Efficient Model Predictive Control Implementation The original algorithm can be found: https://www.researchgate.net/profile/Peter-Gibbens-2/publication/270872533_Efficient_Model_Predictive_Control_Algorithm_for_Aircraft/links/54bd90390cf27c8f2814bad5/Efficient-Model-Predictive-Control-Algorithm-for-Aircraft.pdf You can change the system provided with your own and test for convergence of the Quadratic Optimization solver as well, using two QP algorithms Open main.m Define your system parameters and continous time model (LTI) Define your operating point (states X at t = 0), and discretization time. Define weight matrices, upper and lower input constraints (it has to be in order like the example), along with incremental […]

video (3m) | website | paper Can the collective knowledge from a large bank of pretrained vision models be leveraged to improve GAN training? If so, with so many models to choose from, which one(s) should be selected, and in what manner are they most effective? We find that pretrained computer vision models can significantly improve performance when used in an ensemble of discriminators. We propose an effective selection mechanism, by probing the linear separability between real and fake samples […]

Jiyoung Lee, Wonjae Kim, Daehoon Gwak, Edward Choi This repository provides official pytorch implementation of Fourier-Based Decoder which can generate periodic signals. The paper can be found in this link: Conditional Generation of Periodic Signals with Fourier-Based Decoder Pytorch version >= 1.7.1 Python version >= 3.7 This repository is MIT-licensed. Please cite as: @inproceedings{lee2021conditional, title={Conditional Generation of Periodic Signals with Fourier-Based Decoder}, author={Lee, Jiyoung and Kim, Wonjae and Gwak, Daehoon and Choi, Edward}, booktitle={Advances in Neural Information Processing Systems},   […]

Step 1. Genernate random parameters and Run them sequentially : $ python3 collect_data.py -gp -ep -pp -pl pooling -num 10 -shuffle -d 1080ti Step 2. Data Timeline parser : $ python3 preprocess_data.py -pt -pl pooling -d 1080ti Step 3. Combine all raw data : $ python3 preprocess_data.py -c -pl pooling -d 1080ti Step 4. Split raw data to train and test data as performance prediction inputs $ python3 preprocess_data.py -sp -pl pooling -d 1080ti Step 5. Train Model :

The pydantic-collections package provides BaseCollectionModel class that allows youto manipulate collections of pydantic models(and any other types supported by pydantic). Requirements Python >= 3.7 pydantic >= 1.8.2 Installation pip install pydantic-collections Usage Basic usage

Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation at ICML 2018, by Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan. Dependencies The code for L2X runs with Python and requires Tensorflow of version 1.2.1 or higher and Keras of version 2.0 or higher. Please pip install the following packages: numpy tensorflow keras pandas nltk Or you may run the following and in shell to install the required packages: git […]

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?” Usage To replicate our results in Section 4, run: python3 prompt_tune.py –save-dir ../runs/prompt_tuned_sec4/ –prompt-path ../data/binary_NLI_prompts.csv –experiment-name sec4 –few-shots 3,5,10,20,30,50,100,250 –production –seeds 1 Add –fully-train if you want to train on the entire training set in addition to few-shot settings. To replicate Section 5, run: python3 prompt_tune.py –save-dir ../runs/prompt_tuned_sec5/ –prompt-path ../data/binary_NLI_prompts_permuted.csv    

SlotRefine A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling Reference Main paper to be cited (Di Wu et al., 2020) @article{wu2020slotrefine, title={Slotrefine: A fast non-autoregressive model for joint intent detection and slot filling}, author={Wu, Di and Ding, Liang and Lu, Fan and Xie, Jian}, booktitle={EMNLP}, year={2020} } Requirements Our system is build upon the THUMT codebase. tensorflow 1.12python 3.6 Usage sh train.atis.sh GitHub https://github.com/moore3930/SlotRefine    

SMPLicit Implementation for the paper SMPLicit: Topology-aware Generative Model for Clothed People (CVPR 2021) Installation Follow these commands to install SMPLicit in your environment. The required libraries are standard, with the possible exception of Kaolin which requires a particular version to run with the current code. git clonehttps://github.com/enriccorona/SMPLicit cd SMPLicit Install the dependencies listed in requirements.txt: pip install -r requirements.txt In particular, we use Kaolin v0.1 (see installation command) which should be easy to install. However, if you want to […]

PhotochemPy PhotochemPy is a photochemical model of rocky planet’s atmospheres. Given inputs, like the stellar UV flux, the atmospheric temperature structure, etc., this code will find the steady-state chemical composition of an atmosphere, or evolve atmospheres through time. PhotochemPy is a Python wrapper to Fortran source code. This makes the code very speedy, but also user-friendly. Installation Requirements:To install PhotochemPy, you must have the following installed on your system. Python (>3.6.0) with the numpy package. I suggest using anaconda to […]