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SUMMARY:Séminaire du D2 par Luigi Liquori
DESCRIPTION:Title : « Why reductions have to be synchronised in intersection (and union) typed lambda-calculi ? »\nLuigi Liquori\, Inria Sophia-Antipolis\n \n\n\nAbstract:\n\nWe present the ∆-calculus\, an explicitly typed λ-calculus with strong pairs\, projections and explicit type coercions. The calculus can be parametrized with different intersection type theories\, as described in the Barendregt-Dekker-Statman book on λ-calculi with types\, producing a family of ∆-calculi with related intersection typed systems. We show why annotating pure λ-calculus with intersection types is not easy: a classical example is the difficulty to decorate the bound variable of the explicitly typed polymorphic identity λx: ?.x such that the type of the identity is (σ → σ) ∩ (τ → τ ): previous attempts showed that the full power of the intersection type discipline can be easily lost. We show why intersection typed systems need a kind of synchronised reduction to fix the subject reduction theorem. The same problematics also appear when decorating λ-calculus with union-types. Finally\, we show how the ∆-calculus can be raised to a ∆-framework by adding dependent-types as in the Edinburgh Logical Framework.
URL:https://www.loria.fr/event/seminaire-du-d2-par-luigi-liquori/
CATEGORIES:Séminaire
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DTSTART;TZID=Europe/Paris:20200206T133000
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DTSTAMP:20200115T091902Z
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SUMMARY:Séminaire SSL par Adi Shamir
DESCRIPTION:Le prochain séminaire sera un peu particulier puisqu’il s’agira de la projection de l’exposé invité donné en septembre par Adi Shamir dans le cadre de la conférence Esorics 2019. \nThe European Symposium on Research in Computer Security \nLa projection aura lieu le jeudi 6 février\, à 13h30\, dans l’amphithéâtre du Loria. \nTitre : The Insecurity of Machine Learning: Problems and Solutions \nAbstract : The development of deep neural networks in the last decade had revolutionized machine learning and led to major improvements in the precision with which we can perform many computational tasks. However\, the discovery five years ago of adversarial examples in which tiny changes in the input can fool well trained neural networks makes it difficult to trust such results when the input can be manipulated by an adversary. \nThis problem has many applications and implications in object recognition\, autonomous driving\, cyber security\, etc\, but it is still far from being understood. In particular\, there had been no convincing explanations why such adversarial examples exist\, and which parameters determine the number of input coordinates one has to change in order to mislead the network. \nIn this talk I will describe a simple mathematical framework which enables us to think about this problem from a fresh perspective\, turning the existence of adversarial examples in deep neural networks from a baffling phenomenon into an unavoidable consequence of the geometry of R^n under the Hamming distance\, which can be quantitatively analyzed.
URL:https://www.loria.fr/event/seminaire-ssl-par-adi-shamir/
CATEGORIES:Séminaire
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