With rapid advances in generative artificial intelligence, the text-to-music synthesis task has emerged as a promising direction for music generation from scratch.
We present a simple yet novel parameterized form of linear mapping to achieves remarkable network compression performance: a pseudo SVD called Ternary SVD (TSVD).
Despite the task's significance, prevailing generative models exhibit limitations in music quality, computational efficiency, and generalization.
Ranked #1 on Text-to-Music Generation on MusicCaps
We propose theoretical analyses of a modified natural gradient descent method in the neural network function space based on the eigendecompositions of neural tangent kernel and Fisher information matrix.
The FedDKD introduces a module of decentralized knowledge distillation (DKD) to distill the knowledge of the local models to train the global model by approaching the neural network map average based on the metric of divergence defined in the loss function, other than only averaging parameters as done in literature.
Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest.
However, succeeding researches pointed out that limited by the uncontrolled nature of attention computation, the NMT model requires an external syntax to capture the deep syntactic awareness.
We present BN-NAS, neural architecture search with Batch Normalization (BN-NAS), to accelerate neural architecture search (NAS).
Then, a compact set of the possible combinations for different token pooling and attention sharing mechanisms are constructed.
We introduce the first Neural Architecture Search (NAS) method to find a better transformer architecture for image recognition.
Ranked #494 on Image Classification on ImageNet
This paper presents a method for gaze estimation according to face images.
The automation of neural architecture design has been a coveted alternative to human experts.
Meta-learning is a promising method to achieve efficient training method towards deep neural net and has been attracting increases interests in recent years.