Delft University of Technology

∙ Delft , Netherlands ∙ 0 followers

Delft University of Technology also known as TU Delft, is the largest and oldest Dutch public technological university, located in Delft, Netherlands.

Top-Down Networks: A coarse-to-fine reimagination of CNNs

Biological vision adopts a coarse-to-fine information processing pathway...
04/16/2020 ∙ by Ioannis Lelekas, et al. ∙ 69 ∙ share

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Symbolic Regression Methods for Reinforcement Learning

Reinforcement learning algorithms can be used to optimally solve dynamic...
03/22/2019 ∙ by Jiří Kubalík, et al. ∙ 34 ∙ share

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Maximizing Information Gain in Partially Observable Environments via Prediction Reward

Information gathering in a partially observable environment can be formu...
05/11/2020 ∙ by Yash Satsangi, et al. ∙ 32 ∙ share

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EdgeNets:Edge Varying Graph Neural Networks

Driven by the outstanding performance of neural networks in the structur...
01/21/2020 ∙ by Elvin Isufi, et al. ∙ 29 ∙ share

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Matrix Product Operator Restricted Boltzmann Machines

A restricted Boltzmann machine (RBM) learns a probability distribution o...
11/12/2018 ∙ by Cong Chen, et al. ∙ 22 ∙ share

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TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise

Robustness to label noise is a critical property for weakly-supervised c...
07/13/2020 ∙ by Amirmasoud Ghiassi, et al. ∙ 20 ∙ share

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Fine-grained Classification of Rowing teams

Fine-grained classification tasks such as identifying different breeds o...
12/11/2019 ∙ by M. J. A. van Wezel, et al. ∙ 16 ∙ share

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Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning

Humans are capable of attributing latent mental contents such as beliefs...
01/26/2019 ∙ by Ying Wen, et al. ∙ 12 ∙ share

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Vision-based Navigation Using Deep Reinforcement Learning

Deep reinforcement learning (RL) has been successfully applied to a vari...
08/08/2019 ∙ by Jonáš Kulhánek, et al. ∙ 10 ∙ share

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How do neural networks see depth in single images?

Deep neural networks have lead to a breakthrough in depth estimation fro...
05/16/2019 ∙ by Tom van Dijk, et al. ∙ 8 ∙ share

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Consistency and Finite Sample Behavior of Binary Class Probability Estimation

In this work we investigate to which extent one can recover class probab...
08/30/2019 ∙ by Alexander Mey, et al. ∙ 6 ∙ share

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A Sufficient Statistic for Influence in Structured Multiagent Environments

Making decisions in complex environments is a key challenge in artificia...
07/22/2019 ∙ by Frans A. Oliehoek, et al. ∙ 4 ∙ share

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Testing with Fewer Resources: An Adaptive Approach to Performance-Aware Test Case Generation

Automated test case generation is an effective technique to yield high-c...
07/19/2019 ∙ by Giovanni Grano, et al. ∙ 1 ∙ share

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MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning

This paper introduces MDP homomorphic networks for deep reinforcement le...
06/30/2020 ∙ by Elise van der Pol, et al. ∙ 1 ∙ share

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Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata

This paper focuses on detecting anomalies in a digital video broadcastin...
05/24/2017 ∙ by Xiaoran Liu, et al. ∙ 0 ∙ share

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Efficient exploration with Double Uncertain Value Networks

This paper studies directed exploration for reinforcement learning agent...
11/29/2017 ∙ by Thomas M. Moerland, et al. ∙ 0 ∙ share

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Attended End-to-end Architecture for Age Estimation from Facial Expression Videos

The main challenges of age estimation from facial expression videos lie ...
11/23/2017 ∙ by Wenjie Pei, et al. ∙ 0 ∙ share

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Complexity of Scheduling Charging in the Smart Grid

In the smart grid, the intent is to use flexibility in demand, both to b...
09/21/2017 ∙ by Mathijs de Weerdt, et al. ∙ 0 ∙ share

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Regularization via Mass Transportation

The goal of regression and classification methods in supervised learning...
10/27/2017 ∙ by Soroosh Shafieezadeh-Abadeh, et al. ∙ 0 ∙ share

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Supervised Classification: Quite a Brief Overview

The original problem of supervised classification considers the task of ...
10/25/2017 ∙ by Marco Loog, et al. ∙ 0 ∙ share

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On reducing sampling variance in covariate shift using control variates

Covariate shift classification problems can in principle be tackled by i...
10/17/2017 ∙ by Wouter Kouw, et al. ∙ 0 ∙ share

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MR Acquisition-Invariant Representation Learning

Voxelwise classification is a popular and effective method for tissue qu...
09/22/2017 ∙ by Wouter M. Kouw, et al. ∙ 0 ∙ share

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Accelerating CS in Parallel Imaging Reconstructions Using an Efficient and Effective Circulant Preconditioner

Purpose: Design of a preconditioner for fast and efficient parallel imag...
10/04/2017 ∙ by Kirsten Koolstra, et al. ∙ 0 ∙ share

