<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>AIpedia</title><description>AI and machine learning concepts explained with interactive visuals</description><link>https://aipedia.org/</link><item><title>Attention Is All You Need</title><link>https://aipedia.org/attention-is-all-you-need/</link><guid isPermaLink="true">https://aipedia.org/attention-is-all-you-need/</guid><description>The 2017 paper that introduced the Transformer architecture</description><pubDate>Thu, 26 Feb 2026 00:00:00 GMT</pubDate><category>paper</category><category>architecture</category><category>attention</category><category>deep-learning</category><category>nlp</category><category>transformer</category></item><item><title>Maximum Likelihood Reinforcement Learning (MaxRL)</title><link>https://aipedia.org/maximum-likelihood-rl/</link><guid isPermaLink="true">https://aipedia.org/maximum-likelihood-rl/</guid><description>A recent idea for training models on pass-fail tasks when sampling matters</description><pubDate>Fri, 13 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>reinforcement-learning</category><category>optimization</category><category>policy-gradient</category></item><item><title>Adam Optimizer</title><link>https://aipedia.org/adam/</link><guid isPermaLink="true">https://aipedia.org/adam/</guid><description>Adaptive learning rates with momentum for deep learning</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>training</category><category>optimization</category></item><item><title>Backpropagation</title><link>https://aipedia.org/backpropagation/</link><guid isPermaLink="true">https://aipedia.org/backpropagation/</guid><description>The algorithm that enables neural networks to learn by computing gradients efficiently</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>fundamentals</category><category>optimization</category><category>neural-networks</category></item><item><title>Batch Normalization</title><link>https://aipedia.org/batch-normalization/</link><guid isPermaLink="true">https://aipedia.org/batch-normalization/</guid><description>Normalizing layer inputs to accelerate deep network training</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>training</category><category>optimization</category><category>architecture</category></item><item><title>BERT: Bidirectional Transformers</title><link>https://aipedia.org/bert/</link><guid isPermaLink="true">https://aipedia.org/bert/</guid><description>Pre-training deep bidirectional representations for NLP</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>architecture</category><category>transformer</category><category>deep-learning</category><category>nlp</category><category>pre-training</category></item><item><title>CLIP: Contrastive Language-Image Pre-training</title><link>https://aipedia.org/clip/</link><guid isPermaLink="true">https://aipedia.org/clip/</guid><description>Learning visual concepts from natural language supervision</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>multimodal</category><category>computer-vision</category><category>nlp</category><category>foundation-models</category></item><item><title>Chain-of-Thought Prompting</title><link>https://aipedia.org/chain-of-thought/</link><guid isPermaLink="true">https://aipedia.org/chain-of-thought/</guid><description>Eliciting step-by-step reasoning in language models for complex problem solving</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>nlp</category><category>language-models</category><category>reasoning</category><category>prompting</category></item><item><title>Diffusion Models</title><link>https://aipedia.org/diffusion-models/</link><guid isPermaLink="true">https://aipedia.org/diffusion-models/</guid><description>Generative models that learn to denoise, enabling high-quality image and video synthesis</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>generative</category><category>computer-vision</category><category>diffusion</category><category>foundation-models</category></item><item><title>Deep Q-Networks (DQN)</title><link>https://aipedia.org/dqn/</link><guid isPermaLink="true">https://aipedia.org/dqn/</guid><description>Combining Q-learning with deep neural networks for Atari-level game playing</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>reinforcement-learning</category><category>games</category><category>classic</category></item><item><title>Dropout: Regularization for Neural Networks</title><link>https://aipedia.org/dropout/</link><guid isPermaLink="true">https://aipedia.org/dropout/</guid><description>Randomly dropping units during training to prevent overfitting</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>training</category><category>regularization</category><category>optimization</category></item><item><title>Generative Adversarial Networks</title><link>https://aipedia.org/gan/</link><guid isPermaLink="true">https://aipedia.org/gan/</guid><description>Two neural networks compete to generate realistic data</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>architecture</category><category>deep-learning</category><category>generative</category><category>computer-vision</category></item><item><title>GPT: Generative Pre-Training</title><link>https://aipedia.org/gpt/</link><guid isPermaLink="true">https://aipedia.org/gpt/</guid><description>Autoregressive language models that learn to predict the next token</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>architecture</category><category>transformer</category><category>deep-learning</category><category>nlp</category><category>pre-training</category><category>generative</category></item><item><title>In-Context Learning</title><link>https://aipedia.org/in-context-learning/</link><guid isPermaLink="true">https://aipedia.org/in-context-learning/</guid><description>How large language models learn from examples in the prompt without weight updates</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>nlp</category><category>language-models</category><category>emergent-abilities</category><category>foundation-models</category></item><item><title>Latent