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Adaptive Proximal Gradient Method for Convex Optimization
In this paper, we explore two fundamental first-order algorithms in convex optimization, namely, gradient descent (GD) and proximal gradient method (ProxGD). Our focus is on making these algorithms entirely adaptive by leveraging local curvature information of smooth functions.
Marich: A Query-efficient Distributionally Equivalent Model Extraction Attack using Public Data
First, we define distributionally equivalent and Max-Information model extraction attacks, and reduce them into a variational optimisation problem.
Geometry of naturalistic object representations in recurrent neural network models of working memory
Working memory is a central cognitive ability crucial for intelligent decision-making. Recent experimental and computational work studying working memory has primarily used categorical (i.e., one-hot) inputs, rather than ecologically-relevant, multidimensional naturalistic ones.
Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making Manling Li
We aim to evaluate Large Language Models (LLMs) for embodied decision making.
Learning Adaptive T Density Fields for Clean Reconstruction Supplementary
The ENL metric measures the smoothness in the homogeneous regions.
Overleaf Example
It consists of reconstructing a 3D tomogram from captured 2D projections of the scanned sample.
