Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 9 of 9 for “"random features"”.
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Random Features for Efficient Attention Approximation
… can be reduced to a theoretical derivation of random features with certain properties minimizing the variance of the approximation. This thesis describes an evolution of mechanisms for random-feature approximation: from the so-called FAVOR, to FAVOR+ and, finally, to FAVOR++. The FAVOR++ …
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Quantum Algorithms for Matrix Problems and Machine Learning
… investigate kernel based learning methods using random features. We work with the QRAM input model suitable for big data, and show how matrix functions and the quantum Fourier transform can be used to devise a quantum algorithm for sampling random features that are optimised for given input data …
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Gaussian Processes for Power System Monitoring, Optimization, and Planning
… the sensitivity of the labels with respect to features, yielding improved accuracy. This dissertation tailors Gaussian process regression for three applications in power systems. First, a physics-informed approach is introduced to infer the grid dynamics using synchrophasor data with minimal …
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Tame Long-Horizon Model-Based Reinforcement Learning
… learning with a number of neural-network random features as rewards allows implicit modeling of long-horizon environment dynamics. Then, planning techniques like model-predictive control using these implicit models enable fast adaptation to problems with new reward functions. Our method is …
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Investigating Random Properties for Deterministic Constants
… main purpose for the thesis is to identify the random features for pi, e and a comparable pseudo-random number, all of which are deterministic in the sense that they follow a recursive pattern. Despite their deterministic nature, whether these numbers are random or not is an important problem …
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Probabilistic machine learning algorithms for molecule discovery
… and some property of interest. Tanimoto random features (chapter 5) allows an established cheminformatics model to be applied (approximately) to large datasets. Finally, retro-fallback (chapter 6) uses a novel probabilistic formulation of the retrosynthesis problem to estimate whether a …
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The n-tuple network: coping with sub-optimality
… of classification based on memorisation of random features can be a powerful alternative to slower cost driven models. The speed of the method is at the expense of its optimality. RAMnets will fail for certain datasets but the cases when they do so are relatively easy to determine with the …
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Efficient learning machines
… to alleviate this is an approximation based on random features. In our second contribution we argue that this randomness renders the method inefficient and dissect the effect of these data- and task-agnostic learning bases by means of an empirical study. Viewing approximated kernel machines as …
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Random projection methods for stochastic convex minimization
… closed and convex sets. The problem has random features. Gradient or subgradient of objective function carries stochastic errors. Number of constraint sets can be extensive or infinitely many. Constraint sets might not be known apriori yet revealed through random realizations or randomly …