numerical computing and library names

What this neuron does

This neuron responds to words in technical writing about maths software and numerical computing, such as "LAPACK", "linear algebra" and "computations". Steering did not give a clear theme; the outputs shifted toward matrices and maths.

Written by Claude from this neuron's activations and steering outputs. Confidence: medium. Think it is wrong? Flag it (opens a prefilled issue on GitHub).

Fires most on

The text windows where this neuron's activation was highest. Amber marks positive activation; the outlined token is the peak. Hover a token for its strength.

1. #1 peak

   rely on the standard BLAS and LAPACK routines. Intel® Math Kernel Library (Intel® MKL) provides a high performance implementation of BLAS and LAPACK that is optimized for Intel architectures. This webinar discusses building the open source NumPy

2. #2 peak

   language. Octave has extensive tools for solving common numerical linear algebra problems, finding the roots of nonlinear equations, functions written in Octave's own

3. #3 peak

   ⏎R I B U T E⏎Abramowitz started the⏎historic garment workers strike of⏎1910-11

4. #4 peak

   ence to corundum (emery) and other hard stones. According to Charles William King,

5. #5 peak

   |⏎- load exponent of a floating point number⏎c99 [ flag... ] file... -lm [ library... ] #include <math.h> double ldexp(double x, int exp);⏎float ldexpf(float x, int exp

6. #6 peak

   comparing linear, quadratic, and exponential models of real-life data; and automation of the computation of real zeros of polynomials where such calculations require implementation of time consuming iterative procedures. An iterative procedure based on the Rational Zeros Theorem will be used

Steering

Push this neuron up or down while the model writes, and watch what changes. The outputs were generated ahead of time, so nothing runs in your browser.

Most negative on

Where the activation went furthest below zero. Blue marks negative activation. Many neurons mean something different in each direction.

1. #1 peak

   analyses of experiments to improve the understanding of the phenomenology.⏎The Large Atomic/Molecular Massively Parallel Simulator (LAMMPS) is a Discrete Element Method (DEM) code developed at Sandia National

2. #2 peak

   has extensive tools for solving common numerical linear algebra problems, finding the roots of nonlinear equations, functions written in Octave's own language, or by using dynamically loaded modules written in C, C++, Fortran, or other languages.⏎Donations to

3. #3 peak

   that can provide great performance benefit. This webinar will give a quick tutorial on how to extend NumPy/SciPy and R by writing a wrapper for an Intel MKL function and then use it from NumPy/SciPy or R programs.

4. #4 peak

   high performance implementation of BLAS and LAPACK that is optimized for Intel architectures. This webinar discusses building the open source NumPy/SciPy and R with Intel MKL to significantly improve the performance of linear algebra operations. Beyond BLAS and LAP

Which neurons in earlier layers feed this one, and which later ones it feeds. Amber links excite, blue links suppress; longer bars are stronger.

Activation histogram

How often this neuron fires at each strength. Almost always it does close to nothing (the tall bars in the middle); the snippets above are the rare moments it fired hard, amber for positive and blue for negative.

Logit lens

The words this neuron nudges the model toward (amber) or away from (blue) when it fires. A hint, not a label: many neurons work through later layers instead.