LSTM

A practical tutorial on LSTM interpretability demonstrates how to preprocess time-series data into a 3D tensor, build a stacked LSTM model with dropout and early stopping, and apply permutation import…

Researchers at a single pediatric institution developed TEDDY (Temporal Event Decoder for Disease in Youth), a 1.84-million-parameter decoder transformer trained on approximately 73 million ICD-10 dia…

A study using 134,376 hourly weather observations from Ioannina, Greece (2011-2026) found that hybrid CNN-GRU models outperform traditional recurrent networks in weather forecasting, achieving a Weigh…

A new study from arXiv (2607.09684v1) evaluating Scientific Machine Learning methods including Neural ODEs, PINNs, and UDEs across 23 countries finds that none of the models achieve consistently stron…

A new neuro-agentic control framework coupling an LLM-based planner with a pre-trained Time-Series Foundation Model (TimesFM) achieved zero physically invalid actions and prevented 33.3% of security b…

Researchers applied a Spectral Temporal Graph Neural Network (StemGNN) to predict user equipment scheduling states in 5G networks, recovering 57-73% of sum rate loss caused by backhaul latency. The mo…

Researchers developed a Q-learning-based adaptive retraining method to address traffic drift in Open Radio Access Networks (O-RAN), reducing retraining overhead while maintaining forecasting accuracy.…

A study comparing LSTM networks with traditional machine learning models for sentiment analysis on Twitter data found that LSTM outperformed logistic regression, random forest, naive Bayes, and gradie…

A study on the 'Granularity Paradox' in time-series forecasting finds that finer temporal disaggregation improves in-sample fit but degrades out-of-sample accuracy due to recursive error compounding. …

Researchers proposed an uncertainty-aware reinforcement learning framework for algorithmic trading that integrates distributional, epistemic, and aleatoric uncertainty estimations using SHAP-weighted …

Researchers propose an agentic AI pipeline for appliance-level energy anomaly detection in office buildings, combining deep time-series forecasting, variational anomaly detection, and LLM-based reason…

Shrijith Venkatramana, building git-lrc, explains that sequence transduction—transforming one sequence into another—was the original problem that led to modern large language models. Early neural netw…

A developer building LSTMs with PyTorch and Lightning AI implemented the training_step function and ran initial predictions without training. The model predicted Company A's stock price reasonably clo…

Researchers introduced the Integral Transform Network (ITNet), a unified architecture that subsumes convolution, attention, and recurrence as special cases of a learnable integral transform. ITNet mat…

The Transformer architecture, introduced in the 2017 paper 'Attention Is All You Need', revolutionized AI by replacing sequential RNNs with a parallelizable attention mechanism. This mechanism allows …

Researchers achieved 98.91% accuracy in multimodal emotion recognition from physiological signals by combining LSTM, TCN, and Transformer models with late-fusion ensemble on the WESAD dataset. Transfo…

Jürgen Schmidhuber, a pioneer in artificial intelligence whose lab developed foundational ideas like LSTM, world models, and artificial curiosity decades before they became mainstream, argued in a new…

A developer completed a baseline implementation for the Kaggle Orbit Wars competition, achieving an initial score of around 1030 before the score dipped slightly. The developer also used AWS for the f…

A new arXiv preprint (arXiv:2606.04574) submitted June 3, 2026, presents a hybrid trading architecture that combines statistical pair selection with a Deep Reinforcement Learning execution overlay for…

A study evaluating encoder-only Transformer and LSTM frameworks for streamflow prediction in ungauged basins found that the LSTM outperformed the Transformer across both upstream-only and combined con…