Diffusion Models</title><link>https://aipedia.org/latent-diffusion/</link><guid isPermaLink="true">https://aipedia.org/latent-diffusion/</guid><description>High-resolution image generation by diffusing in learned latent spaces</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>generative</category><category>computer-vision</category><category>diffusion</category></item><item><title>Layer Normalization</title><link>https://aipedia.org/layer-normalization/</link><guid isPermaLink="true">https://aipedia.org/layer-normalization/</guid><description>Normalizing each example across its features</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>fundamentals</category><category>training</category><category>transformers</category></item><item><title>Mamba: State Space Models</title><link>https://aipedia.org/mamba/</link><guid isPermaLink="true">https://aipedia.org/mamba/</guid><description>A sequence model that keeps a running state instead of attending to every token pair</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>architecture</category><category>sequence-modeling</category><category>efficiency</category></item><item><title>Policy Gradient Methods</title><link>https://aipedia.org/policy-gradient/</link><guid isPermaLink="true">https://aipedia.org/policy-gradient/</guid><description>Directly optimizing policies through gradient ascent on expected returns</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>reinforcement-learning</category><category>optimization</category></item><item><title>Proximal Policy Optimization (PPO)</title><link>https://aipedia.org/ppo/</link><guid isPermaLink="true">https://aipedia.org/ppo/</guid><description>A stable, sample-efficient policy gradient algorithm for reinforcement learning</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>reinforcement-learning</category><category>policy-gradient</category><category>optimization</category></item><item><title>Recursive Language Models</title><link>https://aipedia.org/recursive-language-models/</link><guid isPermaLink="true">https://aipedia.org/recursive-language-models/</guid><description>A paradigm where LLMs treat context as an environment and recursively call themselves on sub-problems</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>language-models</category><category>inference</category><category>long-context</category><category>agents</category></item><item><title>Reinforcement Learning</title><link>https://aipedia.org/reinforcement-learning/</link><guid isPermaLink="true">https://aipedia.org/reinforcement-learning/</guid><description>Learning by trial and error through rewards</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>reinforcement-learning</category><category>agents</category><category>decision-making</category></item><item><title>RLHF: Reinforcement Learning from Human Feedback</title><link>https://aipedia.org/rlhf/</link><guid isPermaLink="true">https://aipedia.org/rlhf/</guid><description>Teaching language models to prefer responses that people rank higher</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>nlp</category><category>alignment</category><category>reinforcement-learning</category><category>foundation-models</category></item><item><title>Sequence to Sequence Learning</title><link>https://aipedia.org/seq2seq/</link><guid isPermaLink="true">https://aipedia.org/seq2seq/</guid><description>Encoder-decoder architecture for mapping sequences to sequences</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>architecture</category><category>deep-learning</category><category>nlp</category><category>encoder-decoder</category><category>seq2seq</category><category>rnn</category></item><item><title>Word2Vec: Word Embeddings</title><link>https://aipedia.org/word2vec/</link><guid isPermaLink="true">https://aipedia.org/word2vec/</guid><description>Learning dense vector representations of words from text</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>nlp</category><category>embeddings</category><category>deep-learning</category><category>representation-learning</category></item><item><title>Vision Transformer (ViT)</title><link>https://aipedia.org/vision-transformer/</link><guid isPermaLink="true">https://aipedia.org/vision-transformer/</guid><description>Applying Transformers directly to image patches for visual recognition</description><pubDate>Sun, 01 Feb 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>computer-vision</category><category>transformers</category><category>attention</category></item><item><title>AlexNet</title><link>https://aipedia.org/alexnet/</link><guid isPermaLink="true">https://aipedia.org/alexnet/</guid><description>The deep CNN that won ImageNet 2012 and sparked the deep learning revolution</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>cnn</category><category>computer-vision</category><category>imagenet</category></item><item><title>The Annotated Transformer</title><link>https://aipedia.org/annotated-transformer/</link><guid isPermaLink="true">https://aipedia.org/annotated-transformer/</guid><description>Line-by-line PyTorch implementation of the Transformer architecture</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>transformer</category><category>attention</category><category>nlp</category></item><item><title>Neural Machine Translation by Jointly Learning to Align and Translate</title><link>https://aipedia.org/bahdanau-attention/</link><guid isPermaLink="true">https://aipedia.org/bahdanau-attention/</guid><description>The paper that introduced the attention mechanism for sequence-to-sequence models</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>attention</category><category>nlp</category><category>translation</category></item><item><title>Quantifying the Rise and Fall of Complexity in Closed Systems</title><link>https://aipedia.org/coffee-automaton/</link><guid isPermaLink="true">https://aipedia.org/coffee-automaton/</guid><description>The Coffee Automaton paper formalizing how complexity peaks then