nhaliday + acm + approximation   14

Sequence Modeling with CTC
A visual guide to Connectionist Temporal Classiﬁcation, an algorithm used to train deep neural networks in speech recognition, handwriting recognition and other sequence problems.
acmtariat  techtariat  org:bleg  nibble  better-explained  machine-learning  deep-learning  visual-understanding  visualization  analysis  let-me-see  research  sequential  audio  classification  model-class  exposition  language  acm  approximation  comparison  markov  iteration-recursion  concept  atoms  distribution  orders  DP  heuristic  optimization  trees  greedy  matching  gradient-descent
december 2017 by nhaliday
Rank aggregation basics: Local Kemeny optimisation | David R. MacIver
This turns our problem from a global search to a local one: Basically we can start from any point in the search space and search locally by swapping adjacent pairs until we hit a minimum. This turns out to be quite easy to do. _We basically run insertion sort_: At step n we have the first n items in a locally Kemeny optimal order. Swap the n+1th item backwards until the majority think its predecessor is < it. This ensures all adjacent pairs are in the majority order, so swapping them would result in a greater than or equal K. This is of course an O(n^2) algorithm. In fact, the problem of merely finding a locally Kemeny optimal solution can be done in O(n log(n)) (for much the same reason as you can sort better than insertion sort). You just take the directed graph of majority votes and find a Hamiltonian Path. The nice thing about the above version of the algorithm is that it gives you a lot of control over where you start your search.
techtariat  liner-notes  papers  tcs  algorithms  machine-learning  acm  optimization  approximation  local-global  orders  graphs  graph-theory  explanation  iteration-recursion  time-complexity  nibble
september 2017 by nhaliday
Lecture 14: When's that meteor arriving
- Meteors as a random process
- Limiting approximations
- Derivation of the Exponential distribution
- Derivation of the Poisson distribution
- A "Poisson process"
nibble  org:junk  org:edu  exposition  lecture-notes  physics  mechanics  space  earth  probability  stats  distribution  stochastic-processes  closure  additive  limits  approximation  tidbits  acm  binomial  multiplicative
september 2017 by nhaliday