A joint initiative of CIFAR and Mila, the AI Insights for Policymakers Program connects decision-makers with leading AI researchers through office hours and policy feasibility testing. The next session will be held on October 9 and 10.
Hugo Larochelle appointed Scientific Director of Mila
An adjunct professor at the Université de Montréal and former head of Google's AI lab in Montréal, Hugo Larochelle is a pioneer in deep learning and one of Canada’s most respected researchers.
Mila is hosting its first quantum computing hackathon on November 21, a unique day to explore quantum and AI prototyping, collaborate on Quandela and IBM platforms, and learn, share, and network in a stimulating environment at the heart of Quebec’s AI and quantum ecosystem.
This new initiative aims to strengthen connections between Mila’s research community, its partners, and AI experts across Quebec and Canada through in-person meetings and events focused on AI adoption in industry.
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In this review, we aim to inspire research into 001 S elf-S upervised S hared S emantic S pace ( S5 ) 002 multimodal learning problems. We e… (see more)quip non-003 expert researchers with a framework of in-004 formed modeling decisions via an extensive 005 literature review, an actionable modeling check-006 list, as well as a series of novel zero-shot eval-007 uation tasks. The core idea for our S5 check-008 list lies in learning contextual multimodal in-009 teractions at various granularity levels via a 010 shared Transformer encoder with a denoising 011 loss term, which is also regularized by a con-012 trastive loss term to induce a semantic align-013 ment prior on the contextual embedding space. 014 Essentially, we aim to model human concept 015 understanding and thus learn to “put a name to 016 a face”. This ultimately enables interpretable 017 zero-shot S5 generalization on a variety of 018 novel downstream tasks. In summary, this re-019 view provides sufficient background and ac-020 tionable strategies for training cutting-edge S5 021 multimodal networks. 022
Seeing things or seeing scenes: Investigating the capabilities of V&L models to align scene descriptions to images
Images can be described in terms of the objects 001 they contain, or in terms of the types of scene 002 or place that they instantiate. In t… (see more)his paper we 003 address to what extent pretrained Vision and 004 Language models can learn to align descrip-005 tions of both types with images. We com-006 pare 3 state-of-the-art models, VisualBERT, 007 LXMERT and CLIP. We find that (i) V&L 008 models are susceptible to stylistic biases ac-009 quired during pretraining; (ii) only CLIP per-010 forms consistently well on both object-and 011 scene-level descriptions. A follow-up ablation 012 study shows that CLIP uses object-level infor-013 mation in the visual modality to align with 014 scene-level textual descriptions