You gonna pretend there isn’t research suggesting skip connections aren’t needed, and that anyway there’s dozens of ways in CV of dealing with that issue?
CODE
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Commitment to Developers and Customers
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We are going to do right by developers and our customers:
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Data AI Summit Call for Presentations Now Open
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Do you have compelling #GenerativeAI, data science, or ML insights? Or maybe you’ve crafted noteworthy features in open-source tech? The #DataAISummit community wants to hear from you. Call for Presentations is now open! Submissions end Jan 5, act now: https://
bit.ly/3sOkYt0 -
RAG Applications: Tech Stack, Tools, and Use Cases
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Have you created/used RAG applications or learned to build them? What tech stack or libraries are you using for your RAG apps? Vector stores, databases, APIs… And what do you use #RAG for?
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Latent space sequence-to-sequence translation advantages explained
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what do you think are the advantages of doing this kind of sequence-to-sequence translation in latent space?
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Tips for Learning AI: Practical Application and Diverse Learning Methods
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Here are some tips I gathered overtime for learning AI (and pretty much anything): 1. Practical Application: Emphasize building projects and applying what you learn. 2. Use diverse Learning Modalities: Listen, watch, read, and DO for a better learning experience.
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Pretrained Embeddings Optimization Insights and Implementation
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super cool! I wonder if it might work better if you work in the space of a pretrained embedding. care to share some insight on why this doesn't work so well initially?
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ControlNet helps control generative AI model outputs
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I'd add that controlnet helps reign it in a bit
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Embedding to Text Reconstruction: Three-Step Technical Approach
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– step 1 can be implemented with any off-the-shelf embedder
– step 3 is solved with vec2text (
http://
github.com/jxmorris12/vec
2text
…)
– step 2 doesn't exist yet – some tasks might be solvable even with a simple embedding-to-embedding linear mapping if you're interested in trying this, DM me 🙂 -
Text Diffusion in Embedding Space for Sequence Tasks
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cool research idea for someone: text diffusion in embedding space solve any sequence-to-sequence task in three steps:
1. embed source sentence text
2. build diffusion model that maps input text embedding to target text embedding
3. invert to produce target text