Guide Labs debuts a new kind of interpretable LLM

The company open sourced an 8-billion-parameter LLM, Steerling-8B, trained with a new architecture designed to make its ...

Researchers propose a self-distillation fix for ‘catastrophic forgetting’ in LLMs

LLMs tend to lose prior skills when fine-tuned for new tasks. A new self-distillation approach aims to reduce regression and ...
IEEE

Unsupervised Deep Learning for DAS-VSP Denoising Using Attention-Based Deep Image Prior

Abstract: Distributed acoustic sensing (DAS) has emerged as a widely used technology in various applications, including borehole microseismic monitoring, active source exploration, and ambient noise ...
GitHub

eric-vo/mathworks-noise-suppression

data/ ├── noise/ # Background noise samples used for mixing ├── train/ # Training dataset │ ├── clean_trainset_wav/ # Clean training audio │ └── noisy_trainset_wav/ # Noisy training audio ├── validate ...
GitHub

Nonlinear Model-Predictive Control Using Koopman Operator

This document provides a detailed explanation of the MATLAB code that demonstrates the application of the Koopman operator theory for controlling a nonlinear system using Model Predictive Control (MPC ...
IEEE

Learning-Based Motion Planning Leveraging Multivariate Deep Evidential Regression

Abstract: Learning-based motion planning methods have shown significant promise in enhancing the efficiency of traditional algorithms. However, they often face performance degradation in novel ...
Visual Studio Magazine

Linear Regression with Pseudo-Inverse Training Using JavaScript

Linear regression is the most fundamental machine learning technique to create a model that predicts a single numeric value. One of the three most common techniques to train a linear regression model ...