baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

pchiusano

-- Type-aligned sequence catenable queue supporting O(1) append, snoc, uncons
-- Don't need it to be a dequeue (unsnoc not needed)
data TCQueue c a b -- c is the category, a is starting type, b is ending type

type Channel f a b = TCQueue (Transition f) a b
type Process f b = Channel f () b

data Transition f a b where
  Bind :: (a -> Process f b) -> Transition f a b
  OnHalt :: (Cause -> Process f b) -> Transition f a b 

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

mandubian

Class Rectangle

val rectWrites = (
  (__ \ 'width).write[Long] and
  (__ \ 'height).write[Long] and
  (__ \ 'x).write[Long] and
  (__ \ 'y).write[Long]
)( (r: Rectangle) => (r.width, r.height, r.x, r.y)  )

mandubian

mon implementation à moi qui tue!