Python Emnlp2020-debiasing-unknown Resources

Voronoi Multi_Robot Collaborate Exploration Introduction In the unknown environment, the cooperative exploration of multiple robots is completed by Vo

Kalman Filter Exercise What are Kalman Filters? A Kalman Filter is a algorithmic

GNN_PPI Codes and models for the paper "Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction". Lear

Unknown Object Segmentation from Stereo Images Unknown Object Segmentation from Stereo Images Maximilian Durner*, Wout Boerdijk*, Martin Sundermeyer,

SSUL - Official Pytorch Implementation (NeurIPS 2021) SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning Sun

BERT-ATTACK Code for our EMNLP2020 long paper: BERT-ATTACK: Adversarial Attack Against BERT Using BERT Dependencies Python 3.7 PyTorch 1.4.0 transform

FAR Planner uses a dynamically updated visibility graph for fast replanning. The planner models the environment with polygons and builds a global visi

Likelihood-Free Inference in State-Space Models with Unknown Dynamics This package contains the codes required to run the experiments in the paper. Th

CUP-DNN CUP-DNN is a deep neural network model used to predict tissues of origin for cancers of unknown of primary. The model was trained on the expre

Residual2Vec: Debiasing graph embedding using random graphs This repository contains the code for S. Kojaku, J. Yoon, I. Constantino, and Y.-Y. Ahn, R

DisUnknown: Distilling Unknown Factors for Disentanglement Learning See introduction on our project page Requirements PyTorch = 1.8.0 torch.linalg.ei

DEAR (Deep Evidential Action Recognition) Project | Paper & Supp Wentao Bao, Qi Yu, Yu Kong International Conference on Computer Vision (ICCV Oral), 2

Self-Diagnosis and Self-Debiasing This repository contains the source code for Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based

SOFA This repository is the implementation of SOFA, the Simulator for OFfline leArning and evaluation. Keeping Dataset Biases out of the Simulation: A

SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo Thomas Kollar, Michael Laskey, Kevin Stone, Brijen Thananjeyan

SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo Thomas Kollar, Michael Laskey, Kevin Stone, Brijen Thananjeyan

Towards Debiasing NLU Models from Unknown Biases Abstract: NLU models often exploit biased features to achieve high dataset-specific performance witho

AutoDebias This is the official pytorch implementation of AutoDebias, a debiasing method for recommendation system. AutoDebias is proposed in the pape

NeRFactor [Paper] [Video] [Project] This is the authors' code release for: NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown I

Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback About This repository accompanies the real-world experiments conducted i

Debiasing Item-to-Item Recommendations With Small Annotated Datasets This is the code for our RecSys '20 paper. Other materials can be found here: Ful

Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution.

pair-emnlp2020 Official repository for the paper: Xinyu Hua and Lu Wang: PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long

Unknown-Horizons based on Fifengine is no longer in development. We are porting it to Godot Engine. Please dont report any new bugs. Only bugfixes wil