49 citations · 70 across the 15 of their papers we have counts for
17 papers
HyperTransport: Amortized Conditioning of T2I Generative Models
Valentino Maiorca, Eleonora Gualdoni, Xavier Suau +3
As foundation models grow in capability, the ability to efficiently and reliably control their behavior becomes critical. Fine-tuning these models can be costly, and while promptin…
ParaRNN: Unlocking Parallel Training of Nonlinear RNNs for Large Language Models
Federico Danieli, Pau Rodriguez, Miguel Sarabia +2
Recurrent Neural Networks (RNNs) laid the foundation for sequence modeling, but their intrinsic sequential nature restricts parallel computation, creating a fundamental barrier to…
LinEAS: End-to-end Learning of Activation Steering with a Distributional Loss
Pau Rodriguez, Michal Klein, Eleonora Gualdoni +5
The growing use of generative models in daily life calls for efficient mechanisms to control their generation, to e.g., produce safe content or provide users with tools to explore…
Controlling Language and Diffusion Models by Transporting Activations
Pau Rodriguez, Arno Blaas, Michal Klein +4
The increasing capabilities of large generative models and their ever more widespread deployment have raised concerns about their reliability, safety, and potential misuse. To addr…
Whispering Experts: Neural Interventions for Toxicity Mitigation in Language Models
Xavier Suau, Pieter Delobelle, Katherine Metcalf +4
An important issue with Large Language Models (LLMs) is their undesired ability to generate toxic language. In this work, we show that the neurons responsible for toxicity can be d…
DeepPCR: Parallelizing Sequential Operations in Neural Networks
Federico Danieli, Miguel Sarabia, Xavier Suau +2
Parallelization techniques have become ubiquitous for accelerating inference and training of deep neural networks. Despite this, several operations are still performed in a sequent…
How to Scale Your EMA
Dan Busbridge, Jason Ramapuram, Pierre Ablin +4
Preserving training dynamics across batch sizes is an important tool for practical machine learning as it enables the trade-off between batch size and wall-clock time. This trade-o…
The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning
Borja Rodríguez-Gálvez, Arno Blaas, Pau Rodríguez +5
The mechanisms behind the success of multi-view self-supervised learning (MVSSL) are not yet fully understood. Contrastive MVSSL methods have been studied through the lens of InfoN…
DUET: 2D Structured and Approximately Equivariant Representations
Xavier Suau, Federico Danieli, T. Anderson Keller +5
Multiview Self-Supervised Learning (MSSL) is based on learning invariances with respect to a set of input transformations. However, invariance partially or totally removes transfor…
Homomorphic Self-Supervised Learning
T. Anderson Keller, Xavier Suau, Luca Zappella
In this work, we observe that many existing self-supervised learning algorithms can be both unified and generalized when seen through the lens of equivariant representations. Speci…
Symphony: Composing Interactive Interfaces for Machine Learning
Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman +5
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, r…
Fair SA: Sensitivity Analysis for Fairness in Face Recognition
Aparna R. Joshi, Xavier Suau, Nivedha Sivakumar +2
As the use of deep learning in high impact domains becomes ubiquitous, it is increasingly important to assess the resilience of models. One such high impact domain is that of face…
Challenges of Adversarial Image Augmentations
Arno Blaas, Xavier Suau, Jason Ramapuram +2
Image augmentations applied during training are crucial for the generalization performance of image classifiers. Therefore, a large body of research has focused on finding the opti…
Stochastic Contrastive Learning
Jason Ramapuram, Dan Busbridge, Xavier Suau +1
While state-of-the-art contrastive Self-Supervised Learning (SSL) models produce results competitive with their supervised counterparts, they lack the ability to infer latent varia…
Self-conditioning pre-trained language models
Xavier Suau, Luca Zappella, Nicholas Apostoloff
In this paper we aim to investigate the mechanisms that guide text generation with pre-trained Transformer-based Language Models (TLMs). Grounded on the Product of Experts formulat…
Finding Experts in Transformer Models
Xavier Suau, Luca Zappella, Nicholas Apostoloff
In this work we study the presence of expert units in pre-trained Transformer Models (TM), and how they impact a model's performance. We define expert units to be neurons that are…
Filter Distillation for Network Compression
Xavier Suau, Luca Zappella, Nicholas Apostoloff
In this paper we introduce Principal Filter Analysis (PFA), an easy to use and effective method for neural network compression. PFA exploits the correlation between filter response…