[VLDB'22] Anomaly Detection using Transformers, self-conditioning and adversarial training.

PyTorch implementation of some attentions for Deep Learning Researchers.

Deep Xi: A deep learning approach to a priori SNR estimation implemented in TensorFlow 2/Keras. For speech enhancement and robust ASR.

Exploring attention weights in transformer-based models with linguistic knowledge.

"Attention, Learn to Solve Routing Problems!"[Kool+, 2019], Capacitated Vehicle Routing Problem solver

This repository contain various types of attention mechanism like Bahdanau , Soft attention , Additive Attention , Hierarchical Attention etc in Pytorch, Tensorflow, Keras

A Faster Pytorch Implementation of Multi-Head Self-Attention

Visualization for simple attention and Google's multi-head attention.

Multi^2OIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT (Findings of ACL: EMNLP 2020)

Attention-based Induction Networks for Few-Shot Text Classification

Self-Supervised Vision Transformers for multiplexed imaging datasets

several types of attention modules written in PyTorch for learning purposes

This is the official repository of the original Point Transformer architecture.

The original transformer implementation from scratch. It contains informative comments on each block

Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.

Decoding Attention is specially optimized for multi head attention (MHA) using CUDA core for the decoding stage of LLM inference.

2019达观杯信息提取第5名代码

EMNLP 2018: Multi-Head Attention with Disagreement Regularization; NAACL 2019: Information Aggregation for Multi-Head Attention with Routing-by-Agreement

Sentence encoder and training code for Mean-Max AAE

Collection of different types of transformers for learning purposes