Optimization

AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning EMNLP 2024 AdjointDEIS: Efficient Gradients for Diffusion Models NeurIPS 2024 ADOPT: Modified Adam Can Converge with Any β_2 with the Optimal Rate NeurIPS 2024 Advancing Generalized Transfer Attack with Initialization Derived Bilevel Optimization and Dynamic Sequence Truncation IJCAI 2024 A Generic and Flexible Regularization Framework for NeRFs WACV 2024 A Homogenization Approach for Gradient-Dominated Stochastic Optimization UAI 2024 A Huber Loss Minimization Approach to Byzantine Robust Federated Learning AAAI 2024 A Layer-Wise Natural Gradient Optimizer for Training Deep Neural Networks NeurIPS 2024 Alias-Free Mamba Neural Operator NeurIPS 2024 Alpha Invariance: On Inverse Scaling Between Distance and Volume Density in Neural Radiance Fields CVPR 2024 An Accelerated Algorithm for Stochastic Bilevel Optimization under Unbounded Smoothness NeurIPS 2024 Analyzing and Improving the Training Dynamics of Diffusion Models CVPR 2024 An Attentive Inductive Bias for Sequential Recommendation beyond the Self-Attention AAAI 2024 An operator preconditioning perspective on training in physics-informed machine learning ICLR 2024 Are Conventional SNNs Really Efficient? A Perspective from Network Quantization CVPR 2024 Are More LLM Calls All You Need? Towards the Scaling Properties of Compound AI Systems NeurIPS 2024 A Single-Step, Sharpness-Aware Minimization is All You Need to Achieve Efficient and Accurate Sparse Training NeurIPS 2024 AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising NeurIPS 2024 AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop UAI 2024 AZ-NAS: Assembling Zero-Cost Proxies for Network Architecture Search CVPR 2024 BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models NeurIPS 2024 BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models NeurIPS 2024 Benefits of Non-Linear Scale Parameterizations in Black Box Variational Inference through Smoothness Results and Gradient Variance Bounds AISTATS 2024 BiDM: Pushing the Limit of Quantization for Diffusion Models NeurIPS 2024 BOLD: Boolean Logic Deep Learning NeurIPS 2024