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Do 3D reconstruction transformers really need a billion parameters, or are most of those layers just doing the same thing over and over? Introducing Déjà View: a single transformer block, looped K times, that matches or beats models 8–10× its size with lower compute. 🧵

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Tobias Fischer

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112,884 Aufrufe • vor 4 Monaten •via X (Twitter)

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and he’s right! love me a man of science 🤓 🍒 : with how i look at it.. the core and the erector spinae muscles.. those that support your lower back.. i think working on these areas consistently is good. even if you don't go as far as doing pilates or things like that.. just the basics! because eventually, if you lose strength on those muscles over time, it puts strain on your spinal discs. so you really have to work on your lower back muscles!

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Transformer by hand ✍️ ~ 6 steps walkthrough below Open the hood of a transformer and the parts list is overwhelming: embeddings, positional encoding, attention weighting, self-attention, cross-attention, multi-head attention, layer norm, skip connections, softmax, linear, Nx, shifted right, query, key, value, masking. Which of those actually make the car run? Two of them. Attention weighting and the feed-forward network. Everything else is an enhancement to make it run faster and longer, which is how we got from a car to a truck, and to the word "large" in large language model. So I drew and calculated those two parts entirely by hand. Goal: push five features through one transformer block, filling in every cell yourself. 1. Given Five positions of input features, arriving from the previous block. 2. Attention matrix Let us feed all five features to a query-key module (QK) and read back an attention weight matrix, A. The details of that module are a post of their own. 3. Attention weighting We multiply the input features by A to get the attention weighted features, Z. Still five positions. The effect is to combine features *across positions*, horizontally: X1 becomes X1 + X2, X2 becomes X2 + X3, and so on. 4. First layer Let us feed all five weighted features into the first layer of the FFN. Multiply by the weights and biases. This time the combining happens *across feature dimensions*, vertically, and each feature grows from 3 numbers to 4. Note that every position goes through the same weight matrix. That is what "position-wise" means. 5. ReLU We cross out the negatives. They become zeros. 6. Second layer Let us bring it back down: 4 dimensions to 3. The output feeds the next block, which has a completely separate set of parameters, and the whole thing runs again. You have just calculated a transformer block by hand. ✍️ The takeaway: the two parts are doing two different jobs, and neither one alone is enough. Attention mixes *across positions*, so a feature can see its neighbours. The FFN mixes *across feature dimensions*, so each position can think about itself. Horizontal, then vertical. Then that pattern repeats N times, each block with its own separate set of weights. That is the Nx from the list up top, and that is what makes the transformer run. 💾 Save this post! #AIbyHand #Transformers #DeepLearning

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Once she stepped out of that car towards him with a gun. That is not self-defense. She wanted a fight, and she killed an unarmed man at Walmart. All she had to do was stay in her car, or leave. He should of done the same. But you can't just kill someone over a parking spot and call it self defense.
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Once she stepped out of that car towards him with a gun. That is not self-defense. She wanted a fight, and she killed an unarmed man at Walmart. All she had to do was stay in her car, or leave. He should of done the same. But you can't just kill someone over a parking spot and call it self defense.

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