Search Results for author:
Found 14 papers, 5 papers with code
VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting
Combining CNNs or ViTs, with RNNs for spatiotemporal forecasting, has yielded unparalleled results in predicting temporal and spatial dynamics.
ParZC: Parametric Zero-Cost Proxies for Efficient NAS
Recent advancements in Zero-shot Neural Architecture Search (NAS) highlight the efficacy of zero-cost proxies in various NAS benchmarks.
Auto-Prox: Training-Free Vision Transformer Architecture Search via Automatic Proxy Discovery
Based on the discovered zero-cost proxy, we conduct a ViT architecture search in a training-free manner.
TVT: Training-Free Vision Transformer Search on Tiny Datasets
In this paper, for the first time, we investigate how to search in a training-free manner with the help of teacher models and devise an effective Training-free ViT (TVT) search framework.
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
For end users, our benchmark and findings help better understand different optimization techniques, training and inference frameworks, together with hardware platforms in choosing configurations for deploying LLMs.
EMQ: Evolving Training-free Proxies for Automated Mixed Precision Quantization
In particular, we devise an elaborate search space involving the existing proxies and perform an evolution search to discover the best correlated MQ proxy.
DisWOT: Student Architecture Search for Distillation WithOut Training
In this way, our student architecture search for Distillation WithOut Training (DisWOT) significantly improves the performance of the model in the distillation stage with at least 180$\times$ training acceleration.
Progressive Meta-Pooling Learning for Lightweight Image Classification Model
Practical networks for edge devices adopt shallow depth and small convolutional kernels to save memory and computational cost, which leads to a restricted receptive field.
RD-NAS: Enhancing One-shot Supernet Ranking Ability via Ranking Distillation from Zero-cost Proxies
In this paper, we propose Ranking Distillation one-shot NAS (RD-NAS) to enhance ranking consistency, which utilizes zero-cost proxies as the cheap teacher and adopts the margin ranking loss to distill the ranking knowledge.
Automated Knowledge Distillation via Monte Carlo Tree Search
In this paper, we present Auto-KD, the first automated search framework for optimal knowledge distillation design.
DMFormer: Closing the Gap Between CNN and Vision Transformers
Vision transformers have shown excellent performance in computer vision tasks.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
Prior-Guided One-shot Neural Architecture Search
In this paper, we present Prior-Guided One-shot NAS (PGONAS) to strengthen the ranking correlation of supernets.
Multi-trial Neural Architecture Search with Lottery Tickets
Neural architecture search (NAS) has brought significant progress in recent image recognition tasks.
