RL4RS is a real-world deep reinforcement learning recommender system benchmark for practitioners and researchers. import gym from rl4rs.env.slate import SlateRecEnv, SlateState sim = SlateRecEnv(config, state_cls=SlateState) env = gym.make(‘SlateRecEnv-v0’, recsim=sim) for i in range(epoch): obs = env.reset() for j    

SupermarQ is a suite of application-oriented benchmarks used to measure the performance of quantum computing systems. Installation The SupermarQ package is available via pip and can be installed in your current Python environment with the command: Using SupermarQ The benchmarks are defined as classes within supermarq/benchmarks/. Each applicationdefines two methods; circuit and score. These methods are used to generate the benchmarking circuit and evaluate its performanceafter execution on hardware. The quantum benchmarks within SupermarQ are designed to be scalable, meaning […]

Design-Bench is a benchmarking framework for solving automatic design problems that involve choosing an input that maximizes a black-box function. This type of optimization is used across scientific and engineering disciplines in ways such as designing proteins and DNA sequences with particular functions, chemical formulas and molecule substructures, the morphology and controllers of robots, and many more applications. These applications have significant potential to accelerate research in biochemistry, chemical engineering, materials science, robotics and many other disciplines. We hope this […]

This repository is the pytorch implementation of various High Dynamic Range (HDR) Imaging algorithms. Please find the details below. Maintenance and Contributors @TianhongDai and @WeiLi-THU Requirements pytorch==1.4.0 opencv-python scikit-image==0.17.2 ToDo List adaptive padding add more baselines Supported Algorthms DeepHDR [1] NHDRRNet [2] AHDR [3] DAHDR [4] Instruction download the Kalantari dataset via: [link], and organize the dataset as follows: dataset │ └───Traning │ │ 001 │ │ 002 │ │ 003 │ | … │ └───Test │ 001 │ 002 […]

Benchmarks of how well different two dimensional spaces work for clustering algorithms This may be useful for guiding anyone optimizing using clustering for data science or machine learning problems. It also could be used to make cool animations. Benchmarking is done by putting different numbers of points in the space, letting them repel each other for long enough to achieve equilibrium, and measuring how close they get to everything being equidistant. To get metrics run benchmark.py from the command line. […]

Repository for the Bias Benchmark for QA dataset. Authors: Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel R. Bowman. About BBQ It is well documented that NLP models learnsocial biases present in the world, but littlework has been done to show how these biasesmanifest in actual model outputs for appliedtasks like question answering (QA). We introduce the Bias Benchmark for QA (BBQ), adataset consisting of question-sets constructedby the authors that highlightattestedsocialbiases […]

This repo contains the released version of code and datasets used for our IROS 2021 paper: “Map Compressibility Assessment for LiDAR Registration [link]. If you find the code useful for your work, please cite: @inproceedings{Chang21iros, author = {M.-F. Chang and W. Dong and J.G. Mangelson and M. Kaess and S. Lucey}, title = {Map Compressibility Assessment for {LiDAR} Registration}, booktitle =    

GREW-BENCHMARCH This repository contains the code for our ICCV 2021 paper Gait Recognition in the Wild: A Benchmark GREW-BENCHMARK project is mainly maintained By Xianda Guo and Zheng Zhu. For all main contributors, please check contributing. Dataset Download GREW datasets. You can decompress the compressed package with the following command unzip -P password train.zip (password 是要解压的密码 ) tar -xzvf train.tgz cd train ls *.tgz | xargs -n1 tar xzvf Folder structure after decompression is as follows: