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Found 7 papers, 3 papers with code
LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection
In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT.
Scaling Sentence Embeddings with Large Language Models
We also fine-tune LLMs with current contrastive learning approach, and the 2. 7B OPT model, incorporating our prompt-based method, surpasses the performance of 4. 8B ST5, achieving the new state-of-the-art results on STS tasks.
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FamilySeer: Towards Optimized Tensor Codes by Exploiting Computation Subgraph Similarity
Currently, the DL compilers partition the input DL models into several subgraphs and leverage the auto-tuning to find the optimal tensor codes of these subgraphs.
The Deep Learning Compiler: A Comprehensive Survey
In this paper, we perform a comprehensive survey of existing DL compilers by dissecting the commonly adopted design in details, with emphasis on the DL oriented multi-level IRs, and frontend/backend optimizations.
Privacy for Rescue: A New Testimony Why Privacy is Vulnerable In Deep Models
Existing research works rely on metrics that are either impractical or insufficient to measure the effectiveness of privacy protection methods in the above scenario, especially from the aspect of a single user.
CompactNet: Platform-Aware Automatic Optimization for Convolutional Neural Networks
Convolutional Neural Network (CNN) based Deep Learning (DL) has achieved great progress in many real-life applications.
swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor
The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability.
