2 citations · 3 across the 6 of their papers we have counts for
6 papers
Mathematical Principles and Experimental Discoveries of the Emergence of Symbolic Patterns in Artificial Neural Networks
Quanshi Zhang, Qihan Ren, Siyu Lou
Artificial Neural networks (ANNs) are often treated as black-box models, making explainability a central challenge in deep learning. Many engineering methods have been proposed to…
RRAttention: Dynamic Block Sparse Attention via Per-Head Round-Robin Shifts for Long-Context Inference
Siran Liu, Guoxia Wang, Sa Wang +7
The quadratic complexity of attention mechanisms poses a critical bottleneck for large language models processing long contexts. While dynamic sparse attention methods offer input-…
Defining and Extracting generalizable interaction primitives from DNNs
Lu Chen, Siyu Lou, Benhao Huang +1
Faithfully summarizing the knowledge encoded by a deep neural network (DNN) into a few symbolic primitive patterns without losing much information represents a core challenge in ex…
HarsanyiNet: Computing Accurate Shapley Values in a Single Forward Propagation
Lu Chen, Siyu Lou, Keyan Zhang +2
The Shapley value is widely regarded as a trustworthy attribution metric. However, when people use Shapley values to explain the attribution of input variables of a deep neural net…
Bayesian Neural Networks Avoid Encoding Complex and Perturbation-Sensitive Concepts
Qihan Ren, Huiqi Deng, Yunuo Chen +2
In this paper, we focus on mean-field variational Bayesian Neural Networks (BNNs) and explore the representation capacity of such BNNs by investigating which types of concepts are…
Audio-text Retrieval in Context
Siyu Lou, Xuenan Xu, Mengyue Wu +1
Audio-text retrieval based on natural language descriptions is a challenging task. It involves learning cross-modality alignments between long sequences under inadequate data condi…