sfkim

bert

TensorFlow code and pre-trained models for BERT

clarification_question_generation_pytorch

cssAnimation

Css Animation

DRLParaphrase

Paraphrase Generation with Deep Reinforcement Learning

dstc8-schema-guided-dialogue

Schema-Guided Dialogue State Tracking

githubreadmeimagerotation

Repo for testing image rotation bug/feature of github readme.md rendering

google-research

Google Research

keep-coding-n-soju-on

This Repo for practice The Complete Coding Interview Guide in Java

keras-bert

Implementation of BERT that could load official pre-trained models for feature extraction and prediction

learning-to-communicate-pytorch

Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

sfkim/cssAnimation

Css Animation

sfkim/bert

TensorFlow code and pre-trained models for BERT

sfkim/clarification_question_generation_pytorch

sfkim/DRLParaphrase

Paraphrase Generation with Deep Reinforcement Learning

sfkim/dstc8-schema-guided-dialogue

Schema-Guided Dialogue State Tracking

sfkim/githubreadmeimagerotation

Repo for testing image rotation bug/feature of github readme.md rendering

sfkim/google-research

Google Research

sfkim/keep-coding-n-soju-on

This Repo for practice The Complete Coding Interview Guide in Java

sfkim/keras-bert

Implementation of BERT that could load official pre-trained models for feature extraction and prediction

sfkim/learning-to-communicate-pytorch

Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch

sfkim/svoice

We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.

sfkim/The-Complete-Coding-Interview-Guide-in-Java

The Complete Coding Interview Guide in Java, published by Packt

sfkim/transformer-xl

sfkim/wavegan

WaveGAN: Learn to synthesize raw audio with generative adversarial networks