Centrum Wiskunde & Informatica

∙ Amsterdam , Netherlands ∙ 0 followers

The Centrum Wiskunde & Informatica is a research center in the field of mathematics and theoretical computer science.

Surrogate-free machine learning-based organ dose reconstruction for pediatric abdominal radiotherapy

To study radiotherapy-related adverse effects, detailed dose information...
02/17/2020 ∙ by M. Virgolin, et al. ∙ 36 ∙ share

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A Biologically Plausible Learning Rule for Deep Learning in the Brain

Researchers have proposed that deep learning, which is providing importa...
11/05/2018 ∙ by Isabella Pozzi, et al. ∙ 30 ∙ share

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Robust temporal difference learning for critical domains

We present a new Q-function operator for temporal difference (TD) learni...
01/23/2019 ∙ by Richard Klima, et al. ∙ 20 ∙ share

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Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks

The emergence of brain-inspired neuromorphic computing as a paradigm for...
05/24/2020 ∙ by Bojian Yin, et al. ∙ 20 ∙ share

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Observer variation-aware medical image segmentation by combining deep learning and surrogate-assisted genetic algorithms

There has recently been great progress in automatic segmentation of medi...
01/23/2020 ∙ by Arkadiy Dushatskiy, et al. ∙ 18 ∙ share

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Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms

Machine Learning (ML) is proving extremely beneficial in many healthcare...
09/09/2019 ∙ by Marco Virgolin, et al. ∙ 6 ∙ share

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A Cone-Beam X-Ray CT Data Collection Designed for Machine Learning

Unlike previous works, this open data collection consists of X-ray cone-...
05/12/2019 ∙ by Henri Der Sarkissian, et al. ∙ 4 ∙ share

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Prediction Scores as a Window into Classifier Behavior

Most multi-class classifiers make their prediction for a test sample by ...
11/18/2017 ∙ by Medha Katehara, et al. ∙ 0 ∙ share

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A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity

We present a novel notion of complexity that interpolates between and ge...
10/21/2017 ∙ by Peter D. Grünwald, et al. ∙ 0 ∙ share

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Identification of Probabilities

Within psychology, neuroscience and artificial intelligence, there has b...
08/04/2017 ∙ by Paul M. B. Vitanyi, et al. ∙ 0 ∙ share

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Common Knowledge in a Logic of Gossips

Gossip protocols aim at arriving, by means of point-to-point or group co...
07/27/2017 ∙ by Krzysztof R. Apt, et al. ∙ 0 ∙ share

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Lenient Multi-Agent Deep Reinforcement Learning

A significant amount of research in recent years has been dedicated towa...
07/14/2017 ∙ by Gregory Palmer, et al. ∙ 0 ∙ share

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Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting

When a Convolutional Neural Network is used for on-the-fly evaluation of...
03/11/2016 ∙ by Koen Groenland, et al. ∙ 0 ∙ share

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Conditional Time Series Forecasting with Convolutional Neural Networks

We present a method for conditional time series forecasting based on the...
03/14/2017 ∙ by Anastasia Borovykh, et al. ∙ 0 ∙ share

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Can we reach Pareto optimal outcomes using bottom-up approaches?

Traditionally, researchers in decision making have focused on attempting...
07/03/2016 ∙ by Victor Sanchez-Anguix, et al. ∙ 0 ∙ share

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Technical Report: Towards a Universal Code Formatter through Machine Learning

There are many declarative frameworks that allow us to implement code fo...
06/28/2016 ∙ by Terence Parr, et al. ∙ 0 ∙ share

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Translucent Players: Explaining Cooperative Behavior in Social Dilemmas

In the last few decades, numerous experiments have shown that humans do ...
06/24/2016 ∙ by Valerio Capraro, et al. ∙ 0 ∙ share

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Epistemic Protocols for Distributed Gossiping

Gossip protocols aim at arriving, by means of point-to-point or group co...
06/24/2016 ∙ by Krzysztof R. Apt, et al. ∙ 0 ∙ share

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Fractionally Predictive Spiking Neurons

Recent experimental work has suggested that the neural firing rate can b...
10/29/2010 ∙ by Sander M. Bohte, et al. ∙ 0 ∙ share

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A Discipline of Evolutionary Programming

Genetic fitness optimization using small populations or small population...
02/02/1999 ∙ by Paul Vitanyi, et al. ∙ 0 ∙ share

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CompAdaGrad: A Compressed, Complementary, Computationally-Efficient Adaptive Gradient Method

The adaptive gradient online learning method known as AdaGrad has seen w...
09/12/2016 ∙ by Nishant A. Mehta, et al. ∙ 0 ∙ share

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Fast Rates for General Unbounded Loss Functions: from ERM to Generalized Bayes

We present new excess risk bounds for general unbounded loss functions i...
05/01/2016 ∙ by Peter D. Grünwald, et al. ∙ 0 ∙ share

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Online Isotonic Regression

We consider the online version of the isotonic regression problem. Given...
03/14/2016 ∙ by Wojciech Kotłowski, et al. ∙ 0 ∙ share

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Maximin Action Identification: A New Bandit Framework for Games

We study an original problem of pure exploration in a strategic bandit m...
02/15/2016 ∙ by Aurélien Garivier, et al. ∙ 0 ∙ share

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Timed Soft Concurrent Constraint Programs: An Interleaved and a Parallel Approach

