Search Results for author:
Found 8 papers, 3 papers with code
Image Captions are Natural Prompts for Text-to-Image Models
With the rapid development of Artificial Intelligence Generated Content (AIGC), it has become common practice in many learning tasks to train or fine-tune large models on synthetic data due to the data-scarcity and privacy leakage problems.
A Comprehensive Survey of Dataset Distillation
Dataset distillation, a dataset reduction method, addresses this problem by synthesizing a small typical dataset from substantial data and has attracted much attention from the deep learning community.
Understanding Deep Learning via Decision Boundary
From the theoretical view, two lower bounds based on algorithm DB variability are proposed and do not explicitly depend on the sample size.
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer
Bayesian neural networks (BNNs) have become a principal approach to alleviate overconfident predictions in deep learning, but they often suffer from scaling issues due to a large number of distribution parameters.
Spectral Complexity-scaled Generalization Bound of Complex-valued Neural Networks
The bound scales with the spectral complexity, the dominant factor of which is the spectral norm product of weight matrices.
Decision boundary variability and generalization in neural networks
Two new notions, algorithm DB variability and $(\epsilon, \eta)$-data DB variability, are proposed to measure the decision boundary variability from the algorithm and data perspectives.
Neural networks behave as hash encoders: An empirical study
We then define an {\it activation hash phase chart} to represent the space expanded by {model size}, training time, training sample size, and the encoding properties, which is divided into three canonical regions: {\it under-expressive regime}, {\it critically-expressive regime}, and {\it sufficiently-expressive regime}.
Accelerating temporal action proposal generation via high performance computing
It is found that, compared to the traditional Parameter Server architecture, our parallel architecture has higher efficiency on temporal action detection task with multiple GPUs, which is suitable for dealing with the tasks of temporal action proposal generation, especially for large datasets of millions of videos.
