289 Open source projects that are alternatives of or similar to Learning-From-Rules

Self-training with Weak Supervision (NAACL 2021)

Weakly Supervised End-to-End Learning (NeurIPS 2021)

A PyTorch-based open-source framework that provides methods for improving the weakly annotated data and allows researchers to efficiently develop and compare their own methods.

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Dataset for paper "Weak Supervision for Fake News Detection via Reinforcement Learning" published in AAAI'2020.

A system for quickly generating training data with weak supervision

Weakly supervised medical named entity classification

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WRENCH: Weak supeRvision bENCHmark

Official code of our work, Unified Pre-training for Program Understanding and Generation [NAACL 2021].

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The standard package for machine learning with noisy labels, finding mislabeled data, and uncertainty quantification. Works with most datasets and models.

No description or website provided.

A curated list of programmatic weak supervision papers and resources

Framework to learn Named Entity Recognition models without labelled data using weak supervision.

Pytorch implementation of Paper by Google Research - Representation Learning for Information Extraction from Form-like Documents.

SPEAR: Programmatically label and build training data quickly.

PyTorch implementation of AAAI 2021 paper: A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization

our code for paper 'PointCutMix: Regularization Strategy for Point Cloud Classification'

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中文NER的那些事儿

Poincaré Embedding (unofficial)

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A curated (most recent) list of resources for Learning with Noisy Labels

[NeurIPS 2021] COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining

An extended TSP (Time Stretched Pulse). CAPRICEP substantially replaces FVN. CAPRICEP enables interactive and real-time measurement of the linear time-invariant, the non-linear time-invariant, and random and time varying responses simultaneously.

Implementation of modern data augmentation techniques in TensorFlow 2.x to be used in your training pipeline.

A generalizable application framework for segmentation, regression, and classification using PyTorch

TKDE 22. CCCL: Contrastive Cascade Graph Learning.

PyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

a tensorflow version for SEC approach in the paper "seed, expand and constrain: three principles for weakly-supervised image segmentation".

Tensor2tensor experiment with SpecAugment

Implements the ideas presented in https://arxiv.org/pdf/2004.11362v1.pdf by Khosla et al.

Hierarchy-based Image Embeddings for Semantic Image Retrieval

Using efficientnet to provide embeddings for retrieval

Artificial intelligence/machine learning course at UCF in Spring 2020 (Fall 2019 and Spring 2019)

Code for the paper "VAE with a VampPrior", J.M. Tomczak & M. Welling

I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers

Object-aware Contrastive Learning for Debiased Scene Representation (NeurIPS 2021)

Contrastive Predictive Coding for Automatic Speaker Verification

Image rotation and cropping out the black borders in TensorFlow

Bio-Computing Platform featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集

Implementation for "3D human pose, shape and texture from low-resolution images and videos", TPAMI 2021

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Scribbles or Points-based weakly-supervised learning for medical image segmentation, a strong baseline, and tutorial for research and application.

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Winning Solution of the NeurIPS 2020 Competition on Predicting Generalization in Deep Learning

Vector Quantized Autoregressive Predictive Coding (VQ-APC)

SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners

The Official PyTorch Implementation of DiscoBox.

A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"

PyTorch code for NeurIPSW 2020 paper (4th Workshop on Meta-Learning) "Few-Shot Unsupervised Continual Learning through Meta-Examples"

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SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data (AAAI 2021)

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