Centrum Wiskunde & Informatica

research

∙ 02/17/2020

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

To study radiotherapy-related adverse effects, detailed dose information...

M. Virgolin, et al. ∙

research

∙ 11/05/2018

A Biologically Plausible Learning Rule for Deep Learning in the Brain

Researchers have proposed that deep learning, which is providing importa...

Isabella Pozzi, et al. ∙

research

∙ 01/23/2019

Robust temporal difference learning for critical domains

We present a new Q-function operator for temporal difference (TD) learni...

Richard Klima, et al. ∙

research

∙ 05/24/2020

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

The emergence of brain-inspired neuromorphic computing as a paradigm for...

Bojian Yin, et al. ∙

research

∙ 01/23/2020

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...

Arkadiy Dushatskiy, et al. ∙

research

∙ 01/28/2022

A tomographic workflow to enable deep learning for X-ray based foreign object detection

Detection of unwanted (`foreign') objects within products is a common pr...

Mathé T. Zeegers, et al. ∙

research

∙ 09/06/2021

Automatic Landmarks Correspondence Detection in Medical Images with an Application to Deformable Image Registration

Deformable Image Registration (DIR) can benefit from additional guidance...

Monika Grewal, et al. ∙

research

∙ 09/09/2019

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

Machine Learning (ML) is proving extremely beneficial in many healthcare...

Marco Virgolin, et al. ∙

research

∙ 10/25/2021

Quantum Algorithms and Lower Bounds for Linear Regression with Norm Constraints

Lasso and Ridge are important minimization problems in machine learning ...

Yanlin Chen, et al. ∙

research

∙ 05/12/2019

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

Unlike previous works, this open data collection consists of X-ray cone-...

Henri Der Sarkissian, et al. ∙

research

∙ 01/04/2022

The cluster structure function

For each partition of a data set into a given number of parts there is a...

Andrew R. Cohen, et al. ∙

research

∙ 11/18/2017

Prediction Scores as a Window into Classifier Behavior

Most multi-class classifiers make their prediction for a test sample by ...

Medha Katehara, et al. ∙

research

∙ 10/21/2017

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...

Peter D. Grünwald, et al. ∙

research

∙ 08/04/2017

Identification of Probabilities

Within psychology, neuroscience and artificial intelligence, there has b...

Paul M. B. Vitanyi, et al. ∙

research

∙ 07/27/2017

Common Knowledge in a Logic of Gossips

Gossip protocols aim at arriving, by means of point-to-point or group co...

Krzysztof R. Apt, et al. ∙

research

∙ 07/14/2017

Lenient Multi-Agent Deep Reinforcement Learning

A significant amount of research in recent years has been dedicated towa...

Gregory Palmer, et al. ∙

research

∙ 03/11/2016

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...

Koen Groenland, et al. ∙

research

∙ 03/14/2017

Conditional Time Series Forecasting with Convolutional Neural Networks

We present a method for conditional time series forecasting based on the...

Anastasia Borovykh, et al. ∙

research

∙ 07/03/2016

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

Traditionally, researchers in decision making have focused on attempting...

Victor Sanchez-Anguix, et al. ∙

research

∙ 06/28/2016

Technical Report: Towards a Universal Code Formatter through Machine Learning

There are many declarative frameworks that allow us to implement code fo...

Terence Parr, et al. ∙

research

∙ 06/24/2016

Translucent Players: Explaining Cooperative Behavior in Social Dilemmas

In the last few decades, numerous experiments have shown that humans do ...

Valerio Capraro, et al. ∙

research

∙ 06/24/2016

Epistemic Protocols for Distributed Gossiping

Gossip protocols aim at arriving, by means of point-to-point or group co...

Krzysztof R. Apt, et al. ∙

research

∙ 10/29/2010

Fractionally Predictive Spiking Neurons

Recent experimental work has suggested that the neural firing rate can b...

Sander M. Bohte, et al. ∙

research

∙ 02/02/1999

A Discipline of Evolutionary Programming

Genetic fitness optimization using small populations or small population...

Paul Vitanyi, et al. ∙

research

∙ 09/12/2016

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

The adaptive gradient online learning method known as AdaGrad has seen w...

