ALPS – Advanced Language Processing winter school in the French Alps
Seminar: Learning with sparse latent structure
Blog: Robot learning workshop at NeurIPS2020 – podcast with Julien Perez
Seminar: Vision for assistive artificial intelligence
By dissecting the matching process of the recent ColBERT model, we make a step towards unveiling the ranking properties of BERT-based ranking models and show that ColBERT (implicitly) learns a notion of term importance that correlates with IDF.
Running into the unknown with RunAhead: finding pleasant running tours and intuitively navigating through them. The use of combinatorial optimization and UX design, together with head-tracking technology, provides a non-intrusive way of helping runners to discover new itineraries.
A novel framework which uses individual sample losses as error measures to determine the relative difficulty of samples in a dataset. Can be plugged on top of existing neural network models to implement curriculum learning for any task, even with noisy datasets.
GLOBAL AI R&D BELT
ACADEMIA – EU/GOVT – ENTREPRENEURS
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