Ce programme soutient les startups spécialisées en IA à tout moment de l'année. Bénéficiez de ressources de pointe et d'un accompagnement sur mesure pour accélérer le développement de votre technologie.
Offert par Mila et le Forum des politiques publiques, ce programme est conçu pour outiller les décideur·euse·s et les responsables des politiques publiques à naviguer efficacement à travers les opportunités et les risques liés à l'IA. La prochaine cohorte se tiendra en français les 1er et 2 septembre 2026 à Mila.
Échangez avec les conseiller·ère·s académiques de Mila ainsi que des étudiant·e·s-chercheur·euse·s pour en savoir plus sur la communauté de Mila et découvrir comment nous rejoindre les 19 et 31 août et le 11 septembre 2026.
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Lecteur Multimédia
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Publications
Co-developing The Canadian MPS Registry: A longitudinal rare disease patient registry
The prediction of appliances energy consumption in building belongs to time series forecasting problem, which can be solved by echo state ne… (voir plus)twork (ESN). However, due to the randomly initialized inputs and reservoir, some redundant or irrelevant components are inevitably generated in original ESN. To solve this problem, the adaptive sparse deep echo state network (ASDESN) is proposed, in which the information is processed layer by layer. Firstly, the principal component analysis (PCA) layer is inserted to penalize the redundant projection transmitted between sub-reservoirs. Secondly, the coordinate descent based adaptive sparse learning method is proposed to generate the sparse output weights. Particularly, the designed adaptive threshold strategy is able to enlarge the sparsity of output weights as network depth increases. Moreover, the echo state property (ESP) of ASDESN is given to ensure its applications. The experiment results in both simulated benchmark and real appliances energy datasets illustrate that the proposed ASDESN outperforms other ESNs with higher prediction accuracy and stability.
2024-01-31
IEEE transactions on consumer electronics (publié)
Heterogeneous ensemble prediction model of CO emission concentration in municipal solid waste incineration process using virtual data and real data hybrid-driven
Properties and Styles of Software Technology Tutorials
Deeksha M. Arya
Jin L.C. Guo
Martin P. Robillard
A large number of tutorials for popular software development technologies are available online, and those about the same technology vary wid… (voir plus)ely in their presentation. We studied the design of tutorials in the software documentation landscape for five popular programming languages: Java, C#, Python, Javascript, and Typescript. We investigated the extent to which tutorial pages, i.e. resources, differ and report statistics of variations in resource properties. We developed a framework for characterizing resources based on their distinguishing attributes, i.e. properties that vary widely for the resource, relative to other resources. Additionally, we propose that a resource can be represented by its resource style, i.e. the combination of its distinguishing attributes. We discuss three techniques for characterizing resources based on our framework, to capture notable and relevant content and presentation properties of tutorial pages. We apply these techniques on a data set of 2551 resources to validate that our framework identifies valid and interpretable styles. We contribute this framework for reasoning about the design of resources in the online software documentation landscape.
2024-01-31
IEEE Transactions on Software Engineering (publié)
In the current era, the Transdermal delivery of bioactive molecules has become an area of research interest. The transdermal route of admini… (voir plus)stration enables direct entry of bioactive molecules into the systemic circulation with better and easy accessibility, bypassing the hepatic metabolism and improving patient compliance. Permeation through the skin has always been a barrier. To overcome this challenge, an efficient route by the vesicular system has been adopted so as to have better skin permeation of the bioactive molecules. A novel vesicular and non-invasive drug delivery system called Nanoethosomes was developed. Nanoethosomes are lipid-based vesicular carriers that are used for deeper permeation of the bioactive agents into the skin. The main components of Nanoethosomes are Phospholipids, water, and ethanol. High ethanol concentration in Nanoethosomes distinguishes them from other nano-formulation and results in deeper permeation and smaller vesicular size. This review article gives detailed information on the formulation techniques, and characterization parameters of nanoethosomes along with the research work done by various researchers in the same field. The compiled manuscript gives detailed elaboration about the various drugs used to treat different diseases which when incorporated in nanoethosomes resulted in better permeability and enhanced bioavailability.