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self-attention

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SAITS

The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516

  • Updated Aug 25, 2026
  • Python

In this work, we shared the source code which are utilized in the study named ODE Transformers for Isolated Sign Language Recognition: A Study on Robustness and Scalability. This code implements the multimodal experiments which reported in the article.

  • Updated Aug 24, 2026
  • Python

SASRec с нуля на PyTorch (causal self-attention вручную) на Yandex Yambda. Аудио-эмбеддинги квантованы в semantic IDs через RQ-VAE: как замена item-эмбеддингов проигрывают, как дополнение дают +39% к NDCG@10 (0.575 vs 0.413). Ablation из шести моделей

  • Updated Aug 17, 2026
  • Jupyter Notebook

GPT-style decoder-only Transformer built from scratch in PyTorch — custom byte-level BPE tokenizer, causal multi-head self-attention, Pre-LN Transformer blocks, and end-to-end training on WikiText-2. 9 experiments reduced test perplexity 120.79 → 73.76, with an 8.6M-parameter model outperforming larger runs.

  • Updated Aug 13, 2026
  • Jupyter Notebook

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