Early this year, Jacob Hilton from Alignment Research Center (ARC) posted about the problem that his team had been tackling, primarily around really mechanistically understanding small (< 1,500 ...

This is the second installment of the “Complexity Series,” where I endeavor to argue that there are certain classes of problems (simple arithmetic being one of them) that Transformer architectures ...

This is the first installment of the “Complexity Series”, where I endeavor to argue that there are certain classes of problems (simple arithmetic being one of them) that Transformer architectures w...

In MLPs, What Input Gives What Output? If you had a series of MLP (nn.Linear) layers chained together, you may like to answer the question: if I wanted the final layer outputs’ first element (call...

Thinking about how Sparse Auto-encoders (SAEs) aim to learn a sparse over-complete basis (where you are trying to triangulate a larger number of sources than you have signals; e.g. you only have 8 ...

After deep-diving into why SAEs succeed at retrieving superposed features, what their limitations are, and closely inspecting the hidden technical implementations of the sae_lens library, I just wa...

My previous blogpost gives a very clear visualization of how the latents of simple ReLU networks look like, how to interpret them, and a good description of optimization pressures that force them i...

In my previous post, “Superposition - An Actual Image of Latent Spaces”, I illustrate how the parameters of a toy ReLU auto-encoder ($W$, $b$, and $\text{ReLU}$), work together to allow models repr...

This post is the prequel of the next post, “Optimization Failure”, where I investigate how, even in cases where perfectly symmetric, ideal weight configurations exist, ReLU toy models (following An...

Introduction I recently came across an old lecture on High-Dimensional (HD) computing, in the forms of: This Quanta Magazine article This Stanford CS lecture by Pentti Kanerva And thought i...