Scaling and evaluating sparse autoencoders
← Back to all posts
Thread (60 posts)
Initialization for optimizer mostly seems to follow Towards Monosemanticity
Post not found
Using the referenced paper, can you unpack each of the bullet points in §A.1 into a few-paragraphs explanation?
Claude 4 Opus (T) via @hdevalence.bsky.social
Thinking Summary
The user is asking me to look at Section A.1 of the "Scaling and evaluating sparse autoencoders" paper and unpack each bullet point with a few paragraphs of explanation. Let me find Section A.1 in the paper. Looking through the content, I can see references to Appendix A for optimization details. Let me search for Section A.1 specifically. From the paper extract, I can see mentions of Section A.1 and Section A.2, but the full content of these sections isn't included in the extract. However, there are several references to initialization schemes and optimization details throughout the paper. The paper mentions: - Section A.1 appears to be about initialization schemes - Section A.2 is about auxiliary losses for preventing dead latents - There's a mention that "in the infinite width limit, TopK autoencoders with our initialization scheme are actually optimal at init using our init scheme (Section A.1)" Since the full Section A.1 isn't provided in the extract, I should inform the user that I don't have access to the complete content of that section. However, I can discuss what the paper says about initialization based on the references throughout the document.
I don't have access to the complete Section A.1 from the paper in the provided extract. The content appears to be truncated and doesn't include the full appendix sections. However, based on references throughout the paper, I can provide context about what Section A.1 likely covers:
Initialization Schemes (Section A.1 Context)
From the paper's references to Section A.1, it appears to focus on initialization methods for sparse autoencoders. The paper mentions several key aspects:
Encoder-Decoder Transpose Initialization
The paper states they "initialize the encoder to the transpose of the decoder" as one of their key techniques for preventing dead latents. This initialization strategy likely helps ensure that the encoder and decoder start with compatible representations, potentially reducing the likelihood that certain latents will fail to activate during training. In traditional autoencoders, this transpose initialization can help maintain a reasonable reconstruction quality from the start of training.
Optimal Initialization for TopK in Infinite Width
The paper makes an intriguing claim that "in the infinite width limit, TopK autoencoders with our initialization scheme are actually optimal at init." This suggests that their initialization method has special theoretical properties when the autoencoder has infinite latents. This could mean that in the limit of infinite features, their initialization already provides perfect reconstruction before any training, which would be a remarkable property.
Connection to Dead Latent Prevention
The initialization scheme appears to be crucial for preventing dead latents - features that stop activating entirely during training. The paper reports achieving only 7% dead latents even in their 16 million latent autoencoder, which they attribute partly to their initialization strategy combined with auxiliary losses.
Without the full Section A.1 text, I cannot provide the specific bullet points and their detailed explanations. To get the complete information, you would need access to the full paper including all appendices. The archived version or full PDF would contain these details that aren't included in this extract.