The Vision-Language Alignment Problem
How vision-language models align visual and text representations using contrastive learning, cross-modal attention, and CLIP-style training.
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ML Concepts - Page 22
Clear explanations of core machine learning concepts, from foundational ideas to advanced techniques. Understand attention mechanisms, transformers, skip connections, and more.
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Concepts - Page 22
The Modality Gap
The modality gap in CLIP and vision-language models: why image and text embeddings occupy separate regions despite contrastive training.
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Multimodal Scaling Laws
Discover how multimodal vision-language models like CLIP, ALIGN, and LLaVA scale with data, parameters, and compute following Chinchilla-style power laws.
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Vision-Language Adapters: Parameter-Efficient Multimodal Fine-tuning
Master LoRA, bottleneck adapters, and prefix tuning for parameter-efficient fine-tuning of vision-language models like LLaVA with minimal compute and memory.
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Client-Server Communication: Polling vs WebSockets
Learn client-server communication patterns including short polling, long polling, and WebSockets. Compare HTTP protocols for real-time web applications.
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C++ AST & Parsing
Explore how C++ code is parsed into an Abstract Syntax Tree (AST). Learn lexical analysis, tokenization, and syntax parsing for systems programming.
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Showing 127-132 of 174 articles