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Found 6 papers, 1 papers with code
On backpropagating Hessians through ODEs
We discuss the problem of numerically backpropagating Hessians through ordinary differential equations (ODEs) in various contexts and elucidate how different approaches may be favourable in specific situations.
Single-Photon Image Classification
Quantum computing-based machine learning mainly focuses on quantum computing hardware that is experimentally challenging to realize due to requiring quantum gates that operate at very low temperature.
Intelligent Matrix Exponentiation
We present a novel machine learning architecture that uses the exponential of a single input-dependent matrix as its only nonlinearity.
Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagation
The timing of individual neuronal spikes is essential for biological brains to make fast responses to sensory stimuli.
SO(8) Supergravity and the Magic of Machine Learning
Using de Wit-Nicolai $D=4\;\mathcal{N}=8\;SO(8)$ supergravity as an example, we show how modern Machine Learning software libraries such as Google's TensorFlow can be employed to greatly simplify the analysis of high-dimensional scalar sectors of some M-Theory compactifications.
Holographic and other Point Set Distances for Machine Learning
We introduce an analytic distance function for moderately sized point sets of known cardinality that is shown to have very desirable properties, both as a loss function as well as a regularizer for machine learning applications.
