Search Results for "competition"

LiteMultiAgent is a lightweight and extensible multi-agent reinforcement learning (MARL) platform designed for rapid experimentation. It allows researchers to design and test coordination, competition, and collaboration scenarios in simulated environments.

...The V2 model is expected to support more advanced features like better context window handling, more efficient inference, better performance on challenging tasks, and stronger alignment with human feedback. Because DeepSeek is pushing open-weight competition, this V2 iteration is meant to solidify its position in benchmark rankings and in developer adoption. The code in the repository may include description files, support for tool use or plug-in architectures, and artifacts showing fine-tuning or prompt templates.

DeepSeekMath-V2 is a large-scale open-source AI model designed specifically for advanced mathematical reasoning, theorem proving, and rigorous proof verification. It’s built by DeepSeek as a successor to their earlier math-specialist models. Unlike general-purpose LLMs that might generate plausible-looking math but sometimes hallucinate or mishandle rigorous logic, Math-V2 is engineered to not only generate solutions but also self-verify them, meaning it examines the derivations, checks...

This website is a free, open-source guide on prompt engineering. Contributions are welcome! Harsh criticism is welcome too. We launched the first ever prompt hacking competition designed to enhance AI safety and education by challenging participants to outsmart large language models from May 5th to June 3rd! The competition featured 10 increasingly difficult levels of prompt hacking defenses and the chance to win over $35,000 in prizes. Coding is a great skill to learn alongside prompt engineering. We recommend learning Python, as it is a popular language for AI and machine learning. ...

...The V2 version introduces a fully convolutional masked autoencoder (FCMAE) framework where parts of the image are masked and the network reconstructs the missing content, marrying convolutional inductive bias with powerful pretraining. A key innovation is a new Global Response Normalization (GRN) layer added to the ConvNeXt backbone, which enhances feature competition across channels. The result is a convnet that competes strongly with transformer architectures on recognition benchmarks while being efficient and hardware-friendly. The repository provides official PyTorch implementations for multiple model sizes (Atto, Femto, Pico, up through Huge), conversion from JAX weights, code for pretraining/fine-tuning, and pretrained checkpoints. ...

In practice, a higher mAP value indicates a better performance of your neural net, given your ground truth and set of classes. The performance of your neural net will be judged using the mAP criteria defined in the PASCAL VOC 2012 competition. We simply adapted the official Matlab code into Python (in our tests they both give the same results). First, your neural net detection-results are sorted by decreasing confidence and are assigned to ground-truth objects. We have "a match" when they share the same label and an IoU >= 0.5 (Intersection over Union greater than 50%). ...

The Kaggle-EEG project is a machine learning solution developed for seizure prediction from EEG data, achieving 3rd place in the Kaggle/University of Melbourne Seizure Prediction competition. The repository processes EEG data to predict seizures by training machine learning models, specifically using SVM (Support Vector Machine) and RUS Boosted Tree ensemble models. The framework processes EEG data into features, trains models, and outputs predictions, handling temporal data to ensure accuracy.

JCAT is the platform for TAC Market Design Competition and an ideal tool for experimental mechanism design research. It allows multiple electronic markets to compete against each other, and trading agents, as traders, to move between them.

Project aim to provide simple easy APIs for Java developers to use interactive abilities in their Java Applications like speech recognition, handwriting recognition, use of web cam , sound record/play, decision trees , text to speech and many others.

RARS is the Robot Auto Racing Simulation, in which the drivers are robot programs. It is intended as a competition among programmers. It consists of a simulation of the physics of cars, a graphic display of the race, and a robot driver for each car.

SimpleDomino is a Server for a Programming Competition from Schoolinux and Linux-dubai.com for School Students. It uses TCP/IP for Communications. (It's base on Linux but can port to Windows) You can start your own Competition or Help us to develop this P

MARDG is a project to design a robot team to compete in the BYU Robot Soccer competition of 2004.

Development of a snazzy Java program to simulate the growth and competition of entities in a limited environment.