AlpineGate Technologies has developed a novel AI language model that is founded on a generative self-trainable transformer architecture. This advanced architecture allows the model to incorporate live data during its operation, continuously learning and updating its knowledge base. The system leverages ...
一、前言Transformer 中的 全注意力(Full Attention,Full Atten)需计算所有 token 两两之间的相似度,时间复杂度为 \mathcal{O} (n^2) 。随着大语言模型(Large Language Model,LLM)在各类任务中的应用不断扩…
Herein are techniques for configuring, integrating, and operating trainable tensor transformers that each encapsulate an ensemble of trainable machine learning (ML) models. In an embodiment, a computer-implemented trainable tensor transformer uses underlying ML models and additional mechanisms to assemble ...
动态调整Transformer每层参数量 | Dynamic Layer Tying for Parameter-Efficient Transformers In the pursuit of reducing the number of trainable parameters in deep transformer networks, we employ Reinforcement Learning to dynamically select layers during training and tie them together. Every few iterations, th...
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A comparative study on transformer vs RNN in speech applications (2019) A. Tjandra, C. Liu, F. Zhang, X. Zhang, Y. Wang, G. Synnaeve, S. Nakamura, G. Zweig, DEJA-VU: Double Feature... CheC.et al. Constrained transformer network for ECG signal processing and arrhythmia classification...
在具有 270 亿参数的 Transformer 主干网络上使用 2600 亿个标记进行预训练后,我们评估了 NSA 在通用语言评估、长上下文评估和链式思维推理评估中的表现。我们还将其在 A100 GPU 上的内核速度与优化后的 Triton(Tillet 等人,2019 年)实现进行了比较。实验结果表明,NSA 达到了与全注意力基线相当或更优的性能,同时...
This repository contains a demonstrative implementation for pooling-based models, e.g., DeepPyramidion complementing our paper "Sparsifying Transformer Models with Trainable Representation Pooling." - GitHub - applicaai/pyramidions: This repository conta
Transformer26.9627.2122.3121.92 srcAm Vormittag wollte auch die Arbeitsgruppe Migration und Integration ihre Beratungen fortsetzen . refDuring the morning , the Migration and Integration working group also sought to continue its discussions . greedyThe morning also wanted to continue its discussions on mi...
YOLOv7 outperforms: YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, DETR, Deformable DETR, DINO-5scale-R50, ViT-Adapter-B and many other object detectors in speed and accuracy.