TensorFlow Model Optimization Toolkit — Post-Training Integer Quantization
Blog by TensorFlow: https://medium.com/tensorflow/tensorflow-model-optimization-toolkit-post-training-integer-quantization-b4964a1ea9ba
#tensorflow #artificialintelligence#naturallanguageprocessing
Blog by TensorFlow: https://medium.com/tensorflow/tensorflow-model-optimization-toolkit-post-training-integer-quantization-b4964a1ea9ba
#tensorflow #artificialintelligence#naturallanguageprocessing
Medium
TensorFlow Model Optimization Toolkit — Post-Training Integer Quantization
Since we introduced the Model Optimization Toolkit — a suite of techniques that both novice and advanced developers can use to optimize…
Pytorch-Transformers
A library of state-of-the-art pretrained models for Natural Language Processing (NLP)
By Hugging Face: https://huggingface.co/pytorch-transformers/
#naturallanguageprocessing #nlp #pytorch
A library of state-of-the-art pretrained models for Natural Language Processing (NLP)
By Hugging Face: https://huggingface.co/pytorch-transformers/
#naturallanguageprocessing #nlp #pytorch
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu et al.: https://arxiv.org/abs/1907.11692
#bert #naturallanguageprocessing #unsupervisedlearning
Liu et al.: https://arxiv.org/abs/1907.11692
#bert #naturallanguageprocessing #unsupervisedlearning
arXiv.org
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private...
Modeling question asking using neural program generation
Ziyun Wang and Brenden M. Lake : https://arxiv.org/abs/1907.09899
#artificialintelligence #naturallanguageprocessing #reinforcementlearning
Ziyun Wang and Brenden M. Lake : https://arxiv.org/abs/1907.09899
#artificialintelligence #naturallanguageprocessing #reinforcementlearning
Real-Time Voice Cloning
GitHub, by Corentin Jemine : https://github.com/CorentinJ/Real-Time-Voice-Cloning
#deeplearning #pytorch #tensorflow #naturallanguageprocessing
GitHub, by Corentin Jemine : https://github.com/CorentinJ/Real-Time-Voice-Cloning
#deeplearning #pytorch #tensorflow #naturallanguageprocessing
GitHub
GitHub - CorentinJ/Real-Time-Voice-Cloning: Clone a voice in 5 seconds to generate arbitrary speech in real-time
Clone a voice in 5 seconds to generate arbitrary speech in real-time - CorentinJ/Real-Time-Voice-Cloning
"Advanced NLP with spaCy"
By Ines Montani : https://course.spacy.io
#machinelearning #nlp #naturallanguageprocessing
By Ines Montani : https://course.spacy.io
#machinelearning #nlp #naturallanguageprocessing
CS224N Natural Language Processing with Deep Learning
Stanford University School of Engineering
:
https://www.youtube.com/playlist?list=PLU40WL8Ol94IJzQtileLTqGZuXtGlLMP_
#NaturalLanguageProcessing #MachineLearning #DeepLearning
Stanford University School of Engineering
:
https://www.youtube.com/playlist?list=PLU40WL8Ol94IJzQtileLTqGZuXtGlLMP_
#NaturalLanguageProcessing #MachineLearning #DeepLearning
Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations
Talman et al.: https://arxiv.org/abs/1908.02262
GitHub: https://github.com/Helsinki-NLP/prosody
#dataset #machinelearning #naturallanguageprocessing
Talman et al.: https://arxiv.org/abs/1908.02262
GitHub: https://github.com/Helsinki-NLP/prosody
#dataset #machinelearning #naturallanguageprocessing
arXiv.org
Predicting Prosodic Prominence from Text with Pre-trained...
In this paper we introduce a new natural language processing dataset and
benchmark for predicting prosodic prominence from written text. To our
knowledge this will be the largest publicly...
benchmark for predicting prosodic prominence from written text. To our
knowledge this will be the largest publicly...
Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations
Talman et al.: https://arxiv.org/abs/1908.02262
GitHub: https://github.com/Helsinki-NLP/prosody
#dataset #machinelearning #naturallanguageprocessing
Talman et al.: https://arxiv.org/abs/1908.02262
GitHub: https://github.com/Helsinki-NLP/prosody
#dataset #machinelearning #naturallanguageprocessing
arXiv.org
Predicting Prosodic Prominence from Text with Pre-trained...
In this paper we introduce a new natural language processing dataset and
benchmark for predicting prosodic prominence from written text. To our
knowledge this will be the largest publicly...
benchmark for predicting prosodic prominence from written text. To our
knowledge this will be the largest publicly...
Compressing BERT for faster prediction
Blog by Sam Sucik : https://blog.rasa.com/compressing-bert-for-faster-prediction-2/
#ArtificialIntelligence #NaturalLanguageProcessing #UnsupervisedLearning
Blog by Sam Sucik : https://blog.rasa.com/compressing-bert-for-faster-prediction-2/
#ArtificialIntelligence #NaturalLanguageProcessing #UnsupervisedLearning
Rasa
Learn how to make BERT smaller and faster
Let's look at compression methods for neural networks, such as quantization and pruning. Then, we apply one to BERT using TensorFlow Lite.
Visualizing and Measuring the Geometry of BERT
Coenen et al.: https://arxiv.org/abs/1906.02715
#BERT #NaturalLanguageProcessing #UnsupervisedLearning
Coenen et al.: https://arxiv.org/abs/1906.02715
#BERT #NaturalLanguageProcessing #UnsupervisedLearning
arXiv.org
Visualizing and Measuring the Geometry of BERT
Transformer architectures show significant promise for natural language processing. Given that a single pretrained model can be fine-tuned to perform well on many different tasks, these networks...
ACL 2019 Thoughts and Notes
By Vinit Ravishankar, Daniel Hershcovich; edited by Artur Kulmizev, Mostafa Abdou : https://supernlp.github.io/2019/08/16/acl-2019/
#naturallanguageprocessing #machinelearning #deeplearning
By Vinit Ravishankar, Daniel Hershcovich; edited by Artur Kulmizev, Mostafa Abdou : https://supernlp.github.io/2019/08/16/acl-2019/
#naturallanguageprocessing #machinelearning #deeplearning
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models"
Sandeep Subramanian, Raymond Li, Jonathan Pilault, Christopher Pal : https://arxiv.org/abs/1909.03186
#transformer #naturallanguageprocessing #machinelearning
Sandeep Subramanian, Raymond Li, Jonathan Pilault, Christopher Pal : https://arxiv.org/abs/1909.03186
#transformer #naturallanguageprocessing #machinelearning
arXiv.org
On Extractive and Abstractive Neural Document Summarization with...
We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization. We perform a simple extractive step before...