The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study
Optimization algorithms are used to improve model accuracy. The optimization process
undergoes multiple cycles until convergence. A variety of optimization strategies have been …
undergoes multiple cycles until convergence. A variety of optimization strategies have been …
Large-scale optical reservoir computing for spatiotemporal chaotic systems prediction
Reservoir computing is a relatively recent computational paradigm that originates from a
recurrent neural network and is known for its wide range of implementations using different …
recurrent neural network and is known for its wide range of implementations using different …
Random features for kernel approximation: A survey on algorithms, theory, and beyond
The class of random features is one of the most popular techniques to speed up kernel
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …
methods in large-scale problems. Related works have been recognized by the NeurIPS Test …
Generalisation error in learning with random features and the hidden manifold model
We study generalised linear regression and classification for a synthetically generated
dataset encompassing different problems of interest, such as learning with random features …
dataset encompassing different problems of interest, such as learning with random features …
On-chip bacterial foraging training in silicon photonic circuits for projection-enabled nonlinear classification
G Cong, N Yamamoto, T Inoue, Y Maegami… - Nature …, 2022 - nature.com
On-chip training remains a challenging issue for photonic devices to implement machine
learning algorithms. Most demonstrations only implement inference in photonics for offline …
learning algorithms. Most demonstrations only implement inference in photonics for offline …
A review of emerging trends in photonic deep learning accelerators
Deep learning has revolutionized many sectors of industry and daily life, but as application
scale increases, performing training and inference with large models on massive datasets is …
scale increases, performing training and inference with large models on massive datasets is …
Large-scale photonic computing with nonlinear disordered media
Neural networks find widespread use in scientific and technological applications, yet their
implementations in conventional computers have encountered bottlenecks due to ever …
implementations in conventional computers have encountered bottlenecks due to ever …
Reservoir computing meets recurrent kernels and structured transforms
Reservoir Computing is a class of simple yet efficient Recurrent Neural Networks where
internal weights are fixed at random and only a linear output layer is trained. In the large size …
internal weights are fixed at random and only a linear output layer is trained. In the large size …
Photonic Kernel machine learning for ultrafast spectral analysis
We introduce photonic kernel machines, a scheme for ultrafast spectral analysis of noisy
radio-frequency signals from single-shot optical intensity measurements. The approach …
radio-frequency signals from single-shot optical intensity measurements. The approach …
Photonic differential privacy with direct feedback alignment
Abstract Optical Processing Units (OPUs)--low-power photonic chips dedicated to large
scale random projections--have been used in previous work to train deep neural networks …
scale random projections--have been used in previous work to train deep neural networks …