techxplore

Interrupting encoder training in diffusion models enables more efficient generative AI

A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving generative AI models. The method reinterpreted Schrödinger bridge models as ...
GitHub

training_quora_duplicate_questions.py

as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair. It does NOT produce a sentence embedding and does NOT work for individual ...
GitHub

Text encoder errors in both SD3 dreambooth training scripts

Multiple mismatched argument errors in train_dreambooth_sd3.py when training with train_text_encoder set to True. Ordering/assignment errors in train_dreambooth_lora_sd3.py when training with ...
IEEE

SEQUENCE TRAINING OF ENCODER-DECODER MODEL USING POLICY GRADIENT FOR END- TO-END SPEECH RECOGNITION

Abstract: The standard evaluation metric of automatic speech recognition (ASR) is the word error rate (WER), which measures the dissimilarity between recognized word ...
Microsoft

Deep encoders with auxiliary parameters for extreme classification

The task of annotating a data point with labels most relevant to it from a large universe of labels is referred to as Extreme Classification (XC). State-of-the-art XC methods have applications in ...
Microsoft

Refined graph encoder embedding via self-training and latent community recovery

This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community recovery within ...