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Steven Du's Projects

fairseq-py icon fairseq-py

Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

faiss icon faiss

A library for efficient similarity search and clustering of dense vectors.

familia icon familia

A Toolkit for Industrial Topic Modeling

fant icon fant

Filtering and Noise Adding Tool

fast-transe icon fast-transe

An Efficient implementation of TransE for Knowledge Representation Learning

fast_abs_rl icon fast_abs_rl

Code for ACL 2018 paper: "Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting. Chen and Bansal"

fast_rgf icon fast_rgf

Multi-core implementation of Regularized Greedy Forest

fastai icon fastai

The fastai deep learning library, plus lessons and tutorials

fastannotationtool icon fastannotationtool

A tool using OpenCV to annotate images for image classification, optical character reading, ...

faster-rnnlm icon faster-rnnlm

Faster Recurrent Neural Network Language Modeling Toolkit with Noise Contrastive Estimation and Hierarchical Softmax

fasttext icon fasttext

Library for fast text representation and classification.

fate icon fate

An Industrial Grade Federated Learning Framework

favos icon favos

Demo code of the paper "Fast and Accurate Online Video Object Segmentation via Tracking Parts", in CVPR 2018

fb.resnet.torch icon fb.resnet.torch

Torch implementation of ResNet from http://arxiv.org/abs/1512.03385 and training scripts

fcis icon fcis

Fully Convolutional Instance-aware Semantic Segmentation

fcn.berkeleyvision.org icon fcn.berkeleyvision.org

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

feast icon feast

Feature Store for Machine Learning

feat2vec icon feat2vec

Code of NAACL paper "Unsupervised Multi-Domain Adaptation with Feature Embeddings"

fedml icon fedml

FedML: A Research Library and Benchmark for Federated Machine Learning

few-shot-nlg icon few-shot-nlg

Code and Data for paper "Few-Shot NLG with Pre-Trained Language Model" https://arxiv.org/abs/1904.09521

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