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Found 6 papers, 5 papers with code
ZeRO++: Extremely Efficient Collective Communication for Giant Model Training
Zero Redundancy Optimizer (ZeRO) has been used to train a wide range of large language models on massive GPUs clusters due to its ease of use, efficiency, and good scalability.
DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
Computation in a typical Transformer-based large language model (LLM) can be characterized by batch size, hidden dimension, number of layers, and sequence length.
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization.
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets
Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the process.
Learning Interpretable Models Through Multi-Objective Neural Architecture Search
Monumental advances in deep learning have led to unprecedented achievements across various domains.
Explainable Artificial Intelligence (XAI)
Image Classification
+1
Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency
With the trained autoencoder, we generate latent representations of a small dataset, containing pairs of normal and cancer cells of various tumor types.
