Introduces a fairness layer for deep learning models that guarantees output parity and an online primal-dual algorithm for aggregate fairness guarantees in streaming predictions with small batch sizes.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) , year=
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LSAMD searches a multi-dataset super Ans-Net to extract frequently selected base blocks as learngenes that initialize variable-sized Des-Nets with performance comparable to full pretrain-finetune at lower storage and training cost.
LaplacianFormer uses a Laplacian kernel with an injective feature map and efficient approximations to achieve linear attention that preserves mid-range interactions better than Gaussian-based linear attention in vision transformers.
citing papers explorer
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Differentiable Optimization Layers for Guaranteed Fairness in Deep Learning
Introduces a fairness layer for deep learning models that guarantees output parity and an online primal-dual algorithm for aggregate fairness guarantees in streaming predictions with small batch sizes.
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Learngene Search Across Multiple Datasets for Building Variable-Sized Models
LSAMD searches a multi-dataset super Ans-Net to extract frequently selected base blocks as learngenes that initialize variable-sized Des-Nets with performance comparable to full pretrain-finetune at lower storage and training cost.
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LaplacianFormer:Rethinking Linear Attention with Laplacian Kernel
LaplacianFormer uses a Laplacian kernel with an injective feature map and efficient approximations to achieve linear attention that preserves mid-range interactions better than Gaussian-based linear attention in vision transformers.