One binary operator eml(x,y)=exp(x)-ln(y) plus the constant 1 generates all elementary functions including sin, cos, sqrt, log, arithmetic operations, and constants e, pi, i.
Activation func- tions in deep learning: A comprehensive survey and benchmark.Neurocomputing, 503: 92–108
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MS-FLOW uses a capacity-limited sparse routing mechanism to model only critical inter-variable dependencies in time series data, achieving state-of-the-art accuracy on 12 benchmarks with fewer but more reliable connections.
An attack aligns differently shuffled intermediate activations from secure Transformer inference queries to recover model weights with low error using roughly one dollar of queries.
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All elementary functions from a single binary operator
One binary operator eml(x,y)=exp(x)-ln(y) plus the constant 1 generates all elementary functions including sin, cos, sqrt, log, arithmetic operations, and constants e, pi, i.
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What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
MS-FLOW uses a capacity-limited sparse routing mechanism to model only critical inter-variable dependencies in time series data, achieving state-of-the-art accuracy on 12 benchmarks with fewer but more reliable connections.
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On the (In-)Security of the Shuffling Defense in the Transformer Secure Inference
An attack aligns differently shuffled intermediate activations from secure Transformer inference queries to recover model weights with low error using roughly one dollar of queries.