April 2024 AI, Machine Learning and Data Science Meetup
April 18, 2024 | 10 AM PT, 17:00 UTC
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Talks and Speakers
Towards Resource Efficient Robust Text-to-Image Generative Models
Maitreya Patel
Arizona State University
Text-to-image (T2I) diffusion models (such as Stable Diffusion XL, DALL-E 3, etc.) achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks, at the cost of significant computational resources. For instance, the unCLIP (i.e., DALL-E 2) stack comprises T2I prior and diffusion image decoder. The T2I prior model itself adds a billion parameters, increasing the computational and high-quality data requirements. Maitreya propose the ECLIPSE, a novel contrastive learning method that is both parameter and data-efficient as a way to combat these issues
About the Speaker
Maitreya Patel is a PHD student studying at Arizona State University focusing on model performance and efficiency. Whether it is model training or inference, Maitreya strives to make optimizations to make AI more accessible and powerful.
GraphRAG with a Knowledge Graph
Andreas Kollegger
Neo4j
Knowledge Graphs place information in context using graph structures to express local and global semantics. When used in a RAG context, particular access patterns emerge that map natural language to different graph data patterns. We’ll review both the model and the matching code.
About the Speaker
Andreas Kollegger is a founding member of Neo4j, now responsible for researching the use of Knowledge Graphs for GenAI applications.
Optimizing Training Data with the Voxel51 and V7 Darwin Integration
Mark Cox-Smith
V7
One of the most expensive parts of a machine learning project is obtaining high quality training data. In this talk, Mark will discuss how the integration between Voxel51 and the V7 Darwin platform can help you optimize the subset of data to be labeled with the goals of reducing costs whilst maintaining quality.
About the Speaker
Mark Cox-Smith is a Principal Solutions Architect at V7 where he helps customers to connect their labeling workflows into their MLOps stack.
Exploring Multimodal Models: Llava-Next and TextQA Dataset
Harpreet Sahota
Voxel51
In this session, you’ll get hands-on with the newest LlaVa model, LlaVa-next! You’ll learn how to use fiftyone to visually vibe check the performance of both the Vicuna-7B and Mistral-7B backbones models on the TextQA dataset.
About the Speaker
Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.
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