We propose a timed and soft extension of Concurrent Constraint Programmi...
02/24/2014 ∙ by Stefano Bistarelli, et al. ∙ 0 ∙ share

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Fast rates in statistical and online learning

The speed with which a learning algorithm converges as it is presented w...
07/09/2015 ∙ by Tim van Erven, et al. ∙ 0 ∙ share

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Estimating the Maximum Expected Value: An Analysis of (Nested) Cross Validation and the Maximum Sample Average

We investigate the accuracy of the two most common estimators for the ma...
02/28/2013 ∙ by Hado van Hasselt, et al. ∙ 0 ∙ share

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A method for locally approximating regularized iterative tomographic reconstruction methods

In many applications of tomography, the acquired projections are either ...
04/08/2016 ∙ by D. M. Pelt, et al. ∙ 0 ∙ share

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Solving Weighted Voting Game Design Problems Optimally: Representations, Synthesis, and Enumeration

We study the inverse power index problem for weighted voting games: the ...
04/23/2012 ∙ by Bart de Keijzer, et al. ∙ 0 ∙ share

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Horizon-Independent Optimal Prediction with Log-Loss in Exponential Families

We study online learning under logarithmic loss with regular parametric ...
05/19/2013 ∙ by Peter Bartlett, et al. ∙ 0 ∙ share

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Follow the Leader If You Can, Hedge If You Must

Follow-the-Leader (FTL) is an intuitive sequential prediction strategy t...
01/03/2013 ∙ by Steven de Rooij, et al. ∙ 0 ∙ share

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Web Similarity

Normalized web distance (NWD) is a similarity or normalized semantic dis...
02/20/2015 ∙ by Andrew R. Cohen, et al. ∙ 0 ∙ share

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Exact Expression For Information Distance

Information distance can be defined not only between two strings but als...
10/27/2014 ∙ by P. M. B. Vitanyi, et al. ∙ 0 ∙ share

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Adaptive Hedge

Most methods for decision-theoretic online learning are based on the Hed...
10/28/2011 ∙ by Tim van Erven, et al. ∙ 0 ∙ share

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Generic identification of binary-valued hidden Markov processes

The generic identification problem is to decide whether a stochastic pro...
01/19/2011 ∙ by Alexander Schönhuth, et al. ∙ 0 ∙ share

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Applying MDL to Learning Best Model Granularity

The Minimum Description Length (MDL) principle is solidly based on a pro...
05/23/2000 ∙ by Qiong Gao, et al. ∙ 0 ∙ share

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Constraint Programming viewed as Rule-based Programming

We study here a natural situation when constraint programming can be ent...
03/24/2000 ∙ by Krzysztof R. Apt, et al. ∙ 0 ∙ share

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Automatic Generation of Constraint Propagation Algorithms for Small Finite Domains

We study here constraint satisfaction problems that are based on predefi...
09/08/1999 ∙ by Krzysztof R. Apt, et al. ∙ 0 ∙ share

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The Rough Guide to Constraint Propagation

We provide here a simple, yet very general framework that allows us to e...
09/08/1999 ∙ by Krzysztof R. Apt, et al. ∙ 0 ∙ share

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An Algebraic Programming Style for Numerical Software and its Optimization

The abstract mathematical theory of partial differential equations (PDEs...
03/01/1999 ∙ by T. B. Dinesh, et al. ∙ 0 ∙ share

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Minimum Description Length Induction, Bayesianism, and Kolmogorov Complexity

The relationship between the Bayesian approach and the minimum descripti...
01/27/1999 ∙ by Paul Vitanyi, et al. ∙ 0 ∙ share

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Catching Up Faster by Switching Sooner: A Prequential Solution to the AIC-BIC Dilemma

Bayesian model averaging, model selection and its approximations such as...
07/07/2008 ∙ by Tim van Erven, et al. ∙ 0 ∙ share

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Normalized Compression Distance of Multisets with Applications

Normalized compression distance (NCD) is a parameter-free, feature-free,...
12/22/2012 ∙ by Andrew R. Cohen, et al. ∙ 0 ∙ share

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Information Distance: New Developments

In pattern recognition, learning, and data mining one obtains informatio...
01/05/2012 ∙ by P. M. B. Vitanyi, et al. ∙ 0 ∙ share

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Normalized Information Distance is Not Semicomputable

Normalized information distance (NID) uses the theoretical notion of Kol...
06/16/2010 ∙ by Sebastiaan A. Terwijn, et al. ∙ 0 ∙ share

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Information Distance in Multiples

Information distance is a parameter-free similarity measure based on com...
05/20/2009 ∙ by Paul M. B. Vitanyi, et al. ∙ 0 ∙ share

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A New Quartet Tree Heuristic for Hierarchical Clustering

We consider the problem of constructing an an optimal-weight tree from t...
06/11/2006 ∙ by Rudi Cilibrasi, et al. ∙ 0 ∙ share

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Similarity of Objects and the Meaning of Words

We survey the emerging area of compression-based, parameter-free, simila...
02/17/2006 ∙ by Rudi Cilibrasi, et al. ∙ 0 ∙ share

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Clustering by compression

We present a new method for clustering based on compression. The method ...
12/19/2003 ∙ by Rudi Cilibrasi, et al. ∙ 0 ∙ share

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The similarity metric

A new class of distances appropriate for measuring similarity relations ...
11/20/2001 ∙ by Ming Li, et al. ∙ 0 ∙ share

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