Category: Uncategorized

• Gensim Tutorial:
  • https://radimrehurek.com/gensim/models/word2vec.html
  • Use google-news model as pre-trained model
  • clustering based on distance matrix
  • Question: how do we do the clustering?
    • should cluster on the keywords?
    • should cluster on the keywords-related words?
• Leg dissection demo:
  • 18 cameras 30frames 10G
  • 5 cameras 100 frames 6G
  • Question:
    • what is our task?
      • we cannot change focal length now. we can only change the viewpoint
      • if we want dynamic, we should have dynamic mesh?
• Foveated ray-tracing:
  • input: eye ray + 1spp
  • output: foveated image
  • question: If we use foveated image as ground truth, what should be the denoising algorithm for the ground truth?
• TODO:
  • read G3D code and change sample number
  • read papers (nvd, disney)
  • Homework

• Cross entropy
  • H(p,q) = D(p||q) + H(p)
    • H(p) is some inherent randomness in p
    • D(p||q) is what we care about. we can try to get D(p||q) by calculating cross entropy.
  • Conclusion: a model is good is that it assign good approximation to the observed data. So we need to find some good q
• Main points:
  • Example: She loves her. (It’s a correct string, but English is not like this. It should be “She loves herself.”)
  • We need a meaning pair.
  • Two orthogonal dimensions:
    • probability for the strings.

    • Units		Prob
      String	{anbn|n>=1}	P(w1, w2,…, wn)
      Structure	A tree structure	PCFG

    • L1 = L2: Language 1 is equal to Language 2
      • Weak equivalence
        • Sense of string are the same.
      • Strong equivalence
        • Structure of language 1 and 2 are the same.
        • G1 = G2 iff {x| G1 generates string x} = {x|G2 generates string x} (all and only the same structures)

        • G1	G2
          S->a s	s->s a
          s->e	s->e

        • G1 and G2 are weak equivalent (they generate the same strings) but not strong equivalent
  • Example: Jon loves mary
  • Questions:
    • How to measure equivalence?
    • binary judgements?
• EM
  • Question: How to find a good model? Expectation maximization (EM)
  • The structure of model is given, we need to find the parameters for the model.
  • Coin: H H H H T T T T T T
  •  
  • MLE: argmax [p(x|mu)]
    • 
    • Solve: 
    • Result: p = k/n
• HMM <A,B, pi>
  • pi: initial probabilities
  • N states
  • V words
  • recipe: http://www.umiacs.umd.edu/~resnik/ling773_sp2011/readings/em_recipe.v2.only_hmm.pdf
  • Three fundamental questions for EM:
    • What is P(O|mu)
    • Best hidden events given O, mu?
    • What’s the best model I can have? argmax_mu P(O|mu)

Basically, the time spent on testing depends on:

• the complexity of the neural network
  • For example, the fastest network should be the fully-connected network.
  • CNN should be faster than LSTM because LSTM is sequential (sequential = slow)
  • Currently, there are many ways to compress deep learning model (remove nodes with lighter weight)
• the complexity of data
  • Try to build parallel models.

Analyze of different deep learning models:

• RNN
  • CNN
  • LSTM
  • GRU
• Seq2seq:
  • used for NLP
• Autoencoder:
  • learn the features and reduce dimension’

Other machine learning models:

• random forest
  • used for classification

Machine learning thought:

CMake Warning at CMakeLists.txt:390 (MESSAGE):
Build of assimp_qt_viewer is disabled. Unsatisfied dendencies: Qt5 DevIL

https://dillinger.io/

Permutation:

http://blog.csdn.net/hackbuteer1/article/details/6657435

Tomorrow:

CORDIC

• FastLog
  • Finding integer log base 2 of an integer (aka the position of the highest bit set)
    • Find the log base 2 of an integer with the MSB N set in O(N) operations (the obvious way)
    • Find the integer log base 2 of an integer with an 64-bit IEEE float
    • Find the log base 2 of an integer with a lookup table
    • Find the log base 2 of an N-bit integer in O(lg(N)) operations
      • (Use This)
      • http://blog.csdn.net/stupid_haiou/article/details/45953007
    • Find the log base 2 of an N-bit integer in O(lg(N)) operations with multiply and lookup
  • Find integer log base 10 of an integer
  • Find integer log base 10 of an integer the obvious way
  • Find integer log base 2 of a 32-bit IEEE float
  • Find integer log base 2 of the pow(2, r)-root of a 32-bit IEEE float (for unsigned integer r)
• Question: Can glsl do bitwise operation?
  • Answer1: Can https://web.cs.ship.edu/~djmoon/cg/cg-notes/cg-glsl-language.pdf
  • Answer2: No.

Description:

https://leetcode.com/problems/k-diff-pairs-in-an-array/description

Code:

Time & Space:
O(nlog(n)) & O(n)

Description:

https://leetcode.com/problems/contains-duplicate/#/description

Algorithm:

1. Use sort, very easy
2. Don’t use sort, use a array to record the appearance of words. If a word appears for more than 1 times, return true.

Code1:

class Solution {
public:
bool containsDuplicate(vector<int>& nums) {
if (nums.size() < 2) return false;
sort(nums.begin(), nums.end());
for (int i = 0; i < nums.size() - 1;i++)
{
if (nums[i] == nums[i+1])
return true;
}
return false;
}
};

Code2:

class Solution {
public:
bool containsDuplicate(vector& nums) {
int min = INT_MAX;
int max = INT_MIN;
for (int i = 0; i < nums.size(); ++i) { if (nums[i] > max) {
max = nums[i];
}

if (nums[i] < min) { min = nums[i]; } } vector exists(max - min + 1, false);
for (int i = 0; i < nums.size(); ++i) { if (exists[nums[i] - min]) { return true; } else { exists[nums[i] - min] = true; } } return false; } };

Time & Space:

Code1:

Time: O(nlogn) Space O(1)

Code2:

Time: O(n), Space O(n)