--- id: "/non-llm-neural-networks-ml-foundations/page/4/" title: "Non-LLM neural networks & ML foundations · page 4" url: "https://skillissue.dev/non-llm-neural-networks-ml-foundations/page/4/" --- # Non-LLM neural networks & ML foundations Non-LLM neural networks & ML foundations - [classical ML & statistics](/non-llm-neural-networks-ml-foundations/classical-ml-statistics/index.md) (8) - [generative theory](/non-llm-neural-networks-ml-foundations/generative-theory/index.md) (1) - [graph NNs](/non-llm-neural-networks-ml-foundations/graph-nns/index.md) (8) - [recommenders](/non-llm-neural-networks-ml-foundations/recommenders/index.md) (10) - [reinforcement learning](/non-llm-neural-networks-ml-foundations/reinforcement-learning/index.md) (41) - [sequence models](/non-llm-neural-networks-ml-foundations/sequence-models/index.md) (16) - [time series & tabular](/non-llm-neural-networks-ml-foundations/time-series-tabular/index.md) (10) - [vision](/non-llm-neural-networks-ml-foundations/vision/index.md) (44) [Beginner](/non-llm-neural-networks-ml-foundations/beginner/index.md) [Intermediate](/non-llm-neural-networks-ml-foundations/intermediate/index.md) [Advanced](/non-llm-neural-networks-ml-foundations/advanced/index.md) [Expert](/non-llm-neural-networks-ml-foundations/expert/index.md) 138 resources · page 4 of 5 - ### [Deep Learning Recommendation Model for Personalization and Recommendation Systems (DLRM)](/foundations/llm-fundamentals/dlrm-2/index.md) by [Maxim Naumov et al. (Meta)](https://ai.meta.com/) arXiv PDF of Meta's DLRM paper (2019), a deep learning recommendation model for personalisation. Advanced–Expert learning_resource paper US - ### [DQN (Atari)](/foundations/llm-fundamentals/dqn-atari/index.md) by [Volodymyr Mnih et al. (Google DeepMind)](https://deepmind.google/) arXiv paper (2013) "Playing Atari with Deep Reinforcement Learning", introducing DQN, deep Q-learning from pixels on Atari games. Advanced–Expert learning_resource paper West en - ### [DreamerV3](/foundations/llm-fundamentals/dreamerv3/index.md) by [Danijar Hafner et al. (Google DeepMind)](https://deepmind.google/) arXiv paper (2023) introducing DreamerV3, a world-model RL agent that masters diverse domains with fixed settings. Advanced–Expert learning_resource paper West en - ### [EfficientNet](/foundations/llm-fundamentals/efficientnet/index.md) by [Mingxing Tan and Quoc V. Le (Google Brain)](https://research.google/) ICML 2019 paper (PDF) EfficientNet: scaling convolutional networks by depth, width and resolution. Advanced–Expert learning_resource paper US - ### [ESPnet](/foundations/llm-fundamentals/espnet/index.md) by [ESPnet](https://espnet.github.io/espnet/) ESPnet documentation: end-to-end speech processing toolkit with recipes for recognition, synthesis and translation. Advanced learning_resource docs Japan/global en - ### [Faster R-CNN](/foundations/llm-fundamentals/faster-r-cnn/index.md) by [Shaoqing Ren et al. (Microsoft Research)](https://www.microsoft.com/en-us/research/) NeurIPS paper (PDF, 2015) Faster R-CNN: real-time object detection with region proposal networks. Advanced–Expert learning_resource paper CN zh - ### [GAT](/foundations/llm-fundamentals/gat/index.md) by [Petar Veličković et al. (University of Cambridge)](https://www.cam.ac.uk) arXiv paper (2017) "Graph Attention Networks", introducing GAT, which weights neighbour nodes with masked self-attention. Advanced–Expert learning_resource paper West en - ### [GCN](/foundations/llm-fundamentals/gcn/index.md) by [Thomas N. Kipf and Max Welling (University of Amsterdam)](https://www.uva.nl) arXiv paper (2016) "Semi-Supervised Classification with Graph Convolutional Networks", introducing GCN layers for node classification. Advanced–Expert learning_resource paper West en - ### [GraphSAGE](/foundations/llm-fundamentals/graphsage/index.md) by [William L. Hamilton et al. (Stanford University)](https://www.stanford.edu) arXiv paper (2017) "Inductive Representation Learning on Large Graphs", introducing GraphSAGE for inductive node embeddings. Advanced–Expert learning_resource paper West en - ### [GRU](/foundations/llm-fundamentals/gru/index.md) by [Kyunghyun Cho et al. (Université de Montréal)](https://www.umontreal.ca) arXiv paper (2014) introducing the RNN encoder–decoder with gated recurrent units (GRU) for machine translation. Advanced–Expert learning_resource paper EU-DE en - ### [GRU empirical evaluation](/foundations/llm-fundamentals/gru-empirical-evaluation/index.md) by [Junyoung Chung et al. (Université de Montréal)](https://www.umontreal.ca/) arXiv paper (2014) empirically comparing gated recurrent units (GRU) and LSTM on sequence modelling. Advanced–Expert learning_resource paper West en - ### [IMPALA](/foundations/llm-fundamentals/impala/index.md) by [Lasse Espeholt et al. (Google)](https://ai.google/) arXiv paper (2018) introducing IMPALA, a scalable distributed deep RL architecture. Advanced–Expert learning_resource paper UK en - ### [Inception / GoogLeNet](/foundations/llm-fundamentals/inception-googlenet/index.md) by [Christian Szegedy et al. (Google)](https://ai.google/) arXiv PDF of the GoogLeNet paper (2014) introducing the Inception architecture. Advanced–Expert learning_resource paper US - ### [Jumanji](/foundations/llm-fundamentals/jumanji/index.md) by [InstaDeep Ltd](https://instadeep.com) Jumanji documentation: InstaDeep's suite of JAX reinforcement-learning environments for combinatorial and decision problems. Advanced learning_resource docs UK/Tunisia en - ### [Mask R-CNN](/foundations/llm-fundamentals/mask-r-cnn/index.md) by [Kaiming He et al. (Facebook AI Research)](https://ai.meta.com/) arXiv paper (2017) "Mask R-CNN", introducing Mask R-CNN for instance segmentation. Advanced–Expert learning_resource paper West en - ### [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](/foundations/llm-fundamentals/mobilenet/index.md) by [Andrew G. Howard et al. (Google)](https://ai.google/) arXiv PDF of the MobileNets paper (2017) on efficient convolutional networks for mobile vision. Advanced–Expert learning_resource paper US - ### [Moirai](/foundations/llm-fundamentals/moirai/index.md) by [Gerald Woo et al. (Salesforce)](https://www.salesforce.com/) arXiv PDF of the Moirai paper (2024), Salesforce's universal time-series forecasting transformer. Advanced–Expert learning_resource paper Global (Salesforce AI Research - ### [Montreal Forced Aligner](/foundations/llm-fundamentals/montreal-forced-aligner/index.md) by [Montreal Forced Aligner project (McGill University)](https://montreal-forced-aligner.readthedocs.io/) Montreal Forced Aligner documentation: tool for aligning speech audio with transcripts and training pronunciation models. Advanced learning_resource docs Canada/global en - ### [MuZero](/foundations/llm-fundamentals/muzero/index.md) by [Julian Schrittwieser et al. (Google DeepMind)](https://deepmind.google/) arXiv paper (2019) introducing MuZero, planning with a learned model to master Atari, Go, chess and shogi. Advanced–Expert learning_resource paper West en - ### [Neural Collaborative Filtering](/foundations/llm-fundamentals/neural-collaborative-filtering/index.md) by [Xiangnan He et al. (National University of Singapore)](https://www.nus.edu.sg/) arXiv PDF of the Neural Collaborative Filtering paper (2017) for neural recommender systems. Advanced–Expert learning_resource paper CN zh - ### [OpenAI Five (Dota 2)](/foundations/llm-fundamentals/openai-five-dota-2/index.md) by [OpenAI](https://openai.com/) arXiv paper (2019) "Dota 2 with Large Scale Deep Reinforcement