
TopDown Networks: A coarsetofine reimagination of CNNs
Biological vision adopts a coarsetofine information processing pathway...
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Symbolic Regression Methods for Reinforcement Learning
Reinforcement learning algorithms can be used to optimally solve dynamic...
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Maximizing Information Gain in Partially Observable Environments via Prediction Reward
Information gathering in a partially observable environment can be formu...
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EdgeNets:Edge Varying Graph Neural Networks
Driven by the outstanding performance of neural networks in the structur...
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Matrix Product Operator Restricted Boltzmann Machines
A restricted Boltzmann machine (RBM) learns a probability distribution o...
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TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise
Robustness to label noise is a critical property for weaklysupervised c...
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Finegrained Classification of Rowing teams
Finegrained classification tasks such as identifying different breeds o...
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Probabilistic Recursive Reasoning for MultiAgent Reinforcement Learning
Humans are capable of attributing latent mental contents such as beliefs...
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Visionbased Navigation Using Deep Reinforcement Learning
Deep reinforcement learning (RL) has been successfully applied to a vari...
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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...
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Fidel: Reconstructing Private Training Samples from Weight Updates in Federated Learning
With the increasing number of data collectors such as smartphones, immen...
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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...
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A Sufficient Statistic for Influence in Structured Multiagent Environments
Making decisions in complex environments is a key challenge in artificia...
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UneVEn: Universal Value Exploration for MultiAgent Reinforcement Learning
This paper focuses on cooperative valuebased multiagent reinforcement ...
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Helping users discover perspectives: Enhancing opinion mining with joint topic models
Support or opposition concerning a debated claim such as abortion should...
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Testing with Fewer Resources: An Adaptive Approach to PerformanceAware Test Case Generation
Automated test case generation is an effective technique to yield highc...
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MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning
This paper introduces MDP homomorphic networks for deep reinforcement le...
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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...
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Efficient exploration with Double Uncertain Value Networks
This paper studies directed exploration for reinforcement learning agent...
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Attended Endtoend Architecture for Age Estimation from Facial Expression Videos
The main challenges of age estimation from facial expression videos lie ...
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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...
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Regularization via Mass Transportation
The goal of regression and classification methods in supervised learning...
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Supervised Classification: Quite a Brief Overview
The original problem of supervised classification considers the task of ...
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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...
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MR AcquisitionInvariant Representation Learning
Voxelwise classification is a popular and effective method for tissue qu...
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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...
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Responsible Autonomy
As intelligent systems are increasingly making decisions that directly a...
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Emotion in Reinforcement Learning Agents and Robots: A Survey
This article provides the first survey of computational models of emotio...
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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 ...
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Target contrastive pessimistic risk for robust domain adaptation
In domain adaptation, classifiers with information from a source domain ...
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Nuclear Discrepancy for Active Learning
Active learning algorithms propose which unlabeled objects should be que...
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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...
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Learning Multimodal Transition Dynamics for ModelBased Reinforcement Learning
In this paper we study how to learn stochastic, multimodal transition dy...
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ScaleRegularized Filter Learning
We start out by demonstrating that an elementary learning task, correspo...
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Interpreting Finite Automata for Sequential Data
Automaton models are often seen as interpretable models. Interpretabilit...
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Active Learning Using Uncertainty Information
Many active learning methods belong to the retrainingbased approaches, ...
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Can we reach Pareto optimal outcomes using bottomup approaches?
Traditionally, researchers in decision making have focused on attempting...
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The Pessimistic Limits of Marginbased Losses in Semisupervised Learning
We show that for linear classifiers defined by convex marginbased surro...
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Solving the L1 regularized least square problem via a boxconstrained smooth minimization
In this paper, an equivalent smooth minimization for the L1 regularized ...
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Optimistic Semisupervised Least Squares Classification
The goal of semisupervised learning is to improve supervised classifier...
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Fast kNN mode seeking clustering applied to active learning
A significantly faster algorithm is presented for the original kNN mode ...
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InfluenceOptimistic Local Values for Multiagent Planning  Extended Version
Recent years have seen the development of methods for multiagent plannin...
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Temporal AttentionGated Model for Robust Sequence Classification
Typical techniques for sequence classification are designed for wellseg...
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A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization
Bayesian optimization through Gaussian process regression is an effectiv...
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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...
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Projected Estimators for Robust Semisupervised Classification
For semisupervised techniques to be applied safely in practice we at le...
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System Identification through Online Sparse Gaussian Process Regression with Input Noise
There has been a growing interest in using nonparametric regression met...
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Robust Semisupervised Least Squares Classification by Implicit Constraints
We introduce the implicitly constrained least squares (ICLS) classifier,...
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FeatureLevel Domain Adaptation
Domain adaptation is the supervised learning setting in which the traini...
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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...
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Delft University of Technology
Delft University of Technology also known as TU Delft, is the largest and oldest Dutch public technological university, located in Delft, Netherlands.