Tech Times

Brain-Like Learning Rule Cracks Convolutional Networks: Sakana AI Measures the Biology Tax

Biologically plausible learning now reaches 96.7% on MNIST and 61.7% on CIFAR-10 without backpropagation, as Sakana AI ...
eLife

Policy-Gradient Reinforcement Learning as a General Theory of Practice-Based Motor Skill Learning

This valuable computational study presents a conceptually simple and biologically plausible reinforcement-learning framework for motor learning based on policy-gradient methods. The evidence ...
IEEE

Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction

Abstract: In compressed sensing (CS) MRI, model-based methods are pivotal to achieving accurate reconstruction. One of the main challenges in model-based methods is finding an effective prior to ...
GitHub

Vision-Language Gradient Descent-driven All-in-One Deep Unfolding Networks

Abstract: Dynamic image degradations, including noise, blur and lighting inconsistencies, pose significant challenges in image restoration, often due to sensor limitations or adverse environmental ...
IEEE

A Low-Rank Projected Proximal Gradient Method for Spectral Compressed Sensing

Abstract: This paper presents a new approach to the recovery of spectrally sparse signals (SSS) from partially observed entries, addressing challenges posed by large-scale data and heavy-noise ...
Frontiers

Arabidopsis root apical meristem adaptation to an osmotic gradient condition: an integrated approach from cell expansion to gene expression

Climate change triggers abiotic stress, such as drought and high salinity, that can cause osmotic stress. Water availability can limit plant growth, and the root tip tissues initially sense it. Most ...
Scientific Research Publishing

Sebbouh, O., Gower, M.R. and Defazio, A. (2021) Almost Sure Convergence Rates for Stochastic Gradient Descent and Stochastic Heavy Ball. Proceedings of Machine Learning ...

ABSTRACT: Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of ...
PNAS

A Hamilton–Jacobi-based proximal operator

Many objective functions do not admit explicit formulas for their proximal operators. Moreover, these operators often cannot be estimated using exact gradients (e.g., when objectives are accessible ...
Imperial College London

Wolfgang Stockinger: Linear Convergence of a Policy Gradient Method for Finite Horizon Continuous Time Stochastic Control Problems

In talk, we will discuss a proximal gradient algorithm for feedback controls of finite-time horizon stochastic control problems. The state dynamics are continuous time nonlinear diffusions with ...