Learning", describing OpenAI Five's large-scale reinforcement learning for Dota 2. Advanced–Expert learning_resource paper West en - ### [PPO](/foundations/llm-fundamentals/ppo/index.md) by [John Schulman et al. (OpenAI)](https://openai.com/) arXiv paper (2017) "Proximal Policy Optimization Algorithms", introducing PPO, a policy gradient method with a clipped surrogate objective. Advanced–Expert learning_resource paper West en - ### [Relational inductive biases, deep learning, and graph networks](/foundations/llm-fundamentals/relational-inductive-biases-graph-networks/index.md) by [Peter W. Battaglia et al. (DeepMind)](https://deepmind.google/) arXiv paper (2018) "Relational inductive biases, deep learning, and graph networks", defining the graph network block that unifies many GNN variants. Advanced–Expert learning_resource paper West en - ### [ResNet](/foundations/llm-fundamentals/resnet/index.md) by [Kaiming He et al. (Microsoft)](https://www.microsoft.com/) arXiv paper (2015) "Deep Residual Learning for Image Recognition", introducing ResNet residual networks. Advanced–Expert learning_resource paper West en - ### [RL4CO](/foundations/llm-fundamentals/rl4co/index.md) by [AI4CO community (RL4CO)](https://ai4co.org/) RL4CO documentation: reinforcement-learning benchmark and library for combinatorial optimisation problems. Advanced learning_resource docs Global en - ### [RWKV](/foundations/llm-fundamentals/rwkv/index.md) by [Bo Peng et al. (University of British Columbia)](https://www.ubc.ca/) arXiv paper (2023) introducing RWKV, a recurrent architecture trained in parallel like a transformer. Advanced–Expert learning_resource paper West en - ### [Efficiently Modeling Long Sequences with Structured State Spaces (S4)](/foundations/llm-fundamentals/s4-structured-state-spaces/index.md) by [Albert Gu et al. (Stanford University)](https://www.stanford.edu) arXiv paper (2021) "Efficiently Modeling Long Sequences with Structured State Spaces", introducing S4, structured state space models for long sequences. Advanced–Expert learning_resource paper West en - ### [SAC](/foundations/llm-fundamentals/sac/index.md) by [Tuomas Haarnoja et al. (UC Berkeley)](https://www.berkeley.edu/) arXiv paper (2018) introducing Soft Actor-Critic (SAC), off-policy maximum-entropy deep reinforcement learning. Advanced–Expert learning_resource paper US en - ### [SAM](/foundations/llm-fundamentals/sam/index.md) by [Alexander Kirillov et al. (Meta AI)](https://ai.meta.com/) arXiv paper (2023) introducing Segment Anything (SAM), a promptable image segmentation model and dataset. Advanced–Expert learning_resource paper US en - ### [A decoder-only foundation model for time-series forecasting (TimesFM)](/foundations/llm-fundamentals/timesfm-2/index.md) by [Abhimanyu Das et al. (Google)](https://ai.google/) arXiv paper (2023) introducing TimesFM, a decoder-only foundation model for time-series forecasting. Advanced–Expert learning_resource paper US en - ### [tslearn](/foundations/llm-fundamentals/tslearn/index.md) by [tslearn project](https://github.com/tslearn-team/tslearn) tslearn documentation: Python machine learning tools for time-series analysis. Advanced learning_resource docs France/global en - ### [U-Net](/foundations/llm-fundamentals/u-net/index.md) by [Olaf Ronneberger et al. (University of Freiburg)](https://www.uni-freiburg.de) arXiv paper (2015) "U-Net: Convolutional Networks for Biomedical Image Segmentation", introducing the encoder-decoder architecture with skip connections. Advanced–Expert learning_resource paper West en [Previous page](/non-llm-neural-networks-ml-foundations/page/3/index.md) [Next page](/non-llm-neural-networks-ml-foundations/page/5/index.md) [Plain-text Markdown copy](/non-llm-neural-networks-ml-foundations/page/4/index.md)