28 citations · 49 across the 17 of their papers we have counts for
5 papers · 1 filter
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.…
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…
Accelerating Transformer Inference for Translation via Parallel Decoding
Andrea Santilli, Silvio Severino, Emilian Postolache +4
Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT). The community proposed specific network architectures and learning-based methods to solv…
Attention-likelihood relationship in transformers
Valeria Ruscio, Valentino Maiorca, Fabrizio Silvestri
We analyze how large language models (LLMs) represent out-of-context words, investigating their reliance on the given context to capture their semantics. Our likelihood-guided text…
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…