Llm Fundamentals: 139 curated resources
Curated material on llm fundamentals within foundations.
139 resources · page 4 of 5
DINOv2: Learning Robust Visual Features without Supervision
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arXiv HTML version of the DINOv2 paper (2023) on self-supervised visual features.
Deep Learning Recommendation Model for Personalization and Recommendation Systems (DLRM)
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arXiv PDF of Meta's DLRM paper (2019), a deep learning recommendation model for personalisation.
DQN (Atari)
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arXiv paper (2013) "Playing Atari with Deep Reinforcement Learning", introducing DQN, deep Q-learning from pixels on Atari games.
DreamerV3
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arXiv paper (2023) introducing DreamerV3, a world-model RL agent that masters diverse domains with fixed settings.
EfficientNet
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ICML 2019 paper (PDF) EfficientNet: scaling convolutional networks by depth, width and resolution.
ESPnet
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ESPnet documentation: end-to-end speech processing toolkit with recipes for recognition, synthesis and translation.
Faster R-CNN
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NeurIPS paper (PDF, 2015) Faster R-CNN: real-time object detection with region proposal networks.
GAT
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arXiv paper (2017) "Graph Attention Networks", introducing GAT, which weights neighbour nodes with masked self-attention.
GCN
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arXiv paper (2016) "Semi-Supervised Classification with Graph Convolutional Networks", introducing GCN layers for node classification.
GraphSAGE
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arXiv paper (2017) "Inductive Representation Learning on Large Graphs", introducing GraphSAGE for inductive node embeddings.
GRU
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arXiv paper (2014) introducing the RNN encoder–decoder with gated recurrent units (GRU) for machine translation.
GRU empirical evaluation
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arXiv paper (2014) empirically comparing gated recurrent units (GRU) and LSTM on sequence modelling.
IMPALA
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arXiv paper (2018) introducing IMPALA, a scalable distributed deep RL architecture.
Inception / GoogLeNet
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arXiv PDF of the GoogLeNet paper (2014) introducing the Inception architecture.
Jumanji
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Jumanji documentation: InstaDeep's suite of JAX reinforcement-learning environments for combinatorial and decision problems.
Mask R-CNN
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arXiv paper (2017) "Mask R-CNN", introducing Mask R-CNN for instance segmentation.
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
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arXiv PDF of the MobileNets paper (2017) on efficient convolutional networks for mobile vision.
Moirai
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arXiv PDF of the Moirai paper (2024), Salesforce's universal time-series forecasting transformer.
Montreal Forced Aligner
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Montreal Forced Aligner documentation: tool for aligning speech audio with transcripts and training pronunciation models.
MuZero
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arXiv paper (2019) introducing MuZero, planning with a learned model to master Atari, Go, chess and shogi.
Neural Collaborative Filtering
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arXiv PDF of the Neural Collaborative Filtering paper (2017) for neural recommender systems.
OpenAI Five (Dota 2)
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arXiv paper (2019) "Dota 2 with Large Scale Deep Reinforcement Learning", describing OpenAI Five's large-scale reinforcement learning for Dota 2.
PPO
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arXiv paper (2017) "Proximal Policy Optimization Algorithms", introducing PPO, a policy gradient method with a clipped surrogate objective.
Relational inductive biases, deep learning, and graph networks
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arXiv paper (2018) "Relational inductive biases, deep learning, and graph networks", defining the graph network block that unifies many GNN variants.
ResNet
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arXiv paper (2015) "Deep Residual Learning for Image Recognition", introducing ResNet residual networks.
RL4CO
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RL4CO documentation: reinforcement-learning benchmark and library for combinatorial optimisation problems.
RWKV
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arXiv paper (2023) introducing RWKV, a recurrent architecture trained in parallel like a transformer.
Efficiently Modeling Long Sequences with Structured State Spaces (S4)
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arXiv paper (2021) "Efficiently Modeling Long Sequences with Structured State Spaces", introducing S4, structured state space models for long sequences.
SAC
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arXiv paper (2018) introducing Soft Actor-Critic (SAC), off-policy maximum-entropy deep reinforcement learning.
SAM
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arXiv paper (2023) introducing Segment Anything (SAM), a promptable image segmentation model and dataset.
A decoder-only foundation model for time-series forecasting (TimesFM)
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arXiv paper (2023) introducing TimesFM, a decoder-only foundation model for time-series forecasting.
tslearn
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tslearn documentation: Python machine learning tools for time-series analysis.