SNLP enables layer-parallel Transformer inference by replacing sequential layer execution with structured Newton corrections and SNLP-aware training regularization, yielding up to 2.3x wall-clock speedup on 0.5B models while improving perplexity.
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CoFrGeNets implement a continued-fraction function class as plug-in replacements for transformer blocks, delivering competitive or superior downstream performance on GPT2-xl and Llama3-scale models with one-half to two-thirds the parameters.
Inf-SSM constrains the infinite-horizon evolution of SSMs via Grassmannian geometry and an efficient O(n^2) Sylvester solver to enable exemplar-free continual learning with reduced forgetting.
citing papers explorer
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SNLP: Layer-Parallel Inference via Structured Newton Corrections
SNLP enables layer-parallel Transformer inference by replacing sequential layer execution with structured Newton corrections and SNLP-aware training regularization, yielding up to 2.3x wall-clock speedup on 0.5B models while improving perplexity.
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CoFrGeNet: Continued Fraction Architectures for Language Generation
CoFrGeNets implement a continued-fraction function class as plug-in replacements for transformer blocks, delivering competitive or superior downstream performance on GPT2-xl and Llama3-scale models with one-half to two-thirds the parameters.
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Exemplar-Free Continual Learning for State Space Models
Inf-SSM constrains the infinite-horizon evolution of SSMs via Grassmannian geometry and an efficient O(n^2) Sylvester solver to enable exemplar-free continual learning with reduced forgetting.