{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-11-877-advanced-topics-in-multimodal-machine-learning-fall-2022-carnegie-mellon-university","slug":"11-877-advanced-topics-in-multimodal-machine-learning-fall-2022-carnegie-mellon-university","name":"11-877: Advanced Topics in MultiModal Machine Learning (Fall 2022) - Carnegie Mellon University","type":"product","url":"https://cmu-multicomp-lab.github.io/adv-mmml-course/spring2022/","page_url":"https://unfragile.ai/11-877-advanced-topics-in-multimodal-machine-learning-fall-2022-carnegie-mellon-university","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-11-877-advanced-topics-in-multimodal-machine-learning-fall-2022-carnegie-mellon-university__cap_0","uri":"capability://planning.reasoning.multimodal.fusion.architecture.instruction","name":"multimodal-fusion-architecture-instruction","description":"Teaches architectural patterns for combining visual, audio, and textual modalities through cross-modal attention mechanisms, transformer-based fusion layers, and late/early/hybrid fusion strategies. 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Covers embedding-based retrieval where both modalities are projected into a shared space, ranking objectives like triplet loss and contrastive losses, and efficient indexing strategies (approximate nearest neighbor search) for scaling to millions of candidates while maintaining sub-second query latency.","intents":["Build image search systems that accept text queries and rank images by semantic relevance","Implement text-to-video retrieval for finding relevant video clips from natural language descriptions","Create cross-modal recommendation systems that suggest items across modalities","Design efficient retrieval pipelines that scale to large candidate sets without exhaustive similarity computation"],"best_for":["Search engineers building multimodal search products","ML teams implementing recommendation systems","Researchers optimizing retrieval efficiency for production systems"],"limitations":["Assumes availability of paired training data (image-text, video-text pairs) — limited guidance for unpaired or weakly-paired scenarios","Does not cover recent advances in dense retrieval with large language models or retrieval-augmented generation","Practical deployment considerations like index updates, cache invalidation, and online learning are minimally addressed"],"requires":["Python 3.7+","Vector similarity libraries (FAISS, Annoy, Hnswlib)","Understanding of metric learning and ranking objectives","Familiarity with embedding spaces and distance metrics"],"input_types":["query embeddings (text, image, or other modality)","candidate embeddings (images, videos, text)","paired training data for metric learning"],"output_types":["ranked list of candidates with similarity scores","top-k retrieval results","embedding vectors for indexing"],"categories":["search-retrieval","academic-curriculum"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-11-877-advanced-topics-in-multimodal-machine-learning-fall-2022-carnegie-mellon-university__cap_5","uri":"capability://planning.reasoning.multimodal.representation.learning.instruction","name":"multimodal-representation-learning-instruction","description":"Teaches principles of learning joint representations where different modalities are mapped into a shared embedding space that captures semantic relationships. 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