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AbstractPhil
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https://civitai.com/user/AbstractPhila

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datasets, research papers, experimentation, vision, classification, text encoders, tokenization, llms, diffusion, distillation, and more.

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updated a model 1 day ago
AbstractPhil/geolip-aleph-void
reacted to OzTianlu's post with 🧠 2 days ago
ResNet is Explicit Euler. GPT is Implicit Euler. What Else is Hiding in Plain Sight? Read online: https://datawhalechina.github.io/learning-terrain/ I wrote an open-source monograph on learning dynamics — The Terrain of Learning. Bilingual (Chinese/English), 4 volumes, 12 chapters, 30+ print-grade figures. Completely free (CC BY-NC-SA 4.0). The core argument: gradient descent is not optimization. It's terrain motion. The loss function is a landscape. The gradient is the direction of slope. The optimizer is how you choose each step. Once you see it this way, everything clicks: ResNet = explicit Euler integration on a vector field. The residual branch is the vector field. Each layer takes one Euler step. GPT autoregression = implicit-state Euler iteration. Stable where explicit Euler explodes. That's why transformers handle long-range dependencies. DEQ = the Banach fixed-point theorem in production. The forward pass is root-finding. There are no layers to backprop through. KL divergence = a Bregman divergence on the entropy landscape. Your belief space is curved, not flat. Chain-of-thought reasoning = hidden states flowing along a reasoning field toward an attractor basin. Correct answers have wide basins. The number of reasoning steps is determined by the terrain, not by the problem. Diffusion models = systems flowing downhill along a score vector field, from noise to structure, from high energy to low energy. The book traces one idea across 337 years — from F=ma (Newton, 1687) to H=T+V (Hamilton, 1833) to loss landscape + gradient field (2020s). Hamilton replaced a catalog of forces with one geometric object. This book does the same for deep learning. GitHub: https://github.com/datawhalechina/learning-terrain Discussion: https://github.com/datawhalechina/learning-terrain/discussions/2 Convergence is not hope. Convergence is geometry. You see.
repliedto OzTianlu's post 2 days ago
ResNet is Explicit Euler. GPT is Implicit Euler. What Else is Hiding in Plain Sight? Read online: https://datawhalechina.github.io/learning-terrain/ I wrote an open-source monograph on learning dynamics — The Terrain of Learning. Bilingual (Chinese/English), 4 volumes, 12 chapters, 30+ print-grade figures. Completely free (CC BY-NC-SA 4.0). The core argument: gradient descent is not optimization. It's terrain motion. The loss function is a landscape. The gradient is the direction of slope. The optimizer is how you choose each step. Once you see it this way, everything clicks: ResNet = explicit Euler integration on a vector field. The residual branch is the vector field. Each layer takes one Euler step. GPT autoregression = implicit-state Euler iteration. Stable where explicit Euler explodes. That's why transformers handle long-range dependencies. DEQ = the Banach fixed-point theorem in production. The forward pass is root-finding. There are no layers to backprop through. KL divergence = a Bregman divergence on the entropy landscape. Your belief space is curved, not flat. Chain-of-thought reasoning = hidden states flowing along a reasoning field toward an attractor basin. Correct answers have wide basins. The number of reasoning steps is determined by the terrain, not by the problem. Diffusion models = systems flowing downhill along a score vector field, from noise to structure, from high energy to low energy. The book traces one idea across 337 years — from F=ma (Newton, 1687) to H=T+V (Hamilton, 1833) to loss landscape + gradient field (2020s). Hamilton replaced a catalog of forces with one geometric object. This book does the same for deep learning. GitHub: https://github.com/datawhalechina/learning-terrain Discussion: https://github.com/datawhalechina/learning-terrain/discussions/2 Convergence is not hope. Convergence is geometry. You see.
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