A Python library for dynamic classifier and ensemble selection
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Updated
Apr 15, 2024 - Python
A Python library for dynamic classifier and ensemble selection
MSGAN: Mode Seeking Generative Adversarial Networks for Diverse Image Synthesis (CVPR2019)
[ICCV-2023] Official code for work "HumanMAC: Masked Motion Completion for Human Motion Prediction".
Code base for the precision, recall, density, and coverage metrics for generative models. ICML 2020.
Search with BERT vectors in Solr, Elasticsearch, OpenSearch and GSI APU
[ECCV 2020] Official PyTorch Implementation of "DLow: Diversifying Latent Flows for Diverse Human Motion Prediction". ECCV 2020.
NAACL 2019: Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation
ECCV2018
The official implementation of "DGCN: Diversified Recommendation with Graph Convolutional Networks" (WWW '21)
Code from the paper "Effective Diversity in Population Based Reinforcement Learning", presented as a spotlight at NeurIPS 2020. This is the Evolution Strategies implementation, but of course the method can be used for gradient based RL algorithms (e.g. TD3).
A python library for the computation of various concentration, inequality and diversity indices
A pytorch implementation for the MMI-anti model
SIREN: A Simulation Framework for Understanding the Effects of Recommender Systems in Online News Environments
creating hybrid-gene phylogenetic trees for diversity analyses
[CVPR 2022] "The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of Redundancy" by Tianlong Chen, Zhenyu Zhang, Yu Cheng, Ahmed Awadallah, Zhangyang Wang
MAP-Elites Hyper-Heuristic based algorithm for generating schedules for the Resource Constrained Project Scheduling Problem
Train LLMs with diverse system messages reflecting individualized preferences to generalize to unseen system messages
MerCat: python code for versatile k-mer counting and diversity estimation for database independent property analysis for meta -ome data
User-controllable Recommendation Against Filter Bubbles
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