RNN computation is recovered from multi-hop graph pathways, and constraining these pathways via resolvent regularization yields improved temporal sparsity and task performance over standard L1.
Pathways: Asynchronous distributed dataflow for ml
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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CoCa unifies contrastive and generative pretraining in one image-text model to reach 86.3% zero-shot ImageNet accuracy and new state-of-the-art results on multiple downstream benchmarks.
PaLM 540B demonstrates continued scaling benefits by setting new few-shot SOTA results on hundreds of benchmarks and outperforming humans on BIG-bench.
Piper decouples user-defined distributed training strategies from runtime execution using transformations on a unified global training DAG IR, achieving parity on ZeRO and gains on composed strategies like DualPipe.
citing papers explorer
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Unifying Dynamical Systems and Graph Theory to Mechanistically Understand Computation in Neural Networks
RNN computation is recovered from multi-hop graph pathways, and constraining these pathways via resolvent regularization yields improved temporal sparsity and task performance over standard L1.
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CoCa: Contrastive Captioners are Image-Text Foundation Models
CoCa unifies contrastive and generative pretraining in one image-text model to reach 86.3% zero-shot ImageNet accuracy and new state-of-the-art results on multiple downstream benchmarks.
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PaLM: Scaling Language Modeling with Pathways
PaLM 540B demonstrates continued scaling benefits by setting new few-shot SOTA results on hundreds of benchmarks and outperforming humans on BIG-bench.
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Piper: A Programmable Distributed Training System
Piper decouples user-defined distributed training strategies from runtime execution using transformations on a unified global training DAG IR, achieving parity on ZeRO and gains on composed strategies like DualPipe.