腾讯网

用 PyTorch 实现 LLM-JEPA：不预测 token，预测嵌入

点击上方“Deephub Imba”,关注公众号,好文章不错过 !这篇文章从头实现 LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures。需要说明的是，这里写的是一个简洁的最小化训练脚本，目标是了解 JEPA 的本质：对同一文本创建两个视图，预测被遮蔽片段的嵌入，用表示对齐损失来训练。本文的目标是 ...
GitHub

Rectified Point Flow: Generic Point Cloud Pose Estimation

Improved model speed (9-12% faster) and training stability. Fixed bugs in configs, RK2 sampler, and validation. Simplified point cloud packing and shaping. Checkpoints are compatible with the previous ...
IEEE

ConvNeXt-ECA: An Effective Encoder Network for Few-Shot Learning

Abstract: After having introduced a comprehensive general solution framework for few-shot learning (FSL) classification problems, we provide details of the data augmentation schemes and the learning ...
IEEE

Evaluation of Encoder-Only Transformer for Multi-Step Traffic Flow Prediction

Abstract: Traffic flow prediction is critical for Intelligent Transportation Systems to alleviate congestion and optimize traffic management. The existing basic Encoder-Decoder Transformer model for ...
GitHub

VideoPrism: A Foundational Visual Encoder for Video Understanding

VideoPrism is a general-purpose video encoder designed to handle a wide spectrum of video understanding tasks, including classification, retrieval, localization, captioning, and question answering. It ...