Deep generative models have demonstrated successful applications in learning non-linear
data distributions through a number of latent variables and these models use a non-linear …

Understanding crowd mobility is critical for many applications. In this paper, we propose
CrowdMirage, a WiFi positioning-based crowd mobility digital twin for smart campuses …

Deep generative models like variational autoencoders (VAEs) are widely used for density
estimation and dimensionality reduction, but infer latent representations via amortized …

Without knowledge of specific pockets, generating ligands based on the global structure of a
protein target plays a crucial role in drug discovery as it helps reduce the search space for …

Without knowledge of specific pockets, generating ligands based on the global structure of a
protein target plays a crucial role in drug discovery as it helps reduce the search space for …

Audio adversarial examples (AEs) have posed significant security challenges to real-world
speaker recognition systems. Most black-box attacks still require certain information from the …

In recent years generative AI models and tools have experienced a significant increase,
especially techniques to generate synthetic multimedia content, such as images or videos …

Without knowledge of specific pockets, generating ligands based on the global structure of a
protein target plays a crucial role in drug discovery as it helps reduce the search space for …

We introduce ET-Former, a novel end-to-end algorithm for semantic scene completion using
a single monocular camera. Our approach generates a semantic occupancy map from …

We propose a novel method for 3D shape completion from a partial observation of a point
cloud. Existing methods either operate on a global latent code, which limits the …