5 citations · 7 across the 8 of their papers we have counts for
8 papers
Technical Report: Quantifying and Analyzing the Generalization Power of a DNN
Yuxuan He, Junpeng Zhang, Lei Cheng +2
This paper proposes a new perspective for analyzing the generalization power of deep neural networks (DNNs), i.e., directly disentangling and analyzing the dynamics of generalizabl…
Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k
Zangwei Zheng, Xiangyu Peng, Yuxuan Lou +30
Video generation models have achieved remarkable progress in the past year. The quality of AI video continues to improve, but at the cost of larger model size, increased data quant…
Revisiting Generalization Power of a DNN in Terms of Symbolic Interactions
Lei Cheng, Junpeng Zhang, Qihan Ren +1
This paper aims to analyze the generalization power of deep neural networks (DNNs) from the perspective of interactions. Unlike previous analysis of a DNN's generalization power in…
Randomness of Low-Layer Parameters Determines Confusing Samples in Terms of Interaction Representations of a DNN
Junpeng Zhang, Lei Cheng, Qing Li +2
In this paper, we find that the complexity of interactions encoded by a deep neural network (DNN) can explain its generalization power. We also discover that the confusing samples…
Disentangling Regional Primitives for Image Generation
Zhengting Chen, Lei Cheng, Lianghui Ding +2
This paper explains a neural network for image generation from a new perspective, i.e., explaining representation structures for image generation. We propose a set of desirable pro…
Layerwise Change of Knowledge in Neural Networks
Xu Cheng, Lei Cheng, Zhaoran Peng +3
This paper aims to explain how a deep neural network (DNN) gradually extracts new knowledge and forgets noisy features through layers in forward propagation. Up to now, although th…
Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering
Yichi Zhang, Zhuo Chen, Yin Fang +4
Deploying large language models (LLMs) to real scenarios for domain-specific question answering (QA) is a key thrust for LLM applications, which poses numerous challenges, especial…
Can the Inference Logic of Large Language Models be Disentangled into Symbolic Concepts?
Wen Shen, Lei Cheng, Yuxiao Yang +2
In this paper, we explain the inference logic of large language models (LLMs) as a set of symbolic concepts. Many recent studies have discovered that traditional DNNs usually encod…