AI Technology & Industry Review
A research team from DeepMind proposes a bootstrapped meta-learning algorithm that overcomes the meta-optimization problem and myopic meta objectives, and enables the meta-learner to teach itself.
A research team from the University of Montreal and Max Planck Institute for Intelligent Systems constructs a reinforcement learning agent whose knowledge and reward function can be reused across tasks, along with an attention mechanism that dynamically selects unchangeable knowledge pieces to enable out-of-distribution adaptation and generalization.
Imagine the lips forming the Mona Lisa’s famous smile were to part, and she began “speaking” to you. This is not some sci-fi fantasy or a 3D face animation, it’s an effect achieved by researchers from Samsung AI lab and Skolkovo Institute of Science and Technology, who used adversarial learning to generate a photorealistic talking head model.
The Association for Computing Machinery (ACM) has announced the recipient of its 2018 ACM Doctoral Dissertation Award. Chelsea Finn, a PhD student from UC Berkeley, took the top honour with her paper Learning to Learn with Gradients.
Leading machine learning conference International Conference on Learning Representations (ICLR) has named its best research papers of the last year: On the convergence of Adam and Beyond, Spherical CNNs, and Continuous Adaptation via Meta-learning in Nonstationary and Competitive Environments.
The authors introduced a novel approach called deep meta-reinforcement learning (meta-RL) which can rapidly adapt to new tasks.