declines</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>complexity</category><category>entropy</category><category>automata</category><category>theory</category></item><item><title>The First Law of Complexodynamics</title><link>https://aipedia.org/complexodynamics/</link><guid isPermaLink="true">https://aipedia.org/complexodynamics/</guid><description>Why complexity rises then falls while entropy only increases</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>complexity</category><category>entropy</category><category>theory</category><category>physics</category></item><item><title>CS231n: CNNs for Visual Recognition</title><link>https://aipedia.org/cs231n/</link><guid isPermaLink="true">https://aipedia.org/cs231n/</guid><description>Stanford&apos;s foundational course on deep learning for computer vision</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>cnn</category><category>computer-vision</category><category>course</category></item><item><title>Deep Speech 2: End-to-End Speech Recognition</title><link>https://aipedia.org/deep-speech-2/</link><guid isPermaLink="true">https://aipedia.org/deep-speech-2/</guid><description>Scaling up end-to-end speech recognition with RNNs and CTC</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>speech</category><category>rnn</category><category>ctc</category></item><item><title>Multi-Scale Context Aggregation by Dilated Convolutions</title><link>https://aipedia.org/dilated-convolutions/</link><guid isPermaLink="true">https://aipedia.org/dilated-convolutions/</guid><description>Expanding receptive fields exponentially without losing resolution or adding parameters</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>cnn</category><category>computer-vision</category><category>segmentation</category></item><item><title>GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism</title><link>https://aipedia.org/gpipe/</link><guid isPermaLink="true">https://aipedia.org/gpipe/</guid><description>Training giant neural networks by pipelining micro-batches across devices</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>distributed</category><category>training</category><category>parallelism</category></item><item><title>Kolmogorov Complexity and Algorithmic Randomness</title><link>https://aipedia.org/kolmogorov-complexity/</link><guid isPermaLink="true">https://aipedia.org/kolmogorov-complexity/</guid><description>Measuring how short the best description of an object can be</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>complexity</category><category>information-theory</category><category>computation</category><category>theory</category></item><item><title>Machine Super Intelligence</title><link>https://aipedia.org/machine-superintelligence/</link><guid isPermaLink="true">https://aipedia.org/machine-superintelligence/</guid><description>Shane Legg&apos;s PhD thesis formalizing universal intelligence and the AIXI agent</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>agi</category><category>theory</category><category>intelligence</category><category>aixi</category></item><item><title>A Tutorial Introduction to the Minimum Description Length Principle</title><link>https://aipedia.org/mdl-tutorial/</link><guid isPermaLink="true">https://aipedia.org/mdl-tutorial/</guid><description>Grünwald&apos;s comprehensive guide to MDL for model selection and learning</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>mdl</category><category>information-theory</category><category>model-selection</category><category>theory</category></item><item><title>Keeping Neural Networks Simple by Minimizing the Description Length of the Weights</title><link>https://aipedia.org/mdl-weights/</link><guid isPermaLink="true">https://aipedia.org/mdl-weights/</guid><description>Hinton&apos;s MDL approach to neural network regularization through noisy weights</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>regularization</category><category>mdl</category><category>compression</category></item><item><title>Neural Turing Machines</title><link>https://aipedia.org/neural-turing-machines/</link><guid isPermaLink="true">https://aipedia.org/neural-turing-machines/</guid><description>Neural networks augmented with external memory and attention-based read/write heads</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>memory</category><category>attention</category><category>reasoning</category></item><item><title>Neural Message Passing for Quantum Chemistry</title><link>https://aipedia.org/neural-message-passing/</link><guid isPermaLink="true">https://aipedia.org/neural-message-passing/</guid><description>A unified framework for graph neural networks applied to molecular property prediction</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>gnn</category><category>chemistry</category><category>graphs</category></item><item><title>Order Matters: Sequence to Sequence for Sets</title><link>https://aipedia.org/order-matters/</link><guid isPermaLink="true">https://aipedia.org/order-matters/</guid><description>How input and output ordering affects seq2seq learning on set-structured data</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>seq2seq</category><category>sets</category><category>attention</category></item><item><title>Pointer Networks</title><link>https://aipedia.org/pointer-networks/</link><guid isPermaLink="true">https://aipedia.org/pointer-networks/</guid><description>Neural architecture that outputs pointers to input positions, enabling variable-size outputs</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>attention</category><category>seq2seq</category><category>combinatorial</category></item><item><title>A Simple Neural Network Module for Relational Reasoning</title><link>https://aipedia.org/relational-reasoning/</link><guid isPermaLink="true">https://aipedia.org/relational-reasoning/</guid><description>Relation Networks for learning to reason about object relationships</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>reasoning</category><category>attention</category><category>visual-qa</category></item><item><title>Relational Recurrent Neural