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John D. Pope's Projects

selfreconcode icon selfreconcode

This repository contains a pytorch implementation of "SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Video (CVPR 2022, Oral)".

semanticsegmentation icon semanticsegmentation

A framework for training segmentation models in pytorch on labelme annotations with pretrained examples of skin, cat, and pizza topping segmentation

sememepso-attack icon sememepso-attack

Code and data of the ACL 2020 paper "Word-level Textual Adversarial Attacking as Combinatorial Optimization"

semeval-2018-affect-in-tweets icon semeval-2018-affect-in-tweets

automatically determine the intensity of emotions (E) and intensity of sentiment (aka valence V) of the tweeters from their tweets

semi_perso_user_cold_start icon semi_perso_user_cold_start

Source code from the KDD 2021 article "A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps" by L. Briand, G. Salha-Galvan, W. Bendada, M. Morlon and V.A. Tran

semit icon semit

Image to Image translation, image generataton, few shot learning

semsegpipeline icon semsegpipeline

A simpler way of reading and augmenting image segmentation data into TensorFlow

sendover icon sendover

Tools for creating and paying invoices privately on Bitcoin SV

senet icon senet

Squeeze-and-Excitation Networks

sense2vec icon sense2vec

💥 Use spaCy to go beyond vanilla word2vec

sensor2 icon sensor2

CMSensorRecorder direct to DynamoDB experiments

sentaugment icon sentaugment

SentAugment is a data augmentation technique for NLP that retrieves similar sentences from a large bank of sentences. It can be used in combination with self-training and knowledge-distillation, or for retrieving paraphrases.

sentence-doctor icon sentence-doctor

Many Natural Language Processing tasks rely on sentence boundary detection (SBD). Although amazing libraries like spacy provide state of the art SBD, they often depend on text extractors (e.g pdf text extractors or OCR). The quality of these extractors greatly influence the quality of SBD libraries and as a consequence, the performance of downstream models as well. To help address this problem, we fine-tuned a T5 model from the hugging face hub that attempts to reconstruct “broken sentences”

sentencepiece icon sentencepiece

Unsupervised text tokenizer for Neural Network-based text generation.

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