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Found 10 papers, 3 papers with code
Demystifying Lazy Training of Neural Networks from a Macroscopic Viewpoint
While NTK typically assumes that $\lim_{m\to\infty}\frac{\log \kappa}{\log m}=\frac{1}{2}$, and imposes each weight parameters to scale by the factor $\frac{1}{\sqrt{m}}$, in our theta-lazy regime, we discard the factor and relax the conditions to $\lim_{m\to\infty}\frac{\log \kappa}{\log m}>0$.
Triple GNNs: Introducing Syntactic and Semantic Information for Conversational Aspect-Based Quadruple Sentiment Analysis
This necessitates a dual focus on both the syntactic information of individual utterances and the semantic interaction among them.
AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language Models
Given the importance of ancient Chinese in capturing the essence of rich historical and cultural heritage, the rapid advancements in Large Language Models (LLMs) necessitate benchmarks that can effectively evaluate their understanding of ancient contexts.
Dynamic Multi-Scale Context Aggregation for Conversational Aspect-Based Sentiment Quadruple Analysis
Conversational aspect-based sentiment quadruple analysis (DiaASQ) aims to extract the quadruple of target-aspect-opinion-sentiment within a dialogue.
Stochastic Modified Equations and Dynamics of Dropout Algorithm
In order to investigate the underlying mechanism by which dropout facilitates the identification of flatter minima, we study the noise structure of the derived stochastic modified equation for dropout.
Understanding the Initial Condensation of Convolutional Neural Networks
Previous research has shown that fully-connected networks with small initialization and gradient-based training methods exhibit a phenomenon known as condensation during training.
Phase Diagram of Initial Condensation for Two-layer Neural Networks
The phenomenon of distinct behaviors exhibited by neural networks under varying scales of initialization remains an enigma in deep learning research.
Embedding Principle: a hierarchical structure of loss landscape of deep neural networks
We prove a general Embedding Principle of loss landscape of deep neural networks (NNs) that unravels a hierarchical structure of the loss landscape of NNs, i. e., loss landscape of an NN contains all critical points of all the narrower NNs.
Nonlinear Weighted Directed Acyclic Graph and A Priori Estimates for Neural Networks
In an attempt to better understand structural benefits and generalization power of deep neural networks, we firstly present a novel graph theoretical formulation of neural network models, including fully connected, residual network (ResNet) and densely connected networks (DenseNet).
Towards an Understanding of Residual Networks Using Neural Tangent Hierarchy (NTH)
Gradient descent yields zero training loss in polynomial time for deep neural networks despite non-convex nature of the objective function.
