AutoML: A survey of the state-of-the-art

Virtual homogeneity learning: Defending against data heterogeneity in federated learning

Benchmarking the performance and energy efficiency of AI accelerators for AI training

BurstGPT: A Real-world Workload Dataset to Optimize LLM Serving Systems

Benchmarking deep learning models and automated model design for COVID-19 detection with chest CT scans

Automated model design and benchmarking of deep learning models for covid-19 detection with chest ct scans

Fusionai: Decentralized training and deploying llms with massive consumer-level gpus

EAGAN: Efficient two-stage evolutionary architecture search for GANs

ExpertFlow: Efficient Mixture-of-Experts Inference via Predictive Expert Caching and Token Scheduling

Evolutionary multi-objective architecture search framework: Application to covid-19 3d ct classification

Computer-aided clinical skin disease diagnosis using CNN and object detection models

Nas-lid: Efficient neural architecture search with local intrinsic dimension

Fault-Tolerant Hybrid-Parallel Training at Scale with Reliable and Efficient In-memory Checkpointing

FusionLLM: a decentralized LLM training system on geo-distributed GPUs with adaptive compression

MedPipe: End-to-End Joint Search of Data Augmentation Policy and Neural Architecture for 3D Medical Image Classification

Lang-PINN: From Language to Physics-Informed Neural Networks via a Multi-Agent Framework

Ghost in the cloud: Your geo-distributed large language models training is easily manipulated

Autohete: An automatic and efficient heterogeneous training system for llms

Routemark: A fingerprint for intellectual property attribution in routing-based model merging

GM-Skip: Metric-Guided Transformer Block Skipping for Efficient Vision-Language Models