Derives inequalities between L1 density distances and mixing-measure discrepancies to obtain posterior contraction rates for Dirichlet process mixtures with unknown shared scale.
Sigmoid Self-Attention has Lower Sample Complexity than Softmax Self- Attention: A Mixture-of-Experts Perspective
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Boltzmann attention augments query-key attention with learnable Ising pairwise couplings, yielding consistent gains over softmax attention on character language modeling and bracket matching that increase with sequence length.
An FPGA accelerator for a sparsity-exploiting adaptive Transformer achieves up to 2x speedup and sub-2ms latency for massive MIMO localization with under 10% accuracy loss on real measurements.
Thin-film lithium niobate modulators implement electro-optic Softmax and Sigmoid alternatives for transformers that maintain competitive accuracy under 4-bit quantization and characterized noise up to 10 GBaud.
citing papers explorer
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Convergence Rates for Latent Mixing Measures in Infinite Homoscedastic Location-Scale Mixture Models
Derives inequalities between L1 density distances and mixing-measure discrepancies to obtain posterior contraction rates for Dirichlet process mixtures with unknown shared scale.
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Boltzmann Attention: Learnable Ising Couplings for Cooperative Attention
Boltzmann attention augments query-key attention with learnable Ising pairwise couplings, yielding consistent gains over softmax attention on character language modeling and bracket matching that increase with sequence length.
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Efficient Implementation of an Adaptive Transformer Accelerator for Massive MIMO Outdoor Localization
An FPGA accelerator for a sparsity-exploiting adaptive Transformer achieves up to 2x speedup and sub-2ms latency for massive MIMO localization with under 10% accuracy loss on real measurements.
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Integrated electro-optic attention nonlinearities for transformers
Thin-film lithium niobate modulators implement electro-optic Softmax and Sigmoid alternatives for transformers that maintain competitive accuracy under 4-bit quantization and characterized noise up to 10 GBaud.