NodePFN pre-trains on synthetic graphs with controllable homophily and causal feature-label models to achieve 71.27 average accuracy on 23 node classification benchmarks without graph-specific training.
Proceedings of the IEEE/CVF winter conference on applications of computer vision , pages=
3 Pith papers cite this work. Polarity classification is still indexing.
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ElasticDiT introduces an elastic DiT architecture with adjustable spatial compression and block depth plus Shift Sparse Block Attention and a distilled VAE to enable a single model to cover multiple fidelity-latency points for high-resolution image generation on mobile devices.
A temporal memory-aware Transformer emulator for the Emanuel convective parameterization shows lower offline errors and 10-year stability in single-column model tests compared to memory-less MLP and LSTM baselines.
citing papers explorer
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Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
NodePFN pre-trains on synthetic graphs with controllable homophily and causal feature-label models to achieve 71.27 average accuracy on 23 node classification benchmarks without graph-specific training.
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ElasticDiT: Efficient Diffusion Transformers via Elastic Architecture and Sparse Attention for High-Resolution Image Generation on Mobile Devices
ElasticDiT introduces an elastic DiT architecture with adjustable spatial compression and block depth plus Shift Sparse Block Attention and a distilled VAE to enable a single model to cover multiple fidelity-latency points for high-resolution image generation on mobile devices.
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climt-paraformer: Stable Emulation of Convective Parameterization using a Temporal Memory-aware Transformer
A temporal memory-aware Transformer emulator for the Emanuel convective parameterization shows lower offline errors and 10-year stability in single-column model tests compared to memory-less MLP and LSTM baselines.