#few-shot learning

FSL-Mate provides a comprehensive set of resources for enhancing the research of few-shot learning (FSL). It includes FewShotPapers, a detailed list of cutting-edge research papers, and PaddleFSL, a Python library based on PaddlePaddle tailored for FSL. The platform is regularly updated with publications from key conferences such as ICLR, AAAI, EMNLP, ICCV, and NeurIPS, ensuring access to the latest developments. FSL-Mate is an essential tool for academic and practical advancements in FSL, offering insights and resources for both research and AI innovation without bias or exaggerated claims.

Discover Audio Flamingo, an advanced audio language model with few-shot learning and dialogue abilities. Utilizing innovative training and architecture, it excels in audio tasks and adapts swiftly to new challenges. With PyTorch implementation establishing new benchmarks, its modular code supports non-commercial use. Learn about its state-of-the-art performance and applications in audio processing.

EasyNLP offers a user-friendly integration of NLP development within PyTorch, emphasizing scalable distributed training. It includes support for leading NLP algorithms and multi-modality models for varied applications. Few-shot learning and knowledge distillation enable effective deployment of large models. This toolkit is integrated within Alibaba's AI platforms, featuring open-source compatibility and unique elements like knowledge-injected pre-training, making it essential for high-performance NLP projects.

Explore 1.84M CoT rationales across 1,060 tasks for zero-shot and few-shot learning improvements. Access datasets and CoT-T5 models via Huggingface, and utilize rationale augmentation for enhanced training. Available under OpenAI's non-commercial terms. Reference the research paper for citations.

UniAudio 1.5 presents a pioneering LLM-driven audio codec model that converts audio data into textual tokens, allowing LLMs to perform audio tasks like emotion classification and TTS generation without fine-tuning. This open-source model compresses audio as a new language into LLMs, supporting few-shot learning with high efficacy and minimal examples.