This is the official implementation of the NeurIPS 2021 paper. Method VMAIL simultaneously learns a variational dynamics model and trains an on-policyadversarial imitation learning algorithm in the latent space using only model-basedrollouts. This allows for stable and sample efficient training, as well as zero-shotimitation learning by transfering the learned dynamics model Instructions Get dependencies: conda env create -f vmail.yml conda activate vmail cd robel_claw/robel pip install -e . To train agents for each environmnet download the expert data from the […]

i have been thinking about this one possibility since few dayswhile everyone is talking about consciousness in machines and MLsome scientists said they want to study an entire brain neural network to be able to interpret itbut what if there is a possibility that instead of that we define a sophisticated set of rules instead?i call this the “set of rules approach” to achieve minimal consciousnessi mean to give human like consciousness(at any level) to machinewe need to train it […]

This repo is the official implementation of our paper “Sample Prior Guided Robust Model Learning to Suppress Noisy Labels“. Citation If you use this code/data for your research, please cite our paper “Sample Prior Guided Robust Model Learning to Suppress Noisy Labels“. @misc{chen2021sample, title={Sample Prior Guided Robust Model Learning to Suppress Noisy Labels}, author={Wenkai Chen and Chuang Zhu and Yi Chen}, year={2021}, eprint={2112.01197}, archivePrefix={arXiv}, primaryClass={cs.CV} } Training Take CIFAR-10 with 50% symmetric noise as an example: First, please modify the […]

This repository provides the implementation of our CIKM 2021 paper titled as “Cross-Market Product Recommendation“. Please consider citing our paper if you find the code and XMarket dataset useful in your research. The general schema of our FOREC recommendation system is shown below. For a pair of markets, the middle part shows the market-agnostic model that we pre-train, and then fork and fine-tune for each market shown in the left and right. Note that FOREC is capable of working with […]

This is an implementation of the following paper: Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, Vikas Chandra.Federated Learning with Non-IID DataarXiv:1806.00582. Paper TL;DR: Previous federated optization algorithms (such as FedAvg and FedProx) converge to stationary points of a mismatched objective function due to heterogeneity in data distribution. In this paper, the authors propose a data-sharing strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge […]

TLDR (Twin Learning for Dimensionality Reduction) is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self-supervised learning losses. Inspired by manifold learning, TLDR uses nearest neighbors as a way to build pairs from a training set and a redundancy reduction loss to learn an encoder that produces representations invariant across such pairs. Similar to other neighborhood embeddings, TLDR effectively and unsupervisedly learns low-dimensional spaces where local neighborhoods of the input space […]

Dual Adaptive Sampling for Machine Learning Interatomic potential. How to cite If you use this code in your research, please cite this using: Hongliang Yang, Yifan Zhu, Erting Dong, Yabei Wu, Jiong Yang, and Wenqing Zhang. Dual adaptive sampling and machine learning interatomic potentials for modeling materials with chemical bond hierarchy. Phys. Rev. B 104, 094310 (2021). Install Install pymtp You should first install the python interface for mtp: https://github.com/hlyang1992/pymtp Install das You can download the code by

In this project, the dataset was created through a survey opened on Google forms.The purpose of the form is to find the person’s favorite shopping type based on the information provided. In this context, 13 questions were asked to the user.As a result of these questions, the estimation of the shopping type, which is a classification problem, will be carried out with 5 different algorithms. These algorithms; Logistic Regression Random Forest Classifier Support Vector Machine K Neighbors Decision Tree algorithms […]