Hey r/machinelearning! I'm new here and recently wrote an article titled "Efficiency and Maintainability in Named Entity Recognition: A Trie-based Knowledge Base Approach" where I discuss a trie-based knowledge base approach for Named Entity Recognition (NER) models. I wanted to share it with you all and get your opinions and insights! Summary: In the...

Already few years ago I remember articles reporting about huge neural networks trained by researcher at Google, the size was measured by counting the number of parameters. Back then I thought that they were referring to the weights of the NNs. Now if you see the Wikipedia description of ChatGPT it says that the largest model of version 3 has 175 Billion...

This paper presents a memory-efficient zeroth-order optimizer (MeZO) for fine-tuning language models (LMs). As LMs grow larger, backpropagation becomes computationally costly, requiring large amounts of memory. MeZO adapts the classical Zeroth-order Stochastic Gradient Descent (ZO-SGD) method to operate in-place, enabling fine-tuning of LMs with the...

Code for Landmark Attention is now released and it should be possible to finetune existing LLaMA models using this method. https://github.com/epfml/landmark-attention Paper: https://arxiv.org/abs/2305.16300 The paper introduces a new method called Landmark Attention that addresses the memory limitations of transformers when dealing with longer contexts....

Hi, May I get a suggestion on what machine learning model or statistical approach can I use to identify data points as anomalies that are gradually decreasing or increasing in time series data. Thank you in advance submitted by /u/SeaworthinessGlad975 [link] [comments]

Hello ML friends :) So I'm building a machine learning model for an expensive experiment, the problem as you may have guessed is lack of enough data. I only have the data of 17 previous experiments, with 7 independent variables and 1 dependent variable in each one. My question to you is: How to deal with very important and yet very few data? I think...