A novel differentiable approach optimizes geometric waveguide coatings, achieving substantial gains in light efficiency and uniformity for optical AR displays.

Abstract: This note considers the distributed optimization problem on directed graphs with nonconvex local objective functions and the unknown network connectivity. A new adaptive algorithm is ...

Implementation of Hindsight Differentiable Policy Optimization, as described in the paper Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization. We argue that ...

Abstract: Graph condensation reduces the size of large graphs while preserving performance, addressing the scalability challenges of Graph Neural Networks caused by computational inefficiencies on ...

The last few years have seen a rise in novel differentiable graphics layers which can be inserted in neural network architectures. From spatial transformers to differentiable graphics renderers, these ...

In the late 19th century, Karl Weierstrass invented a fractal-like function that was decried as nothing less than a “deplorable evil.” In time, it would transform the foundations of mathematics.

CBSE 2025 Competency-Based Questions: The Central Board of Secondary Education (CBSE) has officially released the practice question paper for the academic year 2024-25, for the class 12th on its ...

Over the last decade, deep generative models have evolved to generate realistic and sharp images. The success of these models is often attributed to an extremely large number of trainable parameters ...

Diffusion models have revolutionized generative modeling across various data types. However, in practical applications like generating aesthetically pleasing images from text descriptions, fine-tuning ...