[Lisa_seminaires] UdeM-McGill-MITACS machine learning seminar Fri Nov. 20 at 14h30,

Dumitru Erhan dumitru.erhan at umontreal.ca
Jeu 19 Nov 14:31:02 EST 2009


Reminder for tomorrow's seminar @ 14:30:

On Thu, Nov 12, 2009 at 14:05, Dumitru Erhan <dumitru.erhan at umontreal.ca>wrote:

> Next week's seminar (see
> http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en):
>
> Unlocking Brain-Inspired Computer Vision: a Multi-Disciplinary,
> High-Throughput Approach
>
> by Nicolas Pinto
> Department of Brain and Cognitive Sciences
> Massachusetts Institute of Technology
>
> Location: Pavillon André-Aisenstadt (UdeM), room 3195
> Time: Friday, November 20, 14h30
>
> The construction of artificial vision systems and the study of
> biological vision are naturally intertwined as they represent
> simultaneous efforts to forward and reverse engineer systems with
> similar goals. While exploration of the neuronal substrates of visual
> processing provides clues and inspiration for artificial systems,
> artificial systems can in turn serve as important generators of new
> ideas and working hypotheses. However, while systems neuroscience has
> so far provided inspiration for some of the "broad-stroke" properties
> of the visual system (e.g. hierarchical organization, synaptic
> integration of inputs and threshold, normalization, plasticity, etc),
> much is still unknown. Even for those qualitative properties that most
> biological-inspired models hold in common, experimental data currently
> provide little constraint on their key parameters. Consequently, it is
> difficult to truly evaluate a set of computational ideas, since the
> performance of any one model depends strongly on its particular
> instantiation - e.g. the size of the pooling kernels, the number of
> units per layer, exponents in normalization operations, etc. Since the
> number of such parameters (explicit or implicit) is very large, and
> the typical computational cost of evaluating one particular model is
> high, the space of possible model instantiations usually goes largely
> unexplored. Compounding the problem, even if a set of computational
> ideas are on the right track, the instantiated "scale" of those ideas
> is typically small (e.g. in terms of dimensionality and amount of
> learning experience provided). Thus, when a model fails to approach
> the abilities of the visual system, we are left uncertain whether this
> failure is because we are missing a fundamental idea, or because the
> correct "parts" have not been tuned correctly, assembled at sufficient
> scale, or provided with sufficient natural experience.
>
> To pave a possible way forward, we have begun developing a
> high-throughput approach to expansively explore a large range of
> biologically-inspired models - including models of larger, more
> realistic scale - leveraging recent advances in commodity stream
> processing hardware (high-end GPUs and Playstation 3’s Cell
> processors) and scientific cloud computing (e.g. Amazon EC2). In
> analogy to high-throughput screening approaches in molecular biology
> and genetics, we generated and trained thousands of potential network
> architectures and parameter instantiations, and "screened" the visual
> representations produced by these models using an object recognition
> task. From these candidate models, the most promising were selected
> for further analysis. We have shown that this approach can yield
> significant, reproducible gains in performance across an array of
> basic object recognition tasks, consistently outperforming a variety
> of state-of-the-art purpose-built vision systems from the literature,
> and that it can offer insight into which computational ideas are most
> important for achieving this performance.
>
> As the scale of available computational power continues to expand, we
> believe that this approach holds great potential both for accelerating
> progress in artificial vision, and for generating new,
> experimentally-testable hypotheses for the study of biological vision.
>



-- 
http://dumitru.ca, +1-514-432-8435
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