4 citations · 5 across the 5 of their papers we have counts for
5 papers
From Charts to Atlas: Merging Latent Spaces into One
Donato Crisostomi, Irene Cannistraci, Luca Moschella +4
Models trained on semantically related datasets and tasks exhibit comparable inter-sample relations within their latent spaces. We investigate in this study the aggregation of such…
From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication
Irene Cannistraci, Luca Moschella, Marco Fumero +2
It has been observed that representations learned by distinct neural networks conceal structural similarities when the models are trained under similar inductive biases. From a geo…
LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study
Matteo Prata, Giuseppe Masi, Leonardo Berti +6
The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL mod…
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…
AI-based Data Preparation and Data Analytics in Healthcare: The Case of Diabetes
Marianna Maranghi, Aris Anagnostopoulos, Irene Cannistraci +13
The Associazione Medici Diabetologi (AMD) collects and manages one of the largest worldwide-available collections of diabetic patient records, also known as the AMD database. This…