Thomas Gauthier A complex analytic approach to sparsity, rigidity and uniformity 55:59 Terence Tao Infinite Partial Sumsets in the Primes (NTWS 160) 43:50 Solymosi_Recording 44:38 Siksek_Recording 57:31 Skorobogatov_Recording 43:58 Wei Zhang Diagonal cycles some results and conjectures ...
You might also want to impose constraints to induce sparsity on what you actually hold, in order to minimize transaction costs. In saying that your portfolio is mean-variance optimal, there’s the assumption that the returns you’re working with is normal, which is definitely not the case. ...
aThis does not mean you can say, “I prefer solely positive feedback. No criticism, please.” It does mean you can say, “I would love your suggestions for how to make my presentations better, and I’ll be able to hear them best and learn the most if you give them to me afterward...
What Does Compressed Sensing Mean? Compressed sensing is an approach to signal processing that allows for signals and images to be reconstructed with lower sampling rates than with Nyquist’s Law. This makes signal processing and reconstruction much simpler and has a wide variety of applications in...
“Greater love has no one than this: to lay down one’s life for one’s friends. You are my friends if you do what I command. I no longer call you servants, because a servant does not know his master’s business. Instead, I have called you friends, for everything that I learned...
In other words, we are trying to find the vectors uu and vv such that their rank-1 outer product is as close to the data matrix XX with respect to the sparsity promoting norm ∥⋅∥1‖⋅‖1. This will result in S=X−uv⊤S=X−uv⊤ being sparse, as desired, and result ...
Lasso regression adds a penalty term that encourages sparsity in the coefficient values. This results in some coefficients becoming exactly zero, effectively performing feature selection and excluding irrelevant variables. Logistic Regression:It is used when the dependent variable is binary or categorical....
Short texts however – such as search queries, tweets or instant messages – suffer from data sparsity, which causes problems for traditional topic modeling techniques.” It’s a mistake to use the above research paper as proof that Google uses LSI as an important ranking factor. The paper is...
This estimate is quite explicit, does not need the subtraction of the mean value, does not need convexity of , but also does not obey the scaling (which is of no surprise since we used the condition which also does not obey this scaling). In dimension the estimate takes the simpler form...
but there are certain things I know about that are super powerful that they've never done and vice versa. They probably know super powerful stuff that I've never done. So, it's kind of makes you wonder if there is only one 20%. Does that mean everybody has to arrive in the same ...