Search Results for "voting"

ChatGPT-Shortcut, also known as AiShort, is an open source AI prompt management tool designed to help users quickly find and use effective prompts for large language models. It provides a curated collection of prompts that cover many different scenarios, making it easier for users to obtain useful results from AI systems. Prompts can be browsed, searched, and copied with a single click, allowing users to quickly insert them into AI conversations or workflows. ChatGPT-Shortcut includes...

...The project addresses the problem of how to generate useful reward signals from unlabeled test-time data, and its central insight is that common test-time scaling practices such as majority voting can be repurposed into reward estimates for online reinforcement learning. This makes the framework especially interesting for scenarios where models must keep adapting during evaluation or deployment instead of relying only on fixed pretraining and static fine-tuning. The repository is implemented on top of the verl ecosystem, which allows users to enable TTRL as part of an existing reinforcement learning workflow rather than building a new stack from scratch.

...VoteNet works end-to-end: it learns the voting, aggregation, and bounding-box regression components jointly, enabling strong detection accuracy without relying on 2D proxies or voxelization. The codebase includes data preparation for indoor datasets (SUN RGB-D, ScanNet), training and evaluation scripts, and demo utilities to visualize predicted boxes over point clouds.

Belief revision is a central topic in knowledge representation and reasoning. It consists in incorporating a new belief, changing as few as possible of the original beliefs while preserving consistency. Revision always considers new evidence as a better belief. Such new evidence is usually represented in the form of a propositional formula which must be preserved after the revision. Here, the Δps (PS-Merge) belief merging operator is extended in order to consider constraints, and ...

...To enhance classification performances, we propose an ensemble of classifiers that combine the classification outputs of base classifiers using the simplest and largely used majority voting approach. Instead of creating the ensemble using all base classifiers, we have implemented a genetic algorithm (GA) to search for the best combination from heterogeneous base classifiers. The classification performances achieved by the proposed method method on the chosen datasets are promising.

We propose the use of Differential Evolution algorithm for the weight adjustment of base classifiers used in weighted voting heterogeneous ensemble of classifier. Average Matthews Correlation Coefficient (MCC) score, calculated over 10-fold cross-validation, has been used as the measure of quality of an ensemble. DE/rand/1/bin algorithm has been utilised to maximize the average MCC score calculated using 10-fold cross-validation on training dataset. The voting weights of base classifiers are optimized for the heterogeneous ensemble of classifiers aiming to attain better generalization performances on testing datasets.

Belief merging aims at combining several pieces of (possibly inconsistent) information coming from different sources. The goal is to produce a single consistent set of information, trying to keep the most of the information of the sources. A belief merging operator is the responsible for making the belief merging. With this tool you can compare the ΔΣ, ΔGMax and the Δps (PS-Merge) operators.

Java Machine Learning Library is a library of machine learning algorithms and related datasets. Machine learning techniques include: clustering, classification, feature selection, regression, data pre-processing, ensemble learning, voting, ...