3 citations · 6 across the 16 of their papers we have counts for
5 papers · 1 filter
Testing the Test: Score-Direction Instability in Class-Split Anomaly Detection
Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2
Within-dataset class-split evaluation is widely used as a proxy for fully unconditional out-of-distribution anomaly detection. We show that this protocol can become ill-posed when…
When Rule Violations Are Rare: Chimera Training for Logical Anomaly Detection
Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2
Many practical anomalies are not merely rare inputs, but violations of semantic constraints: objects co-occur in structured ways, actions imply preconditions, and events satisfy te…
VAE with Hyperspherical Coordinates: Improving Anomaly Detection from Hypervolume-Compressed Latent Space
Alejandro Ascarate, Leo Lebrat, Rodrigo Santa Cruz +2
Variational autoencoders (VAE) encode data into lower-dimensional latent vectors before decoding those vectors back to data. Once trained, one can hope to detect out-of-distributio…
Physics-Informed Operator Learning for Hemodynamic Modeling
Ryan Chappell, Chayan Banerjee, Kien Nguyen +1
Accurate modeling of personalized cardiovascular dynamics is crucial for non-invasive monitoring and therapy planning. State-of-the-art physics-informed neural network (PINN) appro…
Mining--Gym: A Configurable RL Benchmarking Environment for Truck Dispatch Scheduling
Chayan Banerjee, Kien Nguyen, Clinton Fookes
Optimizing the mining process -- particularly truck dispatch scheduling -- is a key driver of efficiency in open-pit operations. However, the dynamic and stochastic nature of these…