Signalbound

Mathematicians Use Network Theory to Advance Understanding of Fourier Transform

Mathematicians have made significant progress on a long-standing problem related to the Fourier transform, a fundamental mathematical tool u

On the robustness of topological gap detection via transport

High Performance Image Sensor Processing Using FPGA [pdf]

Wider Neural Networks with Fewer Parameters Improve Performance by Reducing Feature Interference

This research paper demonstrates that increasing the number of neurons in a neural network without increasing the number of non-zero paramet

Using Diffusion Models to Visualize What Self-Supervised Neural Networks Actually Learn

This paper introduces the use of Representation Conditional Diffusion Models (RCDM) to visualize what self-supervised learning (SSL) models

Extreme Super-Resolution via Scale Autoregression and Preference Alignment