Authors: Kenneth Ezukwoke, Anis Hoayek, Mireille Batton-Hubert, Xavier Boucher
GCVAE is a generalized version of VAE with adaptive weights. It solves the problem of disentanglement and reconstuction trade-off by replacing fixed hyperparameters with trainable Lagrangian hyperparameters:
The controllable hyperparameters ensures simultaneously learning a well disentangled representation with high reconstruction quality and minimal information loss during the bottleneck-compression.
Authors: Kenneth Ezukwoke, Anis Hoayek, Mireille Batton-Hubert, Xavier Boucher, Pascal Gounet, Jerome Adrian
The research focuses on leveraging pretrained language models (such as transformers) for failure analysis (diagnosis) in semiconductor industry.
Significantly improved results fine-tuning pretrained GPT2 (a decoder only transformer) for the downstream task of FATG. Next, we introduce the GCVAE variational loss for fine-tuning.
Authors: Kenneth Ezukwoke, Anis Hoayek, Mireille Batton-Hubert, Xavier Boucher, Pascal Gounet, Jerome Adrian
This work is the convergence resulting from finetuning Large Language Model (LLM) with the Generalized-Controllable Variational AutoEncoder (GCVAE), for improved Failure Analysis Triplet Generation (FATG)
An improved domain specific (FATG) LLM model that introduces latent modeling for domain generalization and generation.
Author: Kenneth Ezukwoke
A financial research software with the capacity to automatically generate signals for algorithmic trading using OANDA v20 REST API as data source.
Disclaimer: FORECASTING-1.0 and ASG are research tools for educational and experimental purpose only. Software is subject to users discretion and open to financial risk upon usage.