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def get_embedding(text): return model.encode(text).tolist() These talks set the intellectual tone, underscoring both
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| Track | Scope | Representative Papers | |-------|-------|------------------------| | | Methods that learn embeddings without explicit labels (e.g., contrastive, generative, predictive). | • MoCo‑v2: Momentum Contrast for Unsupervised Visual Representation • BERT‑2: Self‑Supervised Language Modeling with Multi‑View Objectives | | Zero‑Shot Transfer & Generalization (ZST) | Techniques that enable models to perform novel tasks or recognize unseen classes using only semantic descriptors. | • CLIP‑Style Vision‑Language Pretraining at Scale • Prompt‑Based Zero‑Shot Classification for Textual Entailment | | Few‑Shot Adaptation and Meta‑Learning (FSA) | Algorithms that quickly adapt to new tasks with a handful of examples, often via gradient‑based or metric‑based meta‑learning. | • Meta‑Transformer: Unified Few‑Shot Learning Across Modalities • MAML‑Lite for Low‑Compute Environments | | Responsible and Ethical AI (REA) | Analyses of bias, robustness, privacy, and governance for unsupervised models. | • Auditing Contrastive Representations for Demographic Bias • Differentially Private Self‑Supervision |
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