6 citations · 7 across the 7 of their papers we have counts for
9 papers
Cross-Modal Forgetting and Transfer in Unified Multimodal Post-Training
Francesco Colasurdo, Ivan Slobozhan, Joaquin Vanschoren +1
Unified multimodal models answer questions about images and also generate images with one shared set of weights. This creates a post-training risk that single-purpose models do not…
PlaySuite: A Large-Scale Benchmark for Interactive Visual Intelligence
Dheeraj Varghese, Anna Vettoruzzo, Walter Simoncini +5
Recent advances in multimodal foundation models yield strong performance on static perception and reasoning benchmarks, yet such evaluations largely overlook a central aspect of in…
TimeSage-EV: A Live Benchmark for Agentic Time Series Analysis in Evolving Environments
Qingren Yao, Yaxuan Kong, Yuqi Nie +6
Time series analysis in high-stakes domains relies on recurring data releases, where new observations can alter the evidence base and the validity of later conclusions. Existing ti…
TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning
Yaxuan Kong, Qingren Yao, Yuqi Nie +7
Time series data inform critical decisions across many real-world domains. While large language model (LLM) agents can analyze data through natural language and tools, it remains u…
Meta-Learning Transformers to Improve In-Context Generalization
Lorenzo Braccaioli, Anna Vettoruzzo, Prabhant Singh +3
In-context learning enables transformer models to generalize to new tasks based solely on input prompts, without any need for weight updates. However, existing training paradigms t…
Learning to Learn without Forgetting using Attention
Anna Vettoruzzo, Joaquin Vanschoren, Mohamed-Rafik Bouguelia +1
Continual learning (CL) refers to the ability to continually learn over time by accommodating new knowledge while retaining previously learned experience. While this concept is inh…
Unsupervised Meta-Learning via In-Context Learning
Anna Vettoruzzo, Lorenzo Braccaioli, Joaquin Vanschoren +1
Unsupervised meta-learning aims to learn feature representations from unsupervised datasets that can transfer to downstream tasks with limited labeled data. In this paper, we propo…
Personalized Federated Learning with Contextual Modulation and Meta-Learning
Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Thorsteinn Rögnvaldsson
Federated learning has emerged as a promising approach for training machine learning models on decentralized data sources while preserving data privacy. However, challenges such as…
Advances and Challenges in Meta-Learning: A Technical Review
Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren +2
Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks, enabling faster adaptation and generalization to new tasks. This review provides…