PyTorch Implement of Context Encoders: Feature Learning by Inpainting

Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems

PyTorch implementation of [Context Encoders: Feature Learning by Inpainting

Official PyTorch code for UAI 2024 paper "ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-variable Context Encoding"

Implement VAE & Context-encoder using PyTorch.

PyTorch implementation of an encoder-decoder network that can recolor images based on context

A PyTorch implementation of the Hierarchical Recurrent Encoder-Decoder model, with an added layer of attention over context states

fork of https://github.com/BoyuanJiang/context_encoder_pytorch, updated for python3 and modified

context-encoder on pytorch

Modified context encoder PyTorch implementation

the pytorch implementation of context_encoder

An implementation in Pytorch showing how to replace the learned position embedding with positional encoding in Meta's OPT models to make the context length unbounded.

SLAM v1 is a transformer-based model combining multi-head self-attention and Encoder Fusion (EF) cycles to optimize efficiency and context processing. It captures global context, improves memory usage, and enhances performance for NLP tasks like text classification. Implemented in PyTorch, SLAM v1 is customizable for various datasets.

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Implement context encoder with PyTorch

Medical image artifact removal using Generative AI

Context Encoder for Image Inpainting using Pytorch

Implementation of Context Encoders : Image Impainting (Pytorch)

My implementation of "Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings" using pytorch.

ContextEncoder-Replication is a PyTorch implementation of Context Encoders, learning image features via inpainting. It includes encoder-decoder, discriminator, masking, and all loss functions in a clean, modular setup.

This project implements a Transformer-based Urdu Conversational Chatbot built from scratch using PyTorch, without any pre-trained models. The chatbot is designed to generate fluent, context-aware Urdu responses using Encoder–Decoder architecture with Multi-Head Attention and Positional Encoding.

chatbot built from scratch in PyTorch that generates empathetic, context-aware responses using multi-head attention, positional encoding, and emotional conditioning on the EmpatheticDialogues dataset.

Transformer architecture built from scratch in PyTorch following "Attention Is All You Need" — custom Multi-Head Attention, Positional Encoding, and Feed-Forward modules with CNN-augmented self-attention for enhanced local context modeling.