Multimodal Lecture Presentations (MLP) is a large-scale benchmark dataset for testing the capabilities of machine learning models in multimodal understanding of educational content. To benchmark the understanding of multimodal information in lecture slides, two research tasks are introduced; they are designed to be a first step towards developing AI that can explain and illustrate lecture slides: automatic retrieval of (1) spoken explanations for an educational figure (Figure-to-Text) and (2) illustrations to accompany a spoken explanation (Text-to-Figure).

ADU G3 MLP

## Overview

ADU G3 MLP is a dataset for object detection tasks - it contains Breadboard annotations for 425 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Mlp Activity

## Overview

Mlp  Activity is a dataset for object detection tasks - it contains Mountain Dew annotations for 1,218 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

kaykyramos/orion-mlp dataset hosted on Hugging Face and contributed by the HF Datasets community

Data sets used to generate learning curves. The two data sets contain the prediction errors (root-mean-square errors) obtained with different machine learning potentials (MLPs) for both energy and gradients of all molecules available in the MD17 database. The following MLP models were tested: KRR-CM, KREG, GAP-SOAP, sGDML, ANI, DPMD and PhysNet. A test set with 20000 geometries was randomly selected for each molecular system to evaluate the model's performance.See http://mlatom.com/MLPbenchmark1/ for web-version of the database, where you can further analyze it.

The following are the original input Excel spreadsheet, the prediction data of Model 1, and the data of the five major traditional indicators for each participant.

Dataset

This dataset was created by Akalya Subramanian

Contents

Mlp Wed Doc

## Overview

Mlp Wed Doc is a dataset for object detection tasks - it contains Note annotations for 280 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset({ features: ['questionId', 'question', 'query_anal', 'plan_create', 'plan_exec', 'final_ans', 'images', 'answers', 'messages'], num_rows: 28295 })

error(final_ans == "<") count : 401

Dataset

This dataset was created by SYED ALI MUJTABA

Released under Apache 2.0

Contents

MLP Lab 5 Group 1

## Overview

MLP Lab 5 Group 1 is a dataset for object detection tasks - it contains Object annotations for 218 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

FunCoup network information for gene MLP in Homo sapiens. CSRP3_HUMAN Cysteine and glycine-rich protein 3

Other-Long-Term-Liabilities Time Series for MLP SE. MLP SE, together with its subsidiaries, provides financial services to private, corporate, and institutional clients in Germany. The company operates through Financial Consulting, Banking, FERI, DOMCURA, Industrial Broker, and Deutschland.Immobilien segments. The Financial Consulting segment offers consulting services for academics and other clients related to insurance, investments, and occupational pension provision schemes; and health and non-life insurance, real estate brokerage, wealth management, and loans and mortgages, as well as the brokering of contracts in financial services. The Banking segment provides banking services, such as accounts, credit cards, loans, and wealth management solutions. This segment also offers securities custody, commission, investment consulting, and investment brokerage services. The FERI segment provides wealth and investment consulting to institutional investors and high net worth individuals. The DOMCURA segment operates as an underwriting agency that designs, develops, and implements coverage concepts in the field of non-life insurance products, as well as provides brokerage services. The Industrial Broker segment offers consulting services and insurances for industrial and commercial clients, as well as the brokerage of insurance policies. The Deutschland.Immobilien segment engages in the brokering of real estate properties; and development and sale of real estate projects. The company was formerly known as MLP AG and changed its name to MLP SE in September 2017. MLP SE was founded in 1971 and is headquartered in Wiesloch, Germany.

This dataset tracks annual total students amount from 2017 to 2023 for Riverside Academy Mlp

MLP Project

## Overview

MLP Project is a dataset for object detection tasks - it contains Helmet annotations for 1,138 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This dataset tracks annual reading and language arts proficiency from 2017 to 2019 for Riverside Academy Mlp vs. Minnesota and Cambridge-Isanti Public School District

Dataset

This dataset was created by Shiro

Contents

It contains the following files:

Context

Seeing as the show is just about to wrap up for good, I figured I'd compile the entirety of the show's episode transcripts (plus other data) into one easily-accessible database.

Content

I acquired the data by scraping https://mlp.fandom.com/wiki/My_Little_Pony_Friendship_is_Magic_Wiki using Python and BeautifulSoup.

Acknowledgements

Thanks to the people who poured their creative energy into this show, and to the people who have been writing the transcripts on the wiki all these years!

The dataset of this paper is collected based on Google, Blockchain, and the Bitcoin market. Generally, there is a total of 26 features, however, a feature whose correlation rate is lower than 0.3 between the variations of price and the variations of feature has been eliminated. Hence, a total of 21 practical features including Market capitalization, Trade-volume, Transaction-fees USD, Average confirmation time, Difficulty, High price, Low price, Total hash rate, Block-size, Miners-revenue, N-transactions-total, Google searches, Open price, N-payments-per Block, Total circulating Bitcoin, Cost-per-transaction percent, Fees-USD-per transaction, N-unique-addresses, N-transactions-per block, and Output-volume have been selected. In addition to the values of these features, for each feature, a new one is created that includes the difference between the previous day and the day before the previous day as a supportive feature. From the point of view of the number and history of the dataset used, a total of 1275 training data were used in the proposed model to extract patterns of Bitcoin price and they were collected from 12 Nov 2018 to 4 Jun 2021.

Dataset

This dataset was created by Chunyu Wei

Contents