doi.bio/max_jaderberg

Max Jaderberg

Max Jaderberg is a researcher in the field of machine learning and artificial intelligence. He has contributed to a range of influential papers in the field, particularly in the area of convolutional neural networks and reinforcement learning.

Education and Career

Jaderberg's academic background and career history are currently unknown. However, he has collaborated with researchers from DeepMind, Microsoft AI, University of Oxford, and Google DeepMind, indicating strong connections with these institutions.

Notable Works

Jaderberg has co-authored several notable papers with leading researchers in the field. Some of his most influential works include:

• Spatial Transformer Networks: This paper introduces the Spatial Transformer, a module that enables neural networks to manipulate data spatially. This allows for active spatial transformation of feature maps within the network.
• Reinforcement Learning with Unsupervised Auxiliary Tasks: In this paper, Jaderberg and his co-authors present a method that significantly outperforms previous results on Atari games and Labyrinth tasks, achieving up to 880% expert human performance on Atari.
• Population Based Training of Neural Networks: The paper introduces Population Based Training, an asynchronous optimisation algorithm that optimises a population of models and their hyperparameters to maximise performance.
• Speeding up Convolutional Neural Networks with Low Rank Expansions: Jaderberg and his colleagues propose two schemes to speed up convolutional neural networks by exploiting filter redundancy and constructing low-rank bases of filters.
• Deep Features for Text Spotting: This work presents a Convolutional Neural Network classifier for text spotting in natural images and introduces an automated data mining method for Flickr to generate annotations for an end-to-end text spotting system.
• Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition: The paper presents a framework for recognising natural scene text without relying on human-labelled data.

Co-authors

Jaderberg has collaborated with several prominent researchers and scientists in the field of AI and machine learning, including Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu, Andrea Vedaldi, Oriol Vinyals, Thore Graepel, and many others.

Max Jaderberg

Early Life and Education

Max Jaderberg is a researcher in the field of machine learning and artificial intelligence. He has made significant contributions to the development of convolutional neural networks and reinforcement learning algorithms.

Career and Research

Jaderberg is known for his work on the AlphaStar agent, which utilizes a multi-agent reinforcement learning algorithm to achieve Grandmaster level performance in the real-time strategy game StarCraft II. He has also published extensively on the topic of text recognition in natural scenes, developing end-to-end systems that can localize and recognize text in images, with applications in image retrieval and news footage searchability.

One of Jaderberg's notable contributions is the introduction of the Spatial Transformer, a learnable module that enables the spatial manipulation of data within a neural network. This module can be inserted into existing convolutional architectures, enhancing their ability to actively transform feature maps.

Jaderberg has also worked on optimizing convolutional neural networks, presenting two schemes that drastically speed up their performance by exploiting cross-channel and filter redundancy.

Publications

• Spatial Transformer Networks (co-authored with K. Simonyan, Andrew Zisserman, and K. Kavukcuoglu)
• Grandmaster level in StarCraft II using multi-agent reinforcement learning (co-authored with O. Vinyals, Igor Babuschkin, David Silver, and others)
• Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition (co-authored with K. Simonyan, A. Vedaldi, and Andrew Zisserman)
• Reading Text in the Wild with Convolutional Neural Networks (co-authored with K. Simonyan, A. Vedaldi, and Andrew Zisserman)
• Reinforcement Learning with Unsupervised Auxiliary Tasks (co-authored with Volodymyr Mnih, K. Kavukcuoglu, and others)
• Population Based Training of Neural Networks (co-authored with Valentin Dalibard, K. Kavukcuoglu, and others)
• Speeding up Convolutional Neural Networks with Low Rank Expansions (co-authored with A. Vedaldi and Andrew Zisserman)
• Deep Features for Text Spotting (co-authored with A. Vedaldi and Andrew Zisserman)

Professional Affiliations

Jaderberg has collaborated with researchers from various institutions, including the University of Oxford, DeepMind, Google, Microsoft AI, UCL, and the University of Toronto.

Impact and Influence

Jaderberg's publications have been highly influential, with his work on Spatial Transformer Networks being particularly well-cited. His contributions to the field of machine learning and AI have advanced the state-of-the-art in areas such as text recognition, reinforcement learning, and neural network optimization.

Max Jaderberg

Max Jaderberg is a researcher in the field of machine learning and artificial intelligence. He has contributed to a range of influential papers in the field, particularly in the application of machine learning to text recognition and reinforcement learning.

Biography

Jaderberg's research focuses on the development and application of machine learning algorithms, particularly in the area of text recognition and reinforcement learning. He has worked with a number of notable researchers in the field, including Karen Simonyan, Andrew Zisserman, Koray Kavukcuoglu, and Andrea Vedaldi.

Notable Works

• Spatial Transformer Networks: This work introduces a new module, the Spatial Transformer, which allows for the spatial manipulation of data within a neural network. This module can be inserted into existing convolutional architectures, enabling neural networks to actively transform feature maps.
• Reinforcement Learning with Unsupervised Auxiliary Tasks: In this paper, Jaderberg and his co-authors present a method that significantly outperforms previous results on Atari games, achieving 880% expert human performance. The method also shows strong results on first-person, three-dimensional Labyrinth tasks, with a mean speedup in learning of 10x.
• Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition: Jaderberg and his colleagues present a framework for recognizing natural scene text that does not require any human-labelled data. The framework performs word recognition on the entire image holistically.
• Reading Text in the Wild with Convolutional Neural Networks: This work demonstrates an end-to-end system for text spotting (localizing and recognizing text in natural scene images) and text-based image retrieval. The system has real-world applications, such as making news footage instantly searchable via text queries.
• Population Based Training of Neural Networks: Jaderberg and his co-authors propose Population Based Training, a simple asynchronous optimization algorithm. This algorithm jointly optimizes a population of models and their hyperparameters to maximize performance within a fixed computational budget.

Co-Authors

• Karen Simonyan (Chief Scientist, Microsoft AI)
• Andrew Zisserman (University of Oxford)
• Koray Kavukcuoglu (DeepMind)
• Andrea Vedaldi (University of Oxford)
• Oriol Vinyals (Research Scientist, Google DeepMind)
• Thore Graepel (Global Lead Computational Science, AI & ML at Altos Labs; Chair of Machine Learning, UCL)
• And others

Affiliations

Jaderberg is currently affiliated with DeepMind, as evidenced by his co-authors on recent papers.