Non Llm Neural Networks Ml Foundations
138 resources · page 5 of 5
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.
EASA AI Concept Paper Proposed Issue 03
by European Union Aviation Safety Agency (EASA)
EASA AI Concept Paper Proposed Issue 03 (PDF), extending aviation AI assurance guidance to more methods and Level 3 automation.
Reward Hacking in Reinforcement Learning (Lil'Log)
by Lilian
Lilian Weng's survey of reward hacking in reinforcement learning, including in RLHF and coding tasks.
Mamba
by Albert Gu and Tri Dao (Carnegie Mellon University)
arXiv paper (2023) introducing Mamba, linear-time sequence modelling with selective state spaces.