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DTSTART;TZID=Europe/Berlin:20230105T160000
DTEND;TZID=Europe/Berlin:20230105T160000
DTSTAMP:20260423T205107
CREATED:20221215T134407Z
LAST-MODIFIED:20250603T130112Z
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SUMMARY:Peter Neri (Laboratoire Des Systèmes Perceptifs\, CNRS\, Paris)\, “The Unreasonable Recalcitrance of Human Vision to Theoretical Domestication”
DESCRIPTION:Abstract: \nWe can view cortex from two fundamentally different perspectives: a powerful device for performing optimal inference\, or an assembly of biological components not built for achieving statistical optimality. The former approach is attractive thanks to its elegance and potentially wide applicability\, however the basic facts of human pattern vision do not support it. Instead\, they indicate that the idiosyncratic behaviour produced by visual cortex is largely dictated by its hardware components. The output of these components can be steered towards optimality by our cognitive apparatus\, but only to a marginal extent. We conclude that current theories of visually-guided behaviour are at best inadequate\, and we turn to neural networks in an attempt to establish whether the idiosyncratic character of human vision may be learnt from a larger repertoire of functional constraints\, such as the statistics of the natural environment. We challenge deep convolutional networks with the same stimuli/tasks used with human observers and apply equivalent characterization of the stimulus–response coupling. For shallow depth of behavioural characterization\, some variants of network-architecture/training-protocol produce human-like trends; however\, more articulate empirical descriptors expose glaring discrepancies. Our results urge caution in assessing whether neural networks do or do not capture human behavior: ultimately\, our ability to assess ‘’success’’ in this area can only be as good as afforded by the depth of behavioral characterization against which the network is evaluated. More generally\, our results provide a compelling demonstration of how far we still are from securing an adequate computational account of even the most basic operations carried out by human vision. \nPhoto by Mathew Schwartz on Unsplash \nThis talk will take place in person at SCIoI. \n 
URL:https://www.scienceofintelligence.de/event/distinguished-speaker-series-peter-neri-laboratoire-des-systemes-perceptifs-cnrs-paris-the-unreasonable-recalcitrance-of-human-vision-to-theoretical-domestication/
CATEGORIES:Distinguished Speaker Series
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DTSTART;TZID=Europe/Berlin:20230119T160000
DTEND;TZID=Europe/Berlin:20230119T173000
DTSTAMP:20260423T205107
CREATED:20230102T111439Z
LAST-MODIFIED:20240813T101638Z
UID:13961-1674144000-1674149400@www.scienceofintelligence.de
SUMMARY:Ingmar Posner (University of Oxford)\, "Learning to Perceive and to Act - Disentangling Tales from (Structured) Latent Space"
DESCRIPTION:Abstract:\nUnsupervised learning is experiencing a renaissance. Driven by an abundance of unlabelled data and the advent of deep generative models\, machines are now able to synthesise complex images\, videos and sounds. In robotics\, one of the most promising features of these models – the ability to learn structured latent spaces – is gradually gaining traction. The ability of a deep generative model to disentangle semantic information into individual latent-space dimensions seems naturally suited to state-space estimation. Combining this information with generative world-models\, models which are able to predict the likely sequence of future states given an initial observation\, is widely recognised to be a promising research direction with applications in perception\, planning and control. Yet\, to date\, designing generative models capable of decomposing and synthesising scenes based on higher-level concepts such as objects remains elusive in all but simple cases. In this talk I will motivate and describe our recent work using deep generative models for unsupervised object-centric scene inference and generation. Furthermore\, I will make the case that exploiting correlations encoded in latent space\, and learnt through experience\, lead to a powerful and intuitive way to disentangle and manipulate task-relevant factors of variation. I will show that this not only casts a novel light on affordance learning\, but also that the same framework is capable of generating plans executable on complex real-world robot platforms. \nPhoto courtesy by Ingmar Posner. \nThis talk will take place in person at SCIoI. \n 
URL:https://www.scienceofintelligence.de/event/distinguished-speaker-series-ingmar-posner-university-of-oxford-learning-to-perceive-and-to-act-disentangling-tales-from-structured-latent-space/
CATEGORIES:Distinguished Speaker Series
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