2 个仓库
Algorithms that track multiple top candidates to find the most probable output sequence.
Distinct from Sequence Learning Models: Focuses specifically on the beam search decoding algorithm rather than general sequence learning architectures.
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AllenNLP is a PyTorch-based research library and deep learning language toolkit designed for developing and training neural network architectures for linguistic tasks. It provides a distributed training system that coordinates data and gradients across multiple GPUs and a framework for integrating pretrained transformer architectures. The system distinguishes itself with a dedicated algorithmic bias mitigation tool used to identify and reduce bias in linguistic model predictions. It also includes model influence analysis to interpret predictions by calculating the influence of specific traini
Implements beam search algorithms with n-gram blocking to produce high-probability text sequences.
This project is a collection of structured study notes and notebooks serving as an educational resource for deep learning and neural network fundamentals. It provides a technical reference for implementing machine learning theory, covering everything from basic network design to the construction of advanced architectures. The material specifically focuses on the implementation of convolutional neural networks for computer vision and sequence models for natural language processing. It includes detailed guidance on building object detection systems, face recognition, and speech transcription mo
Implements beam search to optimize the probability of generated sequences in sequence-to-sequence models.