10 citations · 36 across the 22 of their papers we have counts for
26 papers
From Modes to Memories: Characterizing the Scale-Space Dynamics of Diffusion Models
Cristina López Amado, Marco Fumero, Francesco Locatello
Diffusion models are typically viewed as stochastic processes that transform noise into data. We take a complementary perspective: a diffusion model defines a family of determinist…
-JEPA Spectral Anti-Collapse Regularization for Self-Supervised Learning
Berker Demirel, Clémentine Dominé, Valentino Maiorca +3
Joint-embedding self-supervised learning typically combines an invariance objective across augmented views with additional mechanisms to prevent representational collapse. These ob…
Thinking at the Right Size: Amortized Distillation Across Post-Trained LLMs
Yan Zhou, Sara Kangaslahti, Jonathan Geuter +4
Practical deployment of large language models (LLMs) requires families of post-trained variants---instruction-tuned, reasoning-tuned, and chat-style models---each at multiple sizes…
Understanding Layer Patching in Model Size Interpolation
Sara Kangaslahti, Jonathan Geuter, Nihal V. Nayak +3
Zero-shot model size interpolation aims to create new models of intermediate target sizes by combining existing models without additional training. Recent work on boomerang distill…
Assessing Sample Quality in Conditional Generation under Compositional Shift
Berker Demirel, Valentino Maiorca, Marco Fumero +2
Conditional generators provide a natural tool for controllable generation, including settings where the desired condition is a new composition of observed attributes or experimenta…
Statistical and structural identifiability in representation learning
Walter Nelson, Marco Fumero, Theofanis Karaletsos +1
Representation learning models exhibit a surprising stability in their internal representations. Whereas most prior work treats this stability as a single property, we formalize it…
MorphGen: Controllable and Morphologically Plausible Generative Cell-Imaging
Berker Demirel, Marco Fumero, Theofanis Karaletsos +1
Simulating in silico cellular responses to interventions is a promising direction to accelerate high-content image-based assays, critical for advancing drug discovery and gene edit…
Boomerang Distillation Enables Zero-Shot Model Size Interpolation
Sara Kangaslahti, Nihal V. Nayak, Jonathan Geuter +3
Large language models (LLMs) are typically deployed under diverse memory and compute constraints. Existing approaches build model families by training each size independently, whic…
Learning Explicit Single-Cell Dynamics Using ODE Representations
Jan-Philipp von Bassewitz, Adeel Pervez, Marco Fumero +3
Modeling the dynamics of cellular differentiation is fundamental to advancing the understanding and treatment of diseases associated with this process, such as cancer. With the rap…
Connecting Neural Models Latent Geometries with Relative Geodesic Representations
Hanlin Yu, Berfin Inal, Georgios Arvanitidis +4
Neural models learn representations of high-dimensional data on low-dimensional manifolds. Multiple factors, including stochasticities in the training process, model architectures,…
Navigating the Latent Space Dynamics of Neural Models
Marco Fumero, Luca Moschella, Emanuele Rodolà +1
Neural networks transform high-dimensional data into compact, structured representations, often modeled as elements of a lower dimensional latent space. In this paper, we present a…
Out-of-Distribution Detection with Relative Angles
Berker Demirel, Marco Fumero, Francesco Locatello
Deep learning systems deployed in real-world applications often encounter data that is different from their in-distribution (ID). A reliable model should ideally abstain from makin…
Unifying Causal Representation Learning with the Invariance Principle
Dingling Yao, Dario Rancati, Riccardo Cadei +2
Causal representation learning (CRL) aims at recovering latent causal variables from high-dimensional observations to solve causal downstream tasks, such as predicting the effect o…
Latent Space Translation via Inverse Relative Projection
Valentino Maiorca, Luca Moschella, Marco Fumero +2
The emergence of similar representations between independently trained neural models has sparked significant interest in the representation learning community, leading to the devel…
Latent Functional Maps: a spectral framework for representation alignment
Marco Fumero, Marco Pegoraro, Valentino Maiorca +2
Neural models learn data representations that lie on low-dimensional manifolds, yet modeling the relation between these representational spaces is an ongoing challenge. By integrat…
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…
Leveraging sparse and shared feature activations for disentangled representation learning
Marco Fumero, Florian Wenzel, Luca Zancato +5
Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings. Mostly building on unsupervised and weakly-supervised objective…
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…
Certification of Gaussian Boson Sampling via graph theory
Taira Giordani, Valerio Mannucci, Nicolò Spagnolo +4
Gaussian Boson Sampling is a non-universal model for quantum computing inspired by the original formulation of the Boson Sampling problem. Nowadays, it represents a paradigmatic qu…
Unsupervised Source Separation via Bayesian Inference in the Latent Domain
Michele Mancusi, Emilian Postolache, Giorgio Mariani +4
State of the art audio source separation models rely on supervised data-driven approaches, which can be expensive in terms of labeling resources. On the other hand, approaches for…
CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation
Aditya Sanghi, Hang Chu, Joseph G. Lambourne +4
Generating shapes using natural language can enable new ways of imagining and creating the things around us. While significant recent progress has been made in text-to-image genera…