[Oct 24th 2018] LabMeeting: Some Approaches to Learning of Logical Constraints

Francesco Giannini (DIISM, University of Siena) Oct 24, 2018 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description While learning from constraints is a main topic in artificial intelligence, the problem of learning constraints from exam- ples has received less attention. In this talk, we focus on constraints expressed by logical formulas […]

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dante vs macchina

PoemGen

Is poetry a peculiarity of human beings only? Can machines learn to generate poems that actually convey deep emotional meanings, just like human poets do? A lot of work is still necessary to reach such goal, here we present a simple demo in which we trained two models to generate tercets and verses respectively learning […]

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[Oct 11th 2018] LabMeeting: Perfect Neuron Building

Alessandro Betti (DIISM, Universities of Florence and Siena) Oct 11, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description By and large, Backpropagation (BP) is regarded as one of the most important neural computation algorithms at the basis of the impressive progress in machine learning, including the recent advances in deep […]

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[Oct 4th 2018] LabMeeting: On the notion of sparsity in neural networks

Vincenzo Laveglia (DIISM, Universities of Florence and Siena) Oct 4, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description In this preliminary work we try to formalize the notion of sparsity in neural networks defining an appropriate indicator that we call sparsity index (SI), and investigate how this indicator evolves during […]

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DeepHarvest

Do you want to challenge a Deep Reinforcement Learning based algorithm? If yes, then DeepHarvest is a very good opportunity! >> ONLINE DEMO

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[ Sept 13th 2018] LabMeeting: Emotion recognition from audio

Lisa Graziani (DIISM, Universities of Florence and Siena) Sept 13, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Emotion recognition from audio is a widely studied topic, but is still very challenging because is not entirely clear which features are effective for the recognition task. Moreover voice features keep on […]

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Visual Attention Modeling

Computational models of visual attention are at the crossroad of disciplines like cognitive science, computational neuroscience, and computer vision. When eye-tracking devices are not a viable option, models of human attention can be used to predict fixations.  Not only humans are correlated in terms of the locations they fixate, but they also agree somewhat in the […]

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