#deep-learning

#deep-learning

Whole slide images (WSIs) can be too large to be processed directly by Deep Learning models due to their sizes, often exceeding 6 GB of memory.

Traditional keyword matching in information retrieval fails to understand user intent, which leads to irrelevant results and limits the diversity of responses, requiring query alterations to be effective.

At the TechCrunch Sessions: AI event, experts emphasized that while the rapid evolution of AI models poses challenges for startups, it simultaneously opens unprecedented opportunities for founders.

Recent advancements suggest that employing small Graph Neural Networks to craft preconditioners could significantly enhance performance while preserving necessary sparsity, thereby optimizing computational efficacy.

Glean is an AI-powered enterprise search and knowledge management platform that helps employees quickly find the information they need, no matter where it's stored.

Maverick is "the workhorse" excelling in image and text understanding for general assistant and chat use cases, while Scout focuses on multi-document summarization and personalized tasks.

We present an effective approach to developing a speech-to-text DE from a text-only LLM, suggesting that it can outperform previous methods with less training data.

Incorporating state space models (SSMs) into deep neural networks provides an innovative approach to model selection that enhances the capacity, efficiency, and overall performance of neural architectures.

Transformers are the leading NLP models due to their self-attention mechanism, which improves computational efficiency, scalability, and performance on various linguistic tasks.