Nishant A. Mehta, et al. ∙

research

∙ 05/01/2016

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

We present new excess risk bounds for general unbounded loss functions i...

Peter D. Grünwald, et al. ∙

research

∙ 03/14/2016

Online Isotonic Regression

We consider the online version of the isotonic regression problem. Given...

Wojciech Kotłowski, et al. ∙

research

∙ 02/15/2016

Maximin Action Identification: A New Bandit Framework for Games

We study an original problem of pure exploration in a strategic bandit m...

Aurélien Garivier, et al. ∙

research

∙ 02/24/2014

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

We propose a timed and soft extension of Concurrent Constraint Programmi...

Stefano Bistarelli, et al. ∙

research

∙ 07/09/2015

Fast rates in statistical and online learning

The speed with which a learning algorithm converges as it is presented w...

Tim van Erven, et al. ∙

research

∙ 02/28/2013

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...

Hado van Hasselt, et al. ∙

research

∙ 04/08/2016

A method for locally approximating regularized iterative tomographic reconstruction methods

In many applications of tomography, the acquired projections are either ...

D. M. Pelt, et al. ∙

research

∙ 04/23/2012

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

We study the inverse power index problem for weighted voting games: the ...

Bart de Keijzer, et al. ∙

research

∙ 05/19/2013

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

We study online learning under logarithmic loss with regular parametric ...

Peter Bartlett, et al. ∙

research

∙ 01/03/2013

Follow the Leader If You Can, Hedge If You Must

Follow-the-Leader (FTL) is an intuitive sequential prediction strategy t...

Steven de Rooij, et al. ∙

research

∙ 02/20/2015

Web Similarity

Normalized web distance (NWD) is a similarity or normalized semantic dis...

Andrew R. Cohen, et al. ∙

research

∙ 10/27/2014

Exact Expression For Information Distance

Information distance can be defined not only between two strings but als...

P. M. B. Vitanyi, et al. ∙

research

∙ 10/28/2011

Adaptive Hedge

Most methods for decision-theoretic online learning are based on the Hed...

Tim van Erven, et al. ∙

research

∙ 01/19/2011

Generic identification of binary-valued hidden Markov processes

The generic identification problem is to decide whether a stochastic pro...

Alexander Schönhuth, et al. ∙

research

∙ 05/23/2000

Applying MDL to Learning Best Model Granularity

The Minimum Description Length (MDL) principle is solidly based on a pro...

Qiong Gao, et al. ∙

research

∙ 03/24/2000

Constraint Programming viewed as Rule-based Programming

We study here a natural situation when constraint programming can be ent...

Krzysztof R. Apt, et al. ∙

research

∙ 09/08/1999

Automatic Generation of Constraint Propagation Algorithms for Small Finite Domains

We study here constraint satisfaction problems that are based on predefi...

Krzysztof R. Apt, et al. ∙

research

∙ 09/08/1999

The Rough Guide to Constraint Propagation

We provide here a simple, yet very general framework that allows us to e...

Krzysztof R. Apt, et al. ∙

research

∙ 03/01/1999

An Algebraic Programming Style for Numerical Software and its Optimization

The abstract mathematical theory of partial differential equations (PDEs...

T. B. Dinesh, et al. ∙

research

∙ 01/27/1999

Minimum Description Length Induction, Bayesianism, and Kolmogorov Complexity

The relationship between the Bayesian approach and the minimum descripti...

Paul Vitanyi, et al. ∙

research

∙ 07/07/2008

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

Bayesian model averaging, model selection and its approximations such as...

Tim van Erven, et al. ∙

research

∙ 12/22/2012

Normalized Compression Distance of Multisets with Applications

Normalized compression distance (NCD) is a parameter-free, feature-free,...

Andrew R. Cohen, et al. ∙

research

∙ 01/05/2012

Information Distance: New Developments

In pattern recognition, learning, and data mining one obtains informatio...

P. M. B. Vitanyi, et al. ∙

research

∙ 06/16/2010

Normalized Information Distance is Not Semicomputable

Normalized information distance (NID) uses the theoretical notion of Kol...

Sebastiaan A. Terwijn, et al. ∙

research

∙ 05/20/2009

Information Distance in Multiples

Information distance is a parameter-free similarity measure based on com...

Paul M. B. Vitanyi, et al. ∙

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