wishart prior. After some computation, we prove that the only difference relies
surprisingly in the expected covariance. We prove that the expected covariance
should be lower in the normal Wishart prior model because of the convexity of
the inverse. We present a mixture model that generalizes both normal Wishart
and normal inverse Wishart model. We finally present various numerical
experiments to compare both methods as well as the generalized method.
Training Data Augmentation for Context-Sensitive Neural Lemmatization Using Inflection Tables and Raw Text
Authors: Toms Bergmanis, Sharon Goldwater
Date: 2019-04-02 00:00:00
Tasks: Data Augmentation, LEMMA, Lemmatization, Sentence

Lemmatization aims to reduce the sparse data problem by relating the inflected forms of a word to its dictionary form. Using context can help, both for unseen and ambiguous words. Yet most context-sensitive approaches require full lemma-annotated sentences for training, which may be scarce or unavailable in low-resource languages. In addition (as shown here), in a low-resource setting, a lemmatizer can learn more from $n$ labeled examples of distinct words (types) than from $n$ (contiguous) labeled tokens, since the latter contain far fewer distinct types. To combine the efficiency of type-based learning with the benefits of context, we propose a way to train a context-sensitive lemmatizer with little or no labeled corpus data, using inflection tables from the UniMorph project and raw text examples from Wikipedia that provide sentence contexts for the unambiguous UniMorph examples. Despite these being unambiguous examples, the model successfully generalizes from them, leading to improved results (both overall, and especially on unseen words) in comparison to a baseline that does not use context.