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Found 4 papers, 1 papers with code
Towards MoE Deployment: Mitigating Inefficiencies in Mixture-of-Expert (MoE) Inference
We propose three optimization techniques to mitigate sources of inefficiencies, namely (1) Dynamic gating, (2) Expert Buffering, and (3) Expert load balancing.
SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation
Experimental results provide evidence that our method can discover multiple concepts within a single image and outperforms state-of-the-art unsupervised methods on complex datasets such as Cityscapes and COCO-Stuff.
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting.
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
In this work, our main goal is to understand what aspects of DR methods are important for preserving both local and global structure: it is difficult to design a better method without a true understanding of the choices we make in our algorithms and their empirical impact on the lower-dimensional embeddings they produce.
