StreamKL is the first fused GPU primitive for attention KL divergence that reduces memory from O(N_Q N_K) to O(1) via an online one-pass formulation and tile-wise recomputation.
arXiv preprint arXiv:2308.12469 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Object functionalization is cast as neural graph completion over a functional graph of parts, contacts, and motions, followed by geometry realization that also rectifies erroneous motions, demonstrated on furniture with a new paired dataset.
UniVidX unifies diverse video generation tasks into one conditional diffusion model using stochastic condition masking, decoupled gated LoRAs, and cross-modal self-attention.
citing papers explorer
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StreamKL: Fast and Memory-Efficient KL Divergence for Boosting Attention Distillation
StreamKL is the first fused GPU primitive for attention KL divergence that reduces memory from O(N_Q N_K) to O(1) via an online one-pass formulation and tile-wise recomputation.
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Functionalization via Structure Completion and Motion Rectification
Object functionalization is cast as neural graph completion over a functional graph of parts, contacts, and motions, followed by geometry realization that also rectifies erroneous motions, demonstrated on furniture with a new paired dataset.
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UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors
UniVidX unifies diverse video generation tasks into one conditional diffusion model using stochastic condition masking, decoupled gated LoRAs, and cross-modal self-attention.