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Found 7 papers, 5 papers with code
Data-Juicer: A One-Stop Data Processing System for Large Language Models
A data recipe is a mixture of data from different sources for training LLMs, which plays a vital role in LLMs' performance.
Entropy-Driven Mixed-Precision Quantization for Deep Network Design
Deploying deep convolutional neural networks on Internet-of-Things (IoT) devices is challenging due to the limited computational resources, such as limited SRAM memory and Flash storage.
Hierarchical Cross Contrastive Learning of Visual Representations
In this work, we propose a novel approach termed Hierarchical Cross Contrastive Learning(HCCL) to further distill the information mismatched by the conventional contrastive loss.
Fine-Grained AutoAugmentation for Multi-Label Classification
Data augmentation is a commonly used approach to improving the generalization of deep learning models.
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition
Comparing with previous NAS methods, the proposed Zen-NAS is magnitude times faster on multiple server-side and mobile-side GPU platforms with state-of-the-art accuracy on ImageNet.
Ranked #2 on
Neural Architecture Search
on ImageNet
Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition
To address this issue, instead of using an accuracy predictor, we propose a novel zero-shot index dubbed Zen-Score to rank the architectures.
Neural Architecture Search
Vocal Bursts Intensity Prediction
Neural Architecture Design for GPU-Efficient Networks
To address this issue, we propose a general principle for designing GPU-efficient networks based on extensive empirical studies.
