26 citations · 78 across the 10 of their papers we have counts for
10 papers
Deep Evidential Learning for Bayesian Quantile Regression
Frederik Boe Hüttel, Filipe Rodrigues, Francisco Câmara Pereira
It is desirable to have accurate uncertainty estimation from a single deterministic forward-pass model, as traditional methods for uncertainty quantification are computationally ex…
Applied metamodelling for ATM performance simulations
Christoffer Riis, Francisco N. Antunes, Tatjana Bolić +4
The use of Air traffic management (ATM) simulators for planing and operations can be challenging due to their modelling complexity. This paper presents XALM (eXplainable Active Lea…
Learning and Generalizing Polynomials in Simulation Metamodeling
Jesper Hauch, Christoffer Riis, Francisco C. Pereira
The ability to learn polynomials and generalize out-of-distribution is essential for simulation metamodels in many disciplines of engineering, where the time step updates are descr…
Graph Reinforcement Learning for Network Control via Bi-Level Optimization
Daniele Gammelli, James Harrison, Kaidi Yang +3
Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional op…
Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning
Carolin Schmidt, Daniele Gammelli, Francisco Camara Pereira +1
Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel reques…
Testing for context-dependent changes in neural encoding in naturalistic experiments
Yenho Chen, Carl W. Harris, Xiaoyu Ma +3
We propose a decoding-based approach to detect context effects on neural codes in longitudinal neural recording data. The approach is agnostic to how information is encoded in neur…
Determining Causality in Travel Mode Choice
Rishabh Singh Chauhan, Christoffer Riis, Shishir Adhikari +4
This article presents one of the pioneering studies on causal modeling in travel mode choice decision-making using causal discovery algorithms. These models are a major advancement…
VICE: Variational Interpretable Concept Embeddings
Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure +3
A central goal in the cognitive sciences is the development of numerical models for mental representations of object concepts. This paper introduces Variational Interpretable Conce…
Revealing interpretable object representations from human behavior
Charles Y. Zheng, Francisco Pereira, Chris I. Baker +1
To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images represen…
Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings
Gabriel Grand, Idan Asher Blank, Francisco Pereira +1
The words of a language reflect the structure of the human mind, allowing us to transmit thoughts between individuals. However, language can represent only a subset of our rich and…