The DeepBach model provides a novel way to compose Bach chorales in an interactive manner. In this seminar, we discuss how to extend this model so that it handles other music genres such as traditionnal folk tunes or jazz songs.
Theoretical models of critical mass have shown how minority groups can initiate social change dynamics in the emergence of new social conventions. Here, we study an artificial system of social conventions in which human subjects interact to establish a new coordination equilibrium. The findings provide direct empirical demonstration of the existence of a tipping point in the dynamics of changing social conventions.
When minority groups reached the critical mass—that is, the critical group size for initiating social change—they were consistently able to overturn the established behavior. The size of the required critical mass is expected to vary based on theoretically identifiable features of a social setting. Our results show that the theoretically predicted dynamics of critical mass do in fact emerge as expected within an empirical system of social coordination.
Is it possible to capture the socio-economic footprint of the human behavior in our cities or neighborhoods? Nowadays, all human activities, ranging from the people we call, the places we visit, the things we eat and the products we buy, generates data. This can be analyzed over long periods to paint a comprehensive portrait of human behavior within the city boundaries. These geolocated digital traces, when combined with other information streams from national census, or google api, can be used to extract information about the potential needs and the routines in the collective behavior of different groups of citizens. We will analyze this data to understand the extent to which the urban activities of different population groups or communities are driven by both socio-economic differences and cities’ structure. This new quantitative approach will provide new insights for more inclusive policies to help future urban development.
In large musical catalogs such as in streaming companies, manual curation comes at high cost and the amount of data is considerable with tens of thousands of records delivered every week.
Automatic systems trained directly from audio data help streaming companies describing audio recordings in their catalogs as well as creating relations between them.
We will take a look at what is done by Deezer R&D's team in this domain using machine learning techniques, especially representation learning ones.
Outstanding supervised classification performances obtained by CNNs indicate they have the ability to create relevant invariants for classification. We show that this can be achieved through progressive invariance incorporation and as well via perfectly invertible architectures. Illustrations are given through Hybrid Scattering Networks, based on a geometric representation, and $i$-RevNets, a class of invertible CNNs. We explicit several empirical properties, like progressive linear separability, in order to shed light on the inner mechanisms implemented by CNNs.
Music mixing is the process of combining multitrack recordings into a final product. Sony CSL is involved in music mixing through the AutoMix and DAWGen projects. Beyond making the music merely audible, what is the purpose of mixing? Citing many examples within five categories, we explore a variety of aspects music mixing can address.
LEGO bricks are among the most popular toys for children (and adults) and they also are well known tools capable of fostering individual creativity and problem solving skills.
In relatively recent times, some scientific works exploited LEGO bricks for a wide variety of different purposes, from the measurement of cognitive effects on problem solving in social sciences to the representation of molecular structures, while in some cases they became part of the experimental apparatus.
In this presentation I will talk about my past work with LEGO Bricks, starting from the first experiments on collective creativity during free building events that took place in Rome. From these experiments, we started to develop a new interactive experience in which the “free building” task is replaced by the task of finding sustainable solutions for problems related to urban environments. This new experiment requires a realistic modeling framework of the dynamics of the cities. I will conclude presenting the current issues and research questions related to it.
Nowadays dealing with R&I implies not only looking after the
scientific value of our work, but also having clear in mind the
effects of what we are doing for society in a wide perspective: from policymakers to enterprises, up to citizens. Research and innovation therefore should be able to come out from labs and create bridges with their reference context.
To this regard, the challenge is to be able of creating economic, social, cultural and environmental impact looking at them as a measurement of "change".Through the seminar we will share and talk about some tools, such as logical framework matrix, and some concepts, like Responsible Research and Innovation, to comply with this issue.
About fifty years ago, linguistics played a central role in cognitive science and its insights were highly influential for developments in models and applications of natural language processing. Today, language is still seen as a major issue, but all recent breakthroughs in language studies - particularly in the fields of computational linguistics and artificial intelligence - have been achieved without influence from developments in linguistics. That is unfortunate, because the most powerful language technologies today are still incapable of understanding natural language, and they would greatly benefit from more linguistic sophistication.
In this presentation, I will present how “constructional approaches” to language can put linguistics back on the map of cognitive science and how it can help linguistics to make claim to the position of the science of natural language processing. More specifically, I will present our work on Fluid Construction Grammar, the world’s most advanced computational platform for constructional language processing, which intends to achieve both deep semantic parsing and adequate production using the same linguistic inventories.
Possible issues with FFT:
• Choice of network architecture?
• 1D input data (1 input vector = 1 window) or 2D input data (1 input vector = several reshaped windows)?
• Recurrent or non-recurrent network? Non-recurrent possible in 2D case.
• In case of 2D input data, is reshaping important? (eg. consequences in case of convolutional network)
Possible issues with CWT:
• Due to high dimensionality of CWT, is it feasible to set it as input of NN?
• If it’s feasible, what reshaping?
• Can we imagine a recurrent NN architecture with different time-scales [Alpay 2016] to suit CWT input?