Statistical Learning
Vivien Cabannes Meta AI Stefano Vigogna University of Rome Tor V ergata
Once learned, the I/O mapping can be deployed in the wild to infer useful information from new inputs.
EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis Dragos
Moreover, we observe that the focus in the few related works falls on quantifying similarity in malware, often overlooking the clean data. This one-sided quantification is especially dangerous in the context of detection bypass.
Learning Space-Time Continuous Neural PDEs from Partially Observed States
We propose a space-time continuous latent neural PDE model with an efficient probabilistic framework and a novel encoder design for improved data efficiency and grid independence. The latent state dynamics are governed by a PDE model that combines the collocation method and the method of lines.
Practical Sharpness-Aware Minimization Cannot Converge All the Way to Optima
To address this gap, we study deterministic/stochastic versions of SAM with practical configurations (i.e., constant
