Non-LLM neural networks & ML foundations for advanced learners
106 resources · page 4 of 4
A decoder-only foundation model for time-series forecasting (TimesFM)
by Abhimanyu Das et al. (Google)
arXiv paper (2023) introducing TimesFM, a decoder-only foundation model for time-series forecasting.
tslearn
tslearn documentation: Python machine learning tools for time-series analysis.
U-Net
by Olaf Ronneberger et al. (University of Freiburg)
arXiv paper (2015) "U-Net: Convolutional Networks for Biomedical Image Segmentation", introducing the encoder-decoder architecture with skip connections.
verifiers v1
Prime Intellect post on verifiers v1, splitting environments into tasksets, harnesses and runtimes for agentic RL and evals.
VGG
by Karen Simonyan and Andrew Zisserman (University of Oxford)
arXiv PDF of the VGG paper (2014) on very deep convolutional networks for large-scale image recognition.
ViT
by Alexey Dosovitskiy et al. (Google)
arXiv paper (2020) introducing the Vision Transformer (ViT) for image recognition.
WaveNet
by Aaron van den Oord et al. (DeepMind)
arXiv paper (2016) "WaveNet: A Generative Model for Raw Audio", introducing WaveNet, an autoregressive generative model of raw audio.
Wide & Deep
by Heng-Tze Cheng et al. (Google)
arXiv paper (2016) "Wide & Deep Learning for Recommender Systems", introducing Google's Wide & Deep recommender model.
xLSTM (NXAI, Linz)
by Maximilian Beck et al. (NXAI)
arXiv paper (2024) introducing xLSTM, extended long short-term memory with exponential gating and matrix memory.
YOLO
by Joseph Redmon et al. (University of Washington)
arXiv paper (2015) "You Only Look Once: Unified, Real-Time Object Detection", introducing YOLO, a single-stage real-time object detector.