Publications

Task Arithmetic has emerged as a simple yet effective method to merge models without additional training. However, by treating entire …

Evolutionary model merging enables the creation of high-performing multi-task models but remains computationally prohibitive for …

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are …

Model merging has recently emerged as a cost-efficient paradigm for multi-task learning. Among current approaches, task arithmetic …

In this paper, we present a novel data-free method for merging neural networks in weight space. Differently from most existing works, …

Models trained on semantically related datasets and tasks exhibit comparable inter-sample relations within their latent spaces. We …

Datasets used to train deep learning models in industrial settings often exhibit skewed distributions with some samples repeated a …

Getting a good understanding of the user intent is vital for e-commerce applications to surface the right product to a given customer …

Cross-lingual phenomena are quite common in informal contexts like social media, where users are likely to mix their native language …

Few-shot graph classification is a novel yet promising emerging research field that still lacks the soundness of well-established …

Deep learning approaches have recently raised the bar in many fields, from Natural Language Processing to Computer Vision, by …