State-of-the-art deep neural networks are still struggling to address the catastrophic forgetting problem in continual learning. In this paper, we propose one simple paradigm (named as S-Prompting) and two concrete approaches to highly reduce the forgetting degree in one of the most typical continual learning scenarios, i.e., domain increment learning (DIL). The key idea of the paradigm is to learn prompts independently across domains with pre-trained transformers, avoiding the use of exemplars that commonly appear in conventional methods. This results in a win-win game where the prompting can achieve the best for each domain.

A Justice Ministry panel discusses how the voices of individuals should be protected under publicity and portrait rights, amid a rise in the unauthorized use of celebrities' voices by generative artificial intelligence, at the ministry in Tokyo on Friday. An expert panel under the Justice Ministry has agreed that the voices of individuals should be protected under publicity and portrait rights, amid a rise in the unauthorized use of celebrities' voices by generative artificial intelligence. The agreement was made Friday, during the first meeting of the panel on civil compensation claims related to the unauthorized use of celebrities' images and voices by generative AI. The ministry is set to compile guidelines on the scope and standards for illegal acts under current law by this summer. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

This paper proposes a new neural network architecture by introducing an additional dimension called height beyond width and depth. Neural network architectures with height, width, and depth as hyper-parameters are called three-dimensional architectures. It is shown that neural networks with three-dimensional architectures are significantly more expressive than the ones with two-dimensional architectures (those with only width and depth as hyper-parameters), e.g., standard fully connected networks. The new network architecture is constructed recursively via a nested structure, and hence we call a network with the new architecture nested network (NestNet). ANestNet of height sis built with each hidden neuron activated by a NestNet of height s 1.

We study the implicit bias of gradient flow on linear equivariant steerable networks in group-invariant binary classification. Our findings reveal that the parameterized predictor converges in direction to the unique group-invariant classifier with a maximum margin defined by the input group action. Under a unitary assumption on the input representation, we establish the equivalence between steerable networks and data augmentation. Furthermore, we demonstrate the improved margin and generalization bound of steerable networks over their non-invariant counterparts.