173 Open source projects that are alternatives of or similar to Metalearning4nlp Papers

This repository makes the integral Let's Go dataset publicly available.

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ConvAI baseline solution

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

Paper Lists, Notes and Slides, Focus on NLP. For summarization, please refer to https://github.com/xcfcode/Summarization-Papers

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pytorch implementation of Optimization as a Model for Few-shot Learning

This is the Plato Research Dialogue System, a flexible platform for developing conversational AI agents.

This repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study

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Codes and datasets for AAAI-2021 paper "Learning to Pre-train Graph Neural Networks"

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CakeChat: Emotional Generative Dialog System

enhance seq2seq model for open ended dialog generation

Learning to Learn how to Learn: Self-Adaptive Visual Navigation using Meta-Learning (https://arxiv.org/abs/1812.00971)

A resource list for causality in statistics, data science and physics

A collection of extensions and data-loaders for few-shot learning & meta-learning in PyTorch

基于金融-司法领域(兼有闲聊性质)的聊天机器人，其中的主要模块有信息抽取、NLU、NLG、知识图谱等，并且利用Django整合了前端展示,目前已经封装了nlp和kg的restful接口

PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models (IN PROGRESS)

Automated Machine Learning with scikit-learn

Implement korean restaurant reservation dialogue system based on hybrid code network.

Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.

[CVPR'17] Training a Correlation Filter end-to-end allows lightweight networks of 2 layers (600 kB) to high performance at fast speed..

A comprehensive reading list for Emotion Recognition in Conversations

Graph meta learning via local subgraphs (NeurIPS 2020)

Learning Meta Face Recognition in Unseen Domains, CVPR, Oral, 2020

Combine multiple MNIST digits to create datasets with 100/1000 classes for few-shot learning/meta-learning

2019语言与智能技术竞赛-基于知识图谱的主动聊天

Tensorflow Implementation of MAML

Grounded conversational dataset for end-to-end conversational AI (official DSTC7 data)

MoEL: Mixture of Empathetic Listeners

The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].

"Learning to learn by gradient descent by gradient descent "by PyTorch -- a simple re-implementation.

My personal toolkit for PyTorch development.

Code accompanying the ICML-2018 paper "Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace"

Implementation of the paper "Adversarial Attacks on Graph Neural Networks via Meta Learning".

Meta-Feature Extractor

NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

The repo containing the Critical Role Dungeons and Dragons Dataset.

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PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)

The ATIS (Airline Travel Information System) Dataset

Repository for few-shot learning machine learning projects

The code for paper "CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning"

This repo contains implementation of different architectures for emotion recognition in conversations.

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Bilevel Optimization Library in Python for Multi-Task and Meta Learning

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PyTorch implementation of latent space reinforcement learning for E2E dialog published at NAACL 2019. It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU

The source codes of the paper "Improving Few-shot Text Classification via Pretrained Language Representations" and "When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text Classification".

Memory efficient MAML using gradient checkpointing