FTerViT introduces fully ternary Vision Transformers with TernaryBitConv2d and TernaryLayerNorm operators, achieving 82.43% ImageNet top-1 at 6.09 MB with 15x compression.
Escaping the big data paradigm with compact transformers.arXiv preprint arXiv:2104.05704, 2021
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IAFormer uses boost-invariant pairwise quantities and differential attention to create a sparse Transformer that achieves state-of-the-art classification on top-quark and quark-gluon jet datasets while using over an order of magnitude fewer parameters than prior Particle Transformer models.
Checkerboard derives a closed-form checkerboard trigger for clean-label backdoor attacks that achieves over 94% ASR with poisoning rates as low as 0.46% on ImageNet-100 and 99.99% ASR with 20 samples on CIFAR-10.
SPAR is a street-legal physical rim that cuts modern ALPR accuracy by 60% and reaches 18% targeted impersonation while costing under $100 and requiring no plate modification.
A principle-driven RF encoder achieves 77.7% average accuracy across 15 cross-modal tasks, performing better on physically grounded tasks than semantic ones.
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.
citing papers explorer
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FTerViT: Fully Ternary Vision Transformer
FTerViT introduces fully ternary Vision Transformers with TernaryBitConv2d and TernaryLayerNorm operators, achieving 82.43% ImageNet top-1 at 6.09 MB with 15x compression.
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IAFormer: Interaction-Aware Transformer network for collider data analysis
IAFormer uses boost-invariant pairwise quantities and differential attention to create a sparse Transformer that achieves state-of-the-art classification on top-quark and quark-gluon jet datasets while using over an order of magnitude fewer parameters than prior Particle Transformer models.
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Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget
Checkerboard derives a closed-form checkerboard trigger for clean-label backdoor attacks that achieves over 94% ASR with poisoning rates as low as 0.46% on ImageNet-100 and 99.99% ASR with 20 samples on CIFAR-10.
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Street-Legal Physical-World Adversarial Rim for License Plates
SPAR is a street-legal physical rim that cuts modern ALPR accuracy by 60% and reaches 18% targeted impersonation while costing under $100 and requiring no plate modification.
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Building The Ph(ysical)AI Layer Of Machine Intelligence
A principle-driven RF encoder achieves 77.7% average accuracy across 15 cross-modal tasks, performing better on physically grounded tasks than semantic ones.
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Are Candidate Models Really Needed for Active Learning?
Active learning with randomly initialized models achieves comparable results to traditional candidate-model methods, with low-confidence sampling proving most effective.