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cs.LG2024
Differentiable Prompt Learning for Vision Language Models
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +1
Prompt learning is an effective way to exploit the potential of large-scale pre-trained foundational models. Continuous prompts parameterize context tokens in prompts by turning th…
cs.LG2024
Graph is all you need? Lightweight data-agnostic neural architecture search without training
Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen +2
Neural architecture search (NAS) enables the automatic design of neural network models. However, training the candidates generated by the search algorithm for performance evaluatio…