conftrace_

gradient computation

45 papers

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Co-occurring keywords

automatic differentiation (77) memory efficiency (261) neural network optimization (1293) neural network (6616) gradient descent (1144) spiking neural network (331) distributed training (188) convolutional neural network (4226) large language model (13587) model interpretability (519)

Papers

Parallel Training Time-to-First-Spike Spiking Neural Networks AAAI 2026
FlashKAT: Understanding and Addressing Performance Bottlenecks in the Kolmogorov-Arnold Transformer AAAI 2026
Forward Knows Efficient Backward Path: Saliency-Guided Memory-Efficient Fine-tuning of Large Language Models ACL 2025
The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes CVPR 2024
Federated Automatic Differentiation JMLR 2024
Integrated Decision Gradients: Compute Your Attributions Where the Model Makes Its Decision AAAI 2024
SAMPa: Sharpness-aware Minimization Parallelized NIPS 2024
The Fine-Grained Complexity of Gradient Computation for Training Large Language Models NIPS 2024
AdjointDEIS: Efficient Gradients for Diffusion Models NIPS 2024
Towards training digitally-tied analog blocks via hybrid gradient computation NIPS 2024
Memory-Efficient Fine-Tuning of Transformers via Token Selection EMNLP 2024
Gradients of Functions of Large Matrices NIPS 2024
Full Parameter Fine-tuning for Large Language Models with Limited Resources ACL 2024
Hyperblock Floating Point: Generalised Quantization Scheme for Gradient and Inference Computation WACV 2023
DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training CVPR 2023
On quantum backpropagation, information reuse, and cheating measurement collapse NIPS 2023
Improving Gradient Computation for Differentiable Physics Simulation with Contacts L4DC 2023
Enhancing textual counterfactual explanation intelligibility through Counterfactual Feature Importance ACL 2023
Exact Gradient Computation for Spiking Neural Networks via Forward Propagation AISTATS 2023
On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters ICML 2023
Making Scalable Meta Learning Practical NIPS 2023
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations NIPS 2022
A Unified Framework for Implicit Sinkhorn Differentiation CVPR 2022
An In-depth Study of Stochastic Backpropagation NIPS 2022
Stochastic Backpropagation: A Memory Efficient Strategy for Training Video Models CVPR 2022