AAAI2023「Are Transformers Effective for Time Series Forecasting?」と、HuggingFace「Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)」の紹介です。
This document discusses generative adversarial networks (GANs) and their relationship to reinforcement learning. It begins with an introduction to GANs, explaining how they can generate images without explicitly defining a probability distribution by using an adversarial training process. The second half discusses how GANs are related to actor-critic models and inverse reinforcement learning in reinforcement learning. It explains how GANs can be viewed as training a generator to fool a discriminator, similar to how policies are trained in reinforcement learning.
1. The document discusses Edward, a Python library for probabilistic modeling, inference, and criticism built on top of TensorFlow. It combines TensorFlow for computation and additional probabilistic programming languages (PPLs) for probabilistic modeling.
2. It provides an overview of TensorFlow's key capabilities like GPU/TPU support and high-level APIs and compares it to PPLs' abilities for probabilistic modeling using distributions and performing inference using techniques like variational inference and MCMC.
3. Edward allows building probabilistic models with TensorFlow and performing inference using techniques from PPLs to take advantage of both frameworks' strengths. This allows tasks like Bayesian neural networks and deep generative models.
- The ABEJA platform provides an AI platform that allows for faster circulation of high-volume data through its capabilities in data collection, storage, training, deployment, inference, and retraining.
- It offers AI-Ops solutions for businesses through machine learning to efficiently annotate large datasets and achieve high-performance pattern recognition.
- ABEJA aims to advance the state-of-the-art in machine learning and deep learning through its international master's program.
ABEJA provides an AI platform that allows for faster circulation of high-volume data through their system which includes data collection, storage, training models, deploying models for inference, and retraining models. The platform aims to make deploying machine learning in production less complex than expected through their AI-Ops services for businesses. Their goal is to implement machine learning to create a more fruitful world.