conftrace_

neural network optimization

1293 papers

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Co-occurring keywords

model compression (3302) neural network (6616) convolutional neural network (4226) gradient descent (1144) stochastic gradient descent (1091) efficient computing (781) deep neural network (1803) large language model (13587) knowledge distillation (3725) representation learning (6206)

Papers

The Impact of Depth on Compositional Generalization in Transformer Language Models NAACL 2024
Algorithmic progress in language models NIPS 2024
Weight-Inherited Distillation for Task-Agnostic BERT Compression NAACL 2024
1-Lipschitz Layers Compared: Memory Speed and Certifiable Robustness CVPR 2024
Tuning Stable Rank Shrinkage: Aiming at the Overlooked Structural Risk in Fine-tuning CVPR 2024
Infinite Limits of Multi-head Transformer Dynamics NIPS 2024
Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification COLING 2024
Jump to Conclusions: Short-Cutting Transformers with Linear Transformations COLING 2024
A Single Linear Layer Yields Task-Adapted Low-Rank Matrices COLING 2024
Reducing the Side-Effects of Oscillations in Training of Quantized YOLO Networks WACV 2024
Scalable Optimization in the Modular Norm NIPS 2024
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference EACL 2024
Generalizable and Stable Finetuning of Pretrained Language Models on Low-Resource Texts NAACL 2024
A Structure-Aware Framework for Learning Device Placements on Computation Graphs NIPS 2024
Sketching for Distributed Deep Learning: A Sharper Analysis NIPS 2024
Explicit Eigenvalue Regularization Improves Sharpness-Aware Minimization NIPS 2024
Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning ACL 2024
SDGMNet: Statistic-Based Dynamic Gradient Modulation for Local Descriptor Learning AAAI 2024
A Layer-Wise Natural Gradient Optimizer for Training Deep Neural Networks NIPS 2024
Investigation into Training Dynamics of Learned Optimizers (Student Abstract) AAAI 2024
A provable control of sensitivity of neural networks through a direct parameterization of the overall bi-Lipschitzness NIPS 2024
PTQ4DiT: Post-training Quantization for Diffusion Transformers NIPS 2024
The Price of Implicit Bias in Adversarially Robust Generalization NIPS 2024
Unraveling the Gradient Descent Dynamics of Transformers NIPS 2024
Dual-frame Fluid Motion Estimation with Test-time Optimization and Zero-divergence Loss NIPS 2024