Tag: transformers
All the articles with the tag "transformers".
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From AlexNet to World Models: The Evolution of Multi-Modal Neural Networks
A ground-up tour of how neural networks learned to see, then to see-and-read, and finally to imagine. From AlexNet and CNNs, through CLIP and the vision-language models behind GPT-4V, to world models like Dreamer, V-JEPA 2, and LeWorldModel — with architectures, math, and benchmark numbers along the way.
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Attention Residuals: Softmax Attention Over Depth
A deep dive into the Kimi team's Attention Residuals (AttnRes) — replacing the fixed-weight residual connection with learned softmax attention over depth. Covers the time–depth duality, Full vs Block AttnRes, the structured-matrix view that unifies prior residual variants, the pipeline-parallel infra that makes it practical, and the scaling-law and 48B-MoE results.
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Hybrid Attention and MLA: The Tradeoff
A side-by-side dive into Xiaomi MiMo's hybrid sliding-window/global attention and DeepSeek's Multi-head Latent Attention. The two answer the same question — how to make attention affordable at long context — with very different bets, and those bets shape everything from training infra to KV cache size.
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Inside DeepSeek's Sparse Attention: From NSA to DSA
A deep dive into DeepSeek's two sparse attention designs — Native Sparse Attention (NSA) and DeepSeek Sparse Attention (DSA) — covering the math, the hardware story, and why DSA in V3.2 looks so different from NSA.