It's nice to see sparse interpretable LLMs being made. This is similar to factor rotation in factor analysis (or PCA). A varimax rotation, for example, can produce an equivalent factor analysis with sparse loadings, and…
This advice can also be applied to PhD thesis examinations and paper reviews.
My dad is in his 80s. He keeps careful notes on how to use devices like tablets and TVs. There might be a touch of engineer-brain at work here, but the struggle is very real. He generally wouldn't take in all of the…
For linear models, least squares leads to the BLUE estimator: Best Linear Unbiassed Estimator. This acronym is doing a lot of work with each of the words having a specific technical meaning. Fitting the model is also…
A note mostly about terminology: The least squares model will produce unbiassed predictions of y given x, i.e. predictions for which the average error is zero. This is the usual technical definition of unbiassed in…
535.491…^i = 1
R is so good in part because of the efforts of people like Di Cook, Hadley Wickham, and Yihui Xie to create an software environment that they like working in. It also helps that in R any function can completely change…
Producing a diverse list of results may still help in a couple of ways here. * If there are a lot of lexical matches, real semantic matches may still be in the list but far down the list. A diverse set of, say, 20…
Here's a scenario. You're running a cluster, and your users are biologists producing large datasets. They need to run some very specific command line software to assemble genomes. They need to edit SLURM scripts over…
What a strange web page. Scrolling is thoroughly broken. I recently went to a two day workshop on whole cell modelling. I'm still trying to work out how much of the exercise is fantasy. I get that some of the chemistry…
Thanks for this. Despite the vintage this seems very clearly written, the introductory material on measure theory has already made it worthwhile for me.
As others have said in various ways, start by fitting a survival model using glmnet. That said, here are some folks trying to use SDEs to model cells, they even have a "dW" on their logo. This is a long way from…
A further step is Langevin Dynamics, where the system has damped momentum, and the noise is inserted into the momentum. This can be used in molecular dynamics simulations, and it can also be used for Bayesian MCMC…
It could also be that the aspect of personality that causes people to think Kagi is better also causes those people to buy it.
Brownian motion mentioned at the end of the article and more generally Langevin Dynamics are incredibly useful. They're this weird interface between normal physics and statistical mechanics. When a big complex molecule…
If you plot x^y against x+y, you get a Sierpinski triangle.
The article mentions equivalent ranking from cosine similarity and Euclidean distance. The derivation is very simple. For vectors A and B, the squared Euclidean distance is: (A-B).(A-B) = A.A-2A.B+B.B A and B only…
For web-servers on remote machines, I have found this useful: socat TCP4-LISTEN:1234,fork,bind=127.0.0.1 EXEC:'ssh my.remote.server nc 127.0.0.1 1234' 1234 = local/remote port. Can be adapted to use unix sockets at the…
It's nice to see sparse interpretable LLMs being made. This is similar to factor rotation in factor analysis (or PCA). A varimax rotation, for example, can produce an equivalent factor analysis with sparse loadings, and…
This advice can also be applied to PhD thesis examinations and paper reviews.
My dad is in his 80s. He keeps careful notes on how to use devices like tablets and TVs. There might be a touch of engineer-brain at work here, but the struggle is very real. He generally wouldn't take in all of the…
For linear models, least squares leads to the BLUE estimator: Best Linear Unbiassed Estimator. This acronym is doing a lot of work with each of the words having a specific technical meaning. Fitting the model is also…
A note mostly about terminology: The least squares model will produce unbiassed predictions of y given x, i.e. predictions for which the average error is zero. This is the usual technical definition of unbiassed in…
535.491…^i = 1
R is so good in part because of the efforts of people like Di Cook, Hadley Wickham, and Yihui Xie to create an software environment that they like working in. It also helps that in R any function can completely change…
Producing a diverse list of results may still help in a couple of ways here. * If there are a lot of lexical matches, real semantic matches may still be in the list but far down the list. A diverse set of, say, 20…
Here's a scenario. You're running a cluster, and your users are biologists producing large datasets. They need to run some very specific command line software to assemble genomes. They need to edit SLURM scripts over…
What a strange web page. Scrolling is thoroughly broken. I recently went to a two day workshop on whole cell modelling. I'm still trying to work out how much of the exercise is fantasy. I get that some of the chemistry…
Thanks for this. Despite the vintage this seems very clearly written, the introductory material on measure theory has already made it worthwhile for me.
As others have said in various ways, start by fitting a survival model using glmnet. That said, here are some folks trying to use SDEs to model cells, they even have a "dW" on their logo. This is a long way from…
A further step is Langevin Dynamics, where the system has damped momentum, and the noise is inserted into the momentum. This can be used in molecular dynamics simulations, and it can also be used for Bayesian MCMC…
It could also be that the aspect of personality that causes people to think Kagi is better also causes those people to buy it.
Brownian motion mentioned at the end of the article and more generally Langevin Dynamics are incredibly useful. They're this weird interface between normal physics and statistical mechanics. When a big complex molecule…
If you plot x^y against x+y, you get a Sierpinski triangle.
The article mentions equivalent ranking from cosine similarity and Euclidean distance. The derivation is very simple. For vectors A and B, the squared Euclidean distance is: (A-B).(A-B) = A.A-2A.B+B.B A and B only…
For web-servers on remote machines, I have found this useful: socat TCP4-LISTEN:1234,fork,bind=127.0.0.1 EXEC:'ssh my.remote.server nc 127.0.0.1 1234' 1234 = local/remote port. Can be adapted to use unix sockets at the…