Multi-server multi-key FHE with a shared random mask lets PRoVeFL run complex Byzantine-robust FL aggregation privately and verifiably, with large reported speedups over Prio and ELSA.
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6 Pith papers cite this work. Polarity classification is still indexing.
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DCI decomposes large-scale visual recognition into simpler subproblems with dynamic pruning to raise MLLM accuracy on datasets like ImageNet-1K and 21K.
XOResNet combines OA shortcuts and XOR meta-residuals into a residual block to build deeper SNNs that outperform prior gradient-descent SNNs on Fashion-MNIST, CIFAR-10, CIFAR-100, and miniImageNet.
CA-LIG is a unified hierarchical attribution method that computes layer-wise Integrated Gradients fused with class-specific attention gradients to generate signed, context-sensitive explanations for transformer models.
A framework combining stochastic zeroth-order optimization and dynamic low-rank surrogate modeling with an implicit projector-splitting integrator enables end-to-end training of hybrid neural networks containing black-box physical layers and reaches near-digital accuracy on vision, audio, and text任务
DP-CDA generates synthetic data via class-specific randomized mixing to claim stronger privacy guarantees and higher predictive utility than prior data-publishing methods under equivalent privacy budgets.
citing papers explorer
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PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning
Multi-server multi-key FHE with a shared random mask lets PRoVeFL run complex Byzantine-robust FL aggregation privately and verifiably, with large reported speedups over Prio and ELSA.
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Divide-and-Conquer Inference for Large-Scale Visual Recognition with Multimodal Large Language Models
DCI decomposes large-scale visual recognition into simpler subproblems with dynamic pruning to raise MLLM accuracy on datasets like ImageNet-1K and 21K.
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XOResNet: Exclusive-OR Meta-Residuals Facilitate Deep Spiking Neural Networks Learning
XOResNet combines OA shortcuts and XOR meta-residuals into a residual block to build deeper SNNs that outperform prior gradient-descent SNNs on Fashion-MNIST, CIFAR-10, CIFAR-100, and miniImageNet.
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Explainable AI: Context-Aware Layer-Wise Integrated Gradients for Explaining Transformer Models
CA-LIG is a unified hierarchical attribution method that computes layer-wise Integrated Gradients fused with class-specific attention gradients to generate signed, context-sensitive explanations for transformer models.
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Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers
A framework combining stochastic zeroth-order optimization and dynamic low-rank surrogate modeling with an implicit projector-splitting integrator enables end-to-end training of hybrid neural networks containing black-box physical layers and reaches near-digital accuracy on vision, audio, and text任务
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DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset Synthesis Through Randomized Mixing
DP-CDA generates synthetic data via class-specific randomized mixing to claim stronger privacy guarantees and higher predictive utility than prior data-publishing methods under equivalent privacy budgets.