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- Testnet Faucet | Circle1 savers
- On the Biology of a Large Language Model32 savers
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- A Beginner's Guide To Understanding Convolutional Neural Networks Part 2 – Adit Deshpande – Engineering at Forward | UCLA CS '191 savers
- A Beginner's Guide To Understanding Convolutional Neural Networks – Adit Deshpande – Engineering at Forward | UCLA CS '191 savers
highlights · 7
intervention experiments in the original model, such as inhibiting feature groups and observing their effects on other features and on the model’s output (final figure panel above – percentages indicate fraction of original activation)
On the Biology of a Large Language Modelfeature visualization consisting of different examples of text where the feature activates, we can give each feature a human-interpretable label
On the Biology of a Large Language Modelabout a quarter of the prompts
On the Biology of a Large Language Modelafter which it continues to do so due to pressure to adhere to syntactic and grammatical rules
On the Biology of a Large Language Modelattribution graphs, which allow us to partially trace the chain of intermediate steps that a model uses to transform a specific input prompt into an output response.
On the Biology of a Large Language Modelow when you apply a set of filters on top of that (pass it through the 2nd conv layer), the output will be activations that represent higher level features.
A Beginner's Guide To Understanding Convolutional Neural Networks – Adit Deshpande – Engineering at Forward | UCLA CS '19Now, this is a little bit harder to visualize. When we were talking about the first layer, the input was just the original image. However, when we’re talking about the 2nd conv layer, the input is the activation map(s) that result from the first layer. So each layer of the input is basically describing the locations in the original image for where certain low level features appear.
A Beginner's Guide To Understanding Convolutional Neural Networks – Adit Deshpande – Engineering at Forward | UCLA CS '19