6 papers
When One Adapter Speaks for Many: Discovering Low-Rank Redundancy in Continual Fine-Tuning
Tanguy Dieudonné, Giulia Lanzillotta, Enis Simsar +2
Low-Rank Adaptation (LoRA) has become the standard tool for parameter-efficient fine-tuning of large pretrained models. When applied sequentially across tasks in Continual Learning…
SeqLoRA: Bilevel Orthogonal Adaptation for Continual Multi-Concept Generation
Javad Parsa, Enis Simsar, Amir Joudaki +2
Parameter-efficient fine-tuning enables fast personalization of text-to-image diffusion models, but composing multiple custom concepts remains challenging due to representation int…
FullFlow: Upgrading Text-to-Image Flow Matching Models for Bidirectional Vision--Language Generation
Eric Tillmann Bill, Enis Simsar, Alessio Tonioni +1
Modern text-to-image diffusion models encode rich visual priors, but expose them only through one-way text-conditioned generation. Existing unified vision--language models derived…
Flash PD-SSM: Memory-Optimized Structured Sparse State-Space Models
Aleksandar Terzić, Francesco Carzaniga, Nicolas Menet +4
State-space models (SSMs) face a fundamental trade-off between efficiency and expressivity that is mainly dictated by the structure of the model's transition matrix. Unstructured t…
Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of Plasticity
Amir Joudaki, Giulia Lanzillotta, Mohammad Samragh Razlighi +5
Deep learning models excel in stationary data but struggle in non-stationary environments due to a phenomenon known as loss of plasticity (LoP), the degradation of their ability to…
Emergence of Globally Attracting Fixed Points in Deep Neural Networks With Nonlinear Activations
Amir Joudaki, Thomas Hofmann
Understanding how neural networks transform input data across layers is fundamental to unraveling their learning and generalization capabilities. Although prior work has used insig…