Networks</title><link>https://aipedia.org/relational-rnn/</link><guid isPermaLink="true">https://aipedia.org/relational-rnn/</guid><description>RNNs with relational memory that enables reasoning across time</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>rnn</category><category>memory</category><category>attention</category></item><item><title>Identity Mappings in Deep Residual Networks</title><link>https://aipedia.org/resnet-identity/</link><guid isPermaLink="true">https://aipedia.org/resnet-identity/</guid><description>Pre-activation ResNet design that enables training of 1000+ layer networks</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>cnn</category><category>computer-vision</category><category>residual</category></item><item><title>ResNet</title><link>https://aipedia.org/resnet/</link><guid isPermaLink="true">https://aipedia.org/resnet/</guid><description>Deep residual learning with skip connections that enabled training of 152+ layer networks</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>cnn</category><category>computer-vision</category><category>residual</category></item><item><title>Recurrent Neural Network Regularization</title><link>https://aipedia.org/rnn-regularization/</link><guid isPermaLink="true">https://aipedia.org/rnn-regularization/</guid><description>How to apply dropout to LSTMs without disrupting memory dynamics</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>rnn</category><category>lstm</category><category>regularization</category></item><item><title>The Unreasonable Effectiveness of Recurrent Neural Networks</title><link>https://aipedia.org/rnn-effectiveness/</link><guid isPermaLink="true">https://aipedia.org/rnn-effectiveness/</guid><description>Andrej Karpathy&apos;s influential blog post demonstrating RNN capabilities through character-level generation</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>rnn</category><category>nlp</category><category>generative</category></item><item><title>Scaling Laws for Neural Language Models</title><link>https://aipedia.org/scaling-laws/</link><guid isPermaLink="true">https://aipedia.org/scaling-laws/</guid><description>Why bigger models, more data, and more compute lead to predictable gains</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>nlp</category><category>scaling</category><category>transformers</category></item><item><title>Transformer</title><link>https://aipedia.org/transformer/</link><guid isPermaLink="true">https://aipedia.org/transformer/</guid><description>Self-attention models that process sequences in parallel</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>architecture</category><category>attention</category><category>deep-learning</category><category>nlp</category><category>transformer</category></item><item><title>Variational Autoencoder (VAE)</title><link>https://aipedia.org/vae/</link><guid isPermaLink="true">https://aipedia.org/vae/</guid><description>Probabilistic generative model with structured latent space</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>generative</category><category>autoencoder</category><category>latent-space</category></item><item><title>Understanding LSTM Networks</title><link>https://aipedia.org/understanding-lstms/</link><guid isPermaLink="true">https://aipedia.org/understanding-lstms/</guid><description>Christopher Olah&apos;s visual guide to Long Short-Term Memory networks</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>rnn</category><category>lstm</category><category>nlp</category></item><item><title>Variational Lossy Autoencoder</title><link>https://aipedia.org/variational-lossy-autoencoder/</link><guid isPermaLink="true">https://aipedia.org/variational-lossy-autoencoder/</guid><description>Understanding VAEs as compression systems with a rate-distortion trade-off</description><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>vae</category><category>compression</category><category>generative</category></item><item><title>Gradient Boosted Decision Trees</title><link>https://aipedia.org/gbdt/</link><guid isPermaLink="true">https://aipedia.org/gbdt/</guid><description>Sequential tree ensembles optimized via gradient descent</description><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><category>machine-learning</category><category>ensemble</category><category>boosting</category><category>trees</category></item><item><title>NODE (Neural Oblivious Decision Ensembles)</title><link>https://aipedia.org/node/</link><guid isPermaLink="true">https://aipedia.org/node/</guid><description>Differentiable decision trees and oblivious ensembles for tabular learning</description><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>trees</category><category>tabular-data</category><category>differentiable</category></item><item><title>Pre-training</title><link>https://aipedia.org/pre-training/</link><guid isPermaLink="true">https://aipedia.org/pre-training/</guid><description>The stage where a model learns broad patterns from a very large dataset</description><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><category>deep-learning</category><category>training</category><category>foundation-models</category></item><item><title>Stable Marriage Problem</title><link>https://aipedia.org/stable-marriage/</link><guid isPermaLink="true">https://aipedia.org/stable-marriage/</guid><description>Finding a stable matching with the Gale-Shapley deferred acceptance algorithm</description><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><category>algorithm</category><category>matching</category><category>game-theory</category><category>optimization</category></item><item><title>World of Bits</title><link>https://aipedia.org/world-of-bits/</link><guid isPermaLink="true">https://aipedia.org/world-of-bits/</guid><description>Open-domain platform for web-based reinforcement learning agents</description><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><category>reinforcement-learning</category><category>web-agents</category><category>benchmark</category><category>multimodal</category></item></channel></rss>