LLM research and development initiatives at the National Institute of Informatics
The National Institute of Informatics established the Large Language Model Research and Development Center in April last year. We are leading the llm-jp (LLM Study Group) activities, which have been ongoing since 2023, aiming to build LLMs with high performance on Japanese tasks and enhanced transparency and reliability. This presentation will introduce specific examples, focusing on models we have released so far, including corpus construction for training, training from scratch, tuning and evaluation, and multimodal capabilities.
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国立情報学研究所では昨年4月に大規模言語モデル研究開発センターを開設し、2023年より継続しているllm-jp(L…
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0. What is machine learning?
Weights & Biases
1. Build Your First Machine Learning Model
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Intro to ML: Course Overview
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2. Multi-Layer Perceptrons
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3. Convolutional Neural Networks
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Why Experiment Tracking is Crucial to OpenAI
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4. Autoencoders
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5. Sentiment Analysis
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6. Recurrent Neural Networks [RNNs]
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7. Text Generation using LSTMs and GRUs
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8. Text Classification Using Convolutional Neural Networks
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9. Hybrid LSTMs [Long Short-Term Memory]
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Toyota Research Institute on Experiment Tracking with Weights & Biases
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Introducing Weights & Biases
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10. Seq2Seq Models
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11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
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12. One-shot learning for teaching neural networks to classify objects never seen before
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13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
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14. Data Augmentation | Keras
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15. Batch Size and Learning Rate in CNNs
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Grading Rubric for AI Applications with Sergey Karayev (2019)
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16. Video Frame Prediction using CNNs and LSTMs (2019)
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Image to LaTeX - Applied Deep Learning Fellowship (2019)
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17. Build and Deploy an Emotion Classifier (2019)
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Applied Deep Learning - Data Management with Josh Tobin (2019)
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Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
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Troubleshooting and Iterating ML Models with Lee Redden (2019)
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Designing a Machine Learning Project with Neal Khosla (2019)
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Lukas Beiwald on ML Tools and Experiment Management (2019)
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Building Machine Learning Teams with Josh Tobin (2019)
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Pieter Abeel on Potential Deep Learning Research Directions (2019)
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Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
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Five Lessons for Team-Oriented Research with Peter Welder (2019)
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Applied Deep Learning - Rosanne Liu on AI Research (2019)
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Organizing ML projects — W&B walkthrough (2020)
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Brandon Rohrer — Machine Learning in Production for Robots
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Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
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My experiments with Reinforcement Learning with Jariullah Safi
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Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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VDLS Lavanya Product Walkthrough
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How Linear Algebra is not like Algebra with Charles Frye
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Predicting Protein Structures using Deep Learning with Jonathan King
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Rachael Tatman — Conversational AI and Linguistics
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Reformer by Han Lee
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Sequence Models with Pujaa Rajan
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GitHub Actions & Machine Learning Workflows with Hamel Husain
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Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
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Jack Clark — Building Trustworthy AI Systems
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Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
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Attention for time series forecasting & COVID predictions - Isaac Godfried
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Made with ML - Goku Mohandas
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Angela & Danielle — Designing ML Models for Millions of Consumer Robots
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DeepCamp AI