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MAML-Pytorch is a PyTorch implementation of Model-Agnostic Meta-Learning for supervised learning experiments. It focuses on reproducing and exploring the MAML approach for few-shot learning research. The repository supports MiniImagenet and Omniglot, two common benchmark datasets for meta-learning experiments. It includes separate training scripts, dataset loaders, learner components, and meta-learning logic. The project also notes that MAML can be difficult to train and presents the...

MUSE is a framework for learning multilingual word embeddings that live in a shared space, enabling bilingual lexicon induction, cross-lingual retrieval, and zero-shot transfer. It supports both supervised alignment with seed dictionaries and unsupervised alignment that starts without parallel data by using adversarial initialization followed by Procrustes refinement. The code can align pre-trained monolingual embeddings (such as fastText) across dozens of languages and provides standardized...

GPT-OSS-20B is OpenAI’s smaller, open-weight language model optimized for low-latency, agentic tasks, and local deployment. With 21B total parameters and 3.6B active parameters (MoE), it fits within 16GB of memory thanks to native MXFP4 quantization. Designed for high-performance reasoning, it supports Harmony response format, function calling, web browsing, and code execution. Like its larger sibling (gpt-oss-120b), it offers adjustable reasoning depth and full chain-of-thought visibility...