закладки (спрятать)64
- Scientists used machine learning to reveal new scientific knowledge hidden in old research papers.
- In mathematics, the Wasserstein or Kantorovich–Rubinstein metric or distance is a distance function defined between probability distributions on a given metric space M {\displaystyle M} M. Intuitively, if each distribution is viewed as a unit amount of "dirt" piled on M {\displaystyle M} M, the metric is the minimum "cost" of turning one pile into the other, which is assumed to be the amount of dirt that needs to be moved times the mean distance it has to be moved. Because of this analogy, the metric is known in computer science as the earth mover's distance.
- In this post, Phillip talks through the challenges & pitfalls of LLMs we faced when building our Query Assistant - and that you too may face.
- Can't remember what is precision and recall (sensitivity)? Why accuracy is not enough? Read the explanation with an example with a confusion matrix.
- We introduce Vicuna-13B, an open-source chatbot trained by fine-tuning LLaMA on user-shared conversations collected from ShareGPT. Preliminary evaluation using GPT-4 as a judge shows Vicuna-13B achieves more than 90%* quality of OpenAI ChatGPT and Google Bard while outperforming other models like LLaMA and Stanford Alpaca in more than 90%* of cases. The cost of training Vicuna-13B is around $300. The code and weights, along with an online demo, are publicly available for non-commercial use.
- TLDR — Extractive question answering is an important task for providing a good user experience in many applications. The popular Retriever-Reader framework for QA using BERT can be difficult to scale…
- In natural language understanding (NLU) tasks, there is a hierarchy of lenses through which we can extract meaning — from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics. The process of learning, recognizing, and extracting these topics across a collection of documents is called topic modeling. In this post, we will explore topic modeling through 4 of the most popular techniques today: LSA, pLSA, LDA, and the newer, deep learning-based lda2vec.
- Facebook Research open sourced a great project recently – fastText, a fast (no surprise) and effective method to learn word representations and perform text classification. I was curious about comparing these embeddings to other commonly used embeddings, so word2vec seemed like the obvious choice, especially considering fastText embeddings are an extension of word2vec.
- Basic knowledge of XLNet to understand the difference between XLNet and BERT intuitively
- In this article, I am going to show you how to choose the number of principal components when using principal component analysis for dimensionality reduction. In the first section, I am going to give you a short answer for those of you who are in a hurry and want to get something working. Later, I am going to provide a more extended explanation for those of you who are interested in understanding PCA.
- The pulearn Python package provide a collection of scikit-learn wrappers to several positive-unlabled learning (PU-learning) methods. Features Scikit-learn compliant wrappers to prominent PU-learning methods. Fully tested on Linux, macOS and Windows systems. Compatible with Python 3.5+.
- Build document-based question-answering systems using LangChain, Pinecone, LLMs like GPT-4, and semantic search for precise, context-aware AI solutions.
- Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources
- Document embeddings and sentence relatedness using BERT
- Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation similarly to t-SNE, but also for general non-linear dimension reduction. The algorithm is founded on three assumptions about the data The data is uniformly distributed on Riemannian manifold; The Riemannian metric is locally constant (or can be approximated as such); The manifold is locally connected. From these assumptions it is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low dimensional projection of the data that has the closest possible equivalent fuzzy topological structure.
- Identifying meaningful topics for sparse Steam reviews -- by Steve Shao
- MACE (Multi-Annotator Competence Estimation) is an implementation of an item-response model that let's you evaluate redundant annotations of categorical data. It provides competence estimates of the individual annotators and the most likely answer to each item. If we have 10 annotators answer a question, and five answer with 'yes' and five with 'no' (a surprisingly frequent event), we would normaly have to flip a coin to decide what the right answer is. If we knew, however, that one of the people who answered 'yes' is an expert on the question, while one of the others just alwas selects 'no', we would take this information into account to weight their answers. MACE does exactly that. It tries to find out which annotators are more trustworthy and upweighs their answers. All you need to provide is a CSV file with one item per line. In tests, MACE's trust estimates correlated highly wth the annotators' true competence, and it achieved accuracies of over 0.9 on several test sets. MACE can take annotated items into account, if they are available. This helps to guide the training and improves accuracy.
публикации (спрятать)153
- (2024)
- The Twelfth International Conference on Learning Representations, (2024)
- (2024)
- Forty-first International Conference on Machine Learning, (2024)
- (ноября 2023)
