Search Results for author:
Found 6 papers, 2 papers with code
Scaling Law for Language Models Training Considering Batch Size
In this paper, we empirically investigate how a critical hyper-parameter, i. e., the global batch size, influences the LLM training prdocess.
Chain of Images for Intuitively Reasoning
The human brain is naturally equipped to comprehend and interpret visual information rapidly.
Baichuan 2: Open Large-scale Language Models
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing the need for extensive feature engineering.
MaxMin-L2-SVC-NCH: A Novel Approach for Support Vector Classifier Training and Parameter Selection
We first formulate the training and parameter selection of SVC as a minimax optimization problem named as MaxMin-L2-SVC-NCH, in which the minimization problem is an optimization problem of finding the closest points between two normal convex hulls (L2-SVC-NCH) while the maximization problem is an optimization problem of finding the optimal Gaussian kernel parameters.
Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing
To explore this, we first introduce the notion of training plasticity to quantify the training progress of internal DNN layers.
A Multitask Deep Learning Approach for User Depression Detection on Sina Weibo
In recent years, due to the mental burden of depression, the number of people who endanger their lives has been increasing rapidly.
