
Explainable Deep Learning: A Field Guide for the Uninitiated
Deep neural network (DNN) is an indispensable machine learning tool for ...
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Adversarial Examples on Object Recognition: A Comprehensive Survey
Deep neural networks are at the forefront of machine learning research. ...
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A Differentiable Color Filter for Generating Unrestricted Adversarial Images
We propose Adversarial Color Filtering (AdvCF), an approach that uses a ...
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Causal Transfer Learning
An important goal in both transfer learning and causal inference is to m...
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Predicting and visualizing psychological attributions with a deep neural network
Judgments about personality based on facial appearance are strong effect...
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The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables
This paper introduces the kernel mixture network, a new method for nonpa...
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GP CaKe: Effective brain connectivity with causal kernels
A fundamental goal in network neuroscience is to understand how activity...
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Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks
Managing patients with multimorbidity often results in polypharmacy: the...
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Joint Causal Inference from Observational and Experimental Datasets
We introduce Joint Causal Inference (JCI), a powerful formulation of cau...
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Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
Astrophysics and cosmology are rich with data. The advent of widearea d...
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Weighted Positive Binary Decision Diagrams for Exact Probabilistic Inference
Recent work on weighted model counting has been very successfully applie...
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Estimating Nonlinear Dynamics with the ConvNet Smoother
Estimating the state of a dynamical system from a series of noisecorrup...
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Ancestral Causal Inference
Constraintbased causal discovery from limited data is a notoriously dif...
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Analysis of Nonstationary Time Series Using Locally Coupled Gaussian Processes
The analysis of nonstationary time series is of great importance in many...
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Dynamic Decomposition of Spatiotemporal Neural Signals
Neural signals are characterized by rich temporal and spatiotemporal dyn...
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The "Sprekend Nederland" project and its application to accent location
This paper describes the data collection effort that is part of the proj...
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Local Network Community Detection with Continuous Optimization of Conductance and Weighted Kernel KMeans
Local network community detection is the task of finding a single commun...
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Deep disentangled representations for volumetric reconstruction
We introduce a convolutional neural network for inferring a compact dise...
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Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition
Here, we develop an audiovisual deep residual network for multimodal app...
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Causality on CrossSectional Data: Stable Specification Search in Constrained Structural Equation Modeling
Causal modeling has long been an attractive topic for many researchers a...
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Axioms for graph clustering quality functions
We investigate properties that intuitively ought to be satisfied by grap...
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Sparse Approximate Inference for SpatioTemporal Point Process Models
Spatiotemporal point process models play a central role in the analysis...
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Dynamic Policy Programming
In this paper, we propose a novel policy iteration method, called dynami...
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Entailment Relations on Distributions
In this paper we give an overview of partial orders on the space of prob...
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Sidechannel based intrusion detection for industrial control systems
Industrial Control Systems are under increased scrutiny. Their security ...
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Distances between States and between Predicates
This paper gives a systematic account of various metrics on probability ...
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Increased security through open source
In this paper we discuss the impact of open source on both the security ...
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Gender and Emotion Recognition with Implicit User Signals
We examine the utility of implicit user behavioral signals captured usin...
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Distributive Laws for Monotone Specifications
Turi and Plotkin introduced an elegant approach to structural operationa...
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Normalized Information Distance and the Oscillation Hierarchy
We study the complexity of approximations to the normalized information ...
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Test Martingales for bounded random variables
Test martingales have been proposed as a more intuitive approach to hypo...
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Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos
Explainability and interpretability are two critical aspects of decision...
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Separation choosability and dense bipartite induced subgraphs
We study a restricted form of list colouring, for which every pair of li...
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Linguistic unit discovery from multimodal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop
We summarize the accomplishments of a multidisciplinary workshop explor...
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HumanintheLoop Synthesis for Partially Observable Markov Decision Processes
We study planning problems where autonomous agents operate inside enviro...
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Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
Issues regarding explainable AI involve four components: users, laws & r...
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Neural Nets via Forward State Transformation and Backward Loss Transformation
This article studies (multilayer perceptron) neural networks with an emp...
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Simple Domain Adaptation with Class Prediction Uncertainty Alignment
Unsupervised domain adaptation tries to adapt a classifier trained on a ...
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A Channelbased Exact Inference Algorithm for Bayesian Networks
This paper describes a new algorithm for exact Bayesian inference that i...
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The unified higherorder dependency pair framework
In recent years, two higherorder extensions of the powerful dependency ...
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Forward Amortized Inference for LikelihoodFree Variational Marginalization
In this paper, we introduce a new form of amortized variational inferenc...
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Wasserstein Variational Inference
This paper introduces Wasserstein variational inference, a new form of a...
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Normative Modeling of Neuroimaging Data using Scalable MultiTask Gaussian Processes
Normative modeling has recently been proposed as an alternative for the ...
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A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values
We consider the problem of learning parameters of latent variable models...
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A Mathematical Account of Soft Evidence, and of Jeffrey's `destructive' versus Pearl's `constructive' updating
Evidence in probabilistic reasoning may be `hard' or `soft', that is, it...
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From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene Recognition
As deep learning approaches to scene recognition emerge, they have conti...
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Building a Unified CodeSwitching ASR System for South African Languages
We present our first efforts towards building a single multilingual auto...
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Articulatory Features for ASR of Pathological Speech
In this work, we investigate the joint use of articulatory and acoustic ...
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Acoustic and Textual Data Augmentation for Improved ASR of CodeSwitching Speech
In this paper, we describe several techniques for improving the acoustic...
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CodeSwitching Detection with DataAugmented Acoustic and Language Models
In this paper, we investigate the codeswitching detection performance o...
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Radboud Universiteit
Radboud University Nijmegen is a public university with a strong focus on research located in Nijmegen, the Netherlands.