Tommaso Furlanello — accepted AI-conference papers

2025

Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code

Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T. Stillerman, Felix Friedrich +35

COLING 2025industry

Pretrained language models are integral part of AI applications, but their high computational cost for training limits accessibility. Initiatives such as Bloom and StarCoder aim to democratize access to pretrained models for collaborative community development. Despite these efforts, such models enc…

Cited by 2 Source ↗PDF ↗Scholar ↗

2018

Born Again Neural Networks

Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar

ICML 2018oral

Knowledge Distillation (KD) consists of transferring “knowledge” from one machine learning model (the teacher) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from…

Cited by 1313 Source ↗PDF ↗Scholar ↗

2018

Question Type Guided Attention in Visual Question Answering

Yang Shi, Tommaso Furlanello, Sheng Zha, Animashree Anandkumar

ECCV 2018poster

Visual Question Answering (VQA) requires integration of feature maps with drastically different structures and focus of the correct regions. Image descriptors have structures at multiple spatial scales, while lexical inputs inherently follow a temporal sequence and naturally cluster into semanticall…

Cited by 62 Source ↗PDF ↗Scholar ↗