Machine Learning Summer School (MLSS) is a course about modern methods of statistical machine learning and inference. It presents topics which are at the cor...
In a broader mathematical or computational perspective, an optimization problem is defined as a problem of finding the best solution from all feasible solutions. In terms of Machine Learning and…
All AI/Machine learning jobs in one place. Machine learning jobs; remote, on location, interesting companies, and a directory of developers that work with machine learning
Jiqizhixin("The heart of the machine") is China's leading cutting-edge technology media and industry service platform, focusing on artificial intelligence, robotics and neurocognitive science, and insisting on providing high-quality content and various industrial services for practitioners.
机器之心是国内领先的前沿科技媒体和产业服务平台,关注人工智能、机器人和神经认知科学,坚持为从业者提供高质量内容和多项产业服务。
The purpose of AI Magazine is to disseminate timely and informative articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. The articles are selected for appeal to readers engaged in research and
- ARM Research
- Hound: Causal Learning for Datacenter-scale Straggler Diagnosis
- Adaptive Resource Management for Mobile CMPs through Self-awareness
- On-the-fly deterministic binary filters and other on-going work in Machine Learning Systems
- Managed Modularity for Deep Neural Networks
This post discusses the benefits of full-stack data science generalists over narrow functional specialists. The later will help you execute and bring process...
Unlike task-specific algorithms, Deep Learning is a part of Machine Learning family based on learning data representations. With massive amounts of computational power, machines can now recognize…
Turning procedural and structural knowledge into programs has established methodologies, but what about turning knowledge into probabilistic models? I explore a few examples of what such a process could look like.
While deep learning has achieved remarkable success in supervised and reinforcement learning problems, such as image classification, speech recognition, and game playing, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively. This includes:
- goal-conditioned reinforcement learning techniques that leverage the structure of the provided goal space to learn many tasks significantly faster
- meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly
- curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer
This is a graduate-level course. By the end of the course, students will be able to understand and implement the state-of-the-art multi-task learning and meta-learning algorithms and be ready to conduct research on these topics.
Principal component analysis(PCA) is one of the key algorithms that are part of any machine learning curriculum. Initially created in the early 1900s, PCA is a fundamental algorithm to understand…
Recent studies have shown that vision transformer (ViT) models can attain better results than most state-of-the-art convolutional neural networks (CNNs) across various image recognition tasks, and can do so while using considerably fewer computational resources. This has led some researchers to propose ViTs could replace CNNs in this field.However, despite their promising performance, ViTs areContinue Reading