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Found 9 papers, 4 papers with code
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference
In this work, we provide the first comprehensive study of the behaviour of neural SBI algorithms in the presence of various forms of model misspecification.
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning experiments and open-source training environments are typically limited in scale, supporting tens or sometimes up to hundreds of interacting agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex Noise
A secondary problem is that Gaussian diffusion fails to capture larger anomalies; therefore we develop a multi-scale simplex noise diffusion process that gives control over the target anomaly size.
Ranked #21 on
Anomaly Detection
on VisA
Calibrating Agent-based Models to Microdata with Graph Neural Networks
Calibrating agent-based models (ABMs) to data is among the most fundamental requirements to ensure the model fulfils its desired purpose.
Learning Multimodal VAEs through Mutual Supervision
Here we introduce a novel alternative, the MEME, that avoids such explicit combinations by repurposing semi-supervised VAEs to combine information between modalities implicitly through mutual supervision.
Capturing Label Characteristics in VAEs
We present a principled approach to incorporating labels in VAEs that captures the rich characteristic information associated with those labels.
A General Framework for Survival Analysis and Multi-State Modelling
We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks
Bayesian interpretations of neural network have a long history, dating back to early work in the 1990's and have recently regained attention because of their desirable properties like uncertainty estimation, model robustness and regularisation.
Bernoulli Race Particle Filters
When the weights in a particle filter are not available analytically, standard resampling methods cannot be employed.
