Theory

SVDinsTN: A Tensor Network Paradigm for Efficient Structure Search from Regularized Modeling Perspective CVPR 2024 Symmetries in Overparametrized Neural Networks: A Mean Field View NeurIPS 2024 Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed NeurIPS 2024 The Challenges of the Nonlinear Regime for Physics-Informed Neural Networks NeurIPS 2024 The Effect of Generalisation on the Inadequacy of the Mode EACL 2024 The effect of Leaky ReLUs on the training and generalization of overparameterized networks AISTATS 2024 The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof NeurIPS 2024 The Expressive Capacity of State Space Models: A Formal Language Perspective NeurIPS 2024 The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks NeurIPS 2024 The Fine-Grained Complexity of Gradient Computation for Training Large Language Models NeurIPS 2024 The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective JMLR 2024 The Impact of Geometric Complexity on Neural Collapse in Transfer Learning NeurIPS 2024 The Implicit Bias of Gradient Descent on Separable Multiclass Data NeurIPS 2024 The Implicit Bias of Gradient Descent toward Collaboration between Layers: A Dynamic Analysis of Multilayer Perceptions NeurIPS 2024 The Ladder in Chaos: Improving Policy Learning by Harnessing the Parameter Evolving Path in A Low-dimensional Space NeurIPS 2024 The Loss Landscape of Deep Linear Neural Networks: a Second-order Analysis JMLR 2024 The Mystery of the Pathological Path-star Task for Language Models EMNLP 2024 The Prevalence of Neural Collapse in Neural Multivariate Regression NeurIPS 2024 Topological Expressivity of ReLU Neural Networks COLT 2024 Topological Generalization Bounds for Discrete-Time Stochastic Optimization Algorithms NeurIPS 2024 Topological obstruction to the training of shallow ReLU neural networks NeurIPS 2024 Towards a theory of how the structure of language is acquired by deep neural networks NeurIPS 2024 Towards Efficient Verification of Quantized Neural Networks AAAI 2024 Towards Exact Gradient-based Training on Analog In-memory Computing NeurIPS 2024 Towards General Robustness Verification of MaxPool-based Convolutional Neural Networks via Tightening Linear Approximation CVPR 2024