565 citations · 626 across the 12 of their papers we have counts for
8 papers · 1 filter
DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
Zifeng Ding, Yifeng Li, Yuan He +5
Learning useful representations for continuous-time dynamic graphs (CTDGs) is challenging, due to the concurrent need to span long node interaction histories and grasp nuanced temp…
Latent Space Translation via Semantic Alignment
Valentino Maiorca, Luca Moschella, Antonio Norelli +3
While different neural models often exhibit latent spaces that are alike when exposed to semantically related data, this intrinsic similarity is not always immediately discernible.…
Bootstrapping Parallel Anchors for Relative Representations
Irene Cannistraci, Luca Moschella, Valentino Maiorca +3
The use of relative representations for latent embeddings has shown potential in enabling latent space communication and zero-shot model stitching across a wide range of applicatio…
ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training
Antonio Norelli, Marco Fumero, Valentino Maiorca +3
CLIP proved that aligning visual and language spaces is key to solving many vision tasks without explicit training, but required to train image and text encoders from scratch on a…
Relative representations enable zero-shot latent space communication
Luca Moschella, Valentino Maiorca, Marco Fumero +3
Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. Ideally, the distribution of the data points in the…
Explanatory Learning: Beyond Empiricism in Neural Networks
Antonio Norelli, Giorgio Mariani, Luca Moschella +4
We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences -- e.g. explanations written in hieroglyphic -- by autonomou…
OLIVAW: Mastering Othello without Human Knowledge, nor a Fortune
Antonio Norelli, Alessandro Panconesi
We introduce OLIVAW, an AI Othello player adopting the design principles of the famous AlphaGo programs. The main motivation behind OLIVAW was to attain exceptional competence in a…
LIMP: Learning Latent Shape Representations with Metric Preservation Priors
Luca Cosmo, Antonio Norelli, Oshri Halimi +2
In this paper, we advocate the adoption of metric preservation as a powerful prior for learning latent representations of deformable 3D shapes. Key to our construction is the intro…