activity
20192022
most citedCan Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

53 citations · 79 across the 6 of their papers we have counts for

collaborators

7 papers

physics.space-ph202215 cited

Global geomagnetic perturbation forecasting using Deep Learning

Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi +6

Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere,…

cs.AI202111 cited

Exploration and preference satisfaction trade-off in reward-free learning

Noor Sajid, Panagiotis Tigas, Alexey Zakharov +2

Biological agents have meaningful interactions with their environment despite the absence of immediate reward signals. In such instances, the agent can learn preferred modes of beh…

cs.HC2021

Latent Mappings: Generating Open-Ended Expressive Mappings Using Variational Autoencoders

Tim Murray-Browne, Panagiotis Tigas

In many contexts, creating mappings for gestural interactions can form part of an artistic process. Creators seeking a mapping that is expressive, novel, and affords them a sense o…

physics.geo-ph2021

Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition

Panagiotis Tigas, Téo Bloch, Vishal Upendran +6

Modeling and forecasting the solar wind-driven global magnetic field perturbations is an open challenge. Current approaches depend on simulations of computationally demanding model…

cs.AI2021

Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search

Panagiotis Tigas, Tyson Hosmer

With this work, we investigate the use of Reinforcement Learning (RL) for the generation of spatial assemblies, by combining ideas from Procedural Generation algorithms (Wave Funct…

cs.LG202053 cited

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Angelos Filos, Panagiotis Tigas, Rowan McAllister +3

Out-of-training-distribution (OOD) scenarios are a common challenge of learning agents at deployment, typically leading to arbitrary deductions and poorly-informed decisions. In pr…