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Responsible Autonomy

As intelligent systems are increasingly making decisions that directly a...
06/08/2017 ∙ by Virginia Dignum, et al. ∙ 0 ∙ share

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Emotion in Reinforcement Learning Agents and Robots: A Survey

This article provides the first survey of computational models of emotio...
05/15/2017 ∙ by Thomas M. Moerland, et al. ∙ 0 ∙ share

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On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL

In various approaches to learning, notably in domain adaptation, active ...
07/13/2017 ∙ by Marco Loog, et al. ∙ 0 ∙ share

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Target contrastive pessimistic risk for robust domain adaptation

In domain adaptation, classifiers with information from a source domain ...
06/25/2017 ∙ by Wouter M. Kouw, et al. ∙ 0 ∙ share

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Nuclear Discrepancy for Active Learning

Active learning algorithms propose which unlabeled objects should be que...
06/08/2017 ∙ by Tom J. Viering, et al. ∙ 0 ∙ share

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Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning...
12/11/2016 ∙ by Marc-André Carbonneau, et al. ∙ 0 ∙ share

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Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning

In this paper we study how to learn stochastic, multimodal transition dy...
05/01/2017 ∙ by Thomas M. Moerland, et al. ∙ 0 ∙ share

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Scale-Regularized Filter Learning

We start out by demonstrating that an elementary learning task, correspo...
07/10/2017 ∙ by Marco Loog, et al. ∙ 0 ∙ share

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Interpreting Finite Automata for Sequential Data

Automaton models are often seen as interpretable models. Interpretabilit...
11/21/2016 ∙ by Christian Albert Hammerschmidt, et al. ∙ 0 ∙ share

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Active Learning Using Uncertainty Information

Many active learning methods belong to the retraining-based approaches, ...
02/27/2017 ∙ by Yazhou Yang, 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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The Pessimistic Limits of Margin-based Losses in Semi-supervised Learning

We show that for linear classifiers defined by convex margin-based surro...
12/28/2016 ∙ by Jesse H. Krijthe, et al. ∙ 0 ∙ share

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Solving the L1 regularized least square problem via a box-constrained smooth minimization

In this paper, an equivalent smooth minimization for the L1 regularized ...
04/11/2017 ∙ by Majid Mohammadi, et al. ∙ 0 ∙ share

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Optimistic Semi-supervised Least Squares Classification

The goal of semi-supervised learning is to improve supervised classifier...
10/12/2016 ∙ by Jesse H. Krijthe, et al. ∙ 0 ∙ share

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Fast kNN mode seeking clustering applied to active learning

A significantly faster algorithm is presented for the original kNN mode ...
12/20/2017 ∙ by Robert P. W. Duin, et al. ∙ 0 ∙ share

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Influence-Optimistic Local Values for Multiagent Planning --- Extended Version

Recent years have seen the development of methods for multiagent plannin...
02/18/2015 ∙ by Frans A. Oliehoek, et al. ∙ 0 ∙ share

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Temporal Attention-Gated Model for Robust Sequence Classification

Typical techniques for sequence classification are designed for well-seg...
12/01/2016 ∙ by Wenjie Pei, et al. ∙ 0 ∙ share

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A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization

Bayesian optimization through Gaussian process regression is an effectiv...
04/01/2016 ∙ by Hildo Bijl, et al. ∙ 0 ∙ share

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Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions

This paper analyzes DONE, an online optimization algorithm that iterativ...
03/31/2016 ∙ by Laurens Bliek, et al. ∙ 0 ∙ share

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Projected Estimators for Robust Semi-supervised Classification

For semi-supervised techniques to be applied safely in practice we at le...
02/25/2016 ∙ by Jesse H. Krijthe, et al. ∙ 0 ∙ share

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System Identification through Online Sparse Gaussian Process Regression with Input Noise

There has been a growing interest in using non-parametric regression met...
01/29/2016 ∙ by Hildo Bijl, et al. ∙ 0 ∙ share

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Robust Semi-supervised Least Squares Classification by Implicit Constraints

We introduce the implicitly constrained least squares (ICLS) classifier,...
12/27/2015 ∙ by Jesse H. Krijthe, et al. ∙ 0 ∙ share

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Feature-Level Domain Adaptation

Domain adaptation is the supervised learning setting in which the traini...
12/15/2015 ∙ by Wouter M. Kouw, et al. ∙ 0 ∙ share

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Learning optimization models in the presence of unknown relations

In a sequential auction with multiple bidding agents, it is highly chall...
01/06/2014 ∙ by Sicco Verwer, et al. ∙ 0 ∙ share

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Making a Case for Learning Motion Representations with Phase

This work advocates Eulerian motion representation learning over the cur...
09/06/2016 ∙ by S. L. Pintea, et al. ∙ 0 ∙ share

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Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classification

Improvement guarantees for semi-supervised classifiers can currently onl...
03/01/2015 ∙ by Marco Loog, et al. ∙ 0 ∙ share

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Asynchronous Decentralized Algorithm for Space-Time Cooperative Pathfinding

Cooperative pathfinding is a multi-agent path planning problem where a g...
10/25/2012 ∙ by Michal Čáp, et al. ∙ 0 ∙ share

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