Mitra regressor is a tabular foundation model that is pre-trained on purely synthetic datasets sampled from a mix of random regressors. Mitra is based on a 12-layer Transformer of 72 M parameters, pre-trained by incorporating an in-context learning paradigm. To use Mitra regressor, install AutoGluon by running: A minimal example showing how to perform inference using the Mitra regressor: This project is licensed under the Apache-2.0 License. Amazon Science blog: Mitra: Mixed synthetic priors for enhancing tabular foundation models
Open weights
apache-2.0
76M parameters
Day-ahead electricity price forecasts 2 to 9 days out, hourly, in EUR/MWh, for every bidding zone in Denmark, Norway, Sweden, Finland and the Netherlands: Retrained and re-uploaded daily, so the weights here are what production runs. A companion model specialised to Denmark alone lives at A single LightGBM quantile model covers all 13 zones, with zone as a categorical feature. The zones share nearly all their structure — the same calendar effects, the same weather physics, the same lag behaviour — so pooling lets a thin zone borrow strength from a busy one, and leaves one thing to maintain instead of thirteen. Pooling only works because the model can identify a zone's price level before it…
Open weights
cc-by-4.0
lightgbm
A small tabular model that estimates the native frame rate of a PC game at 1080p, 1440p or 4K from two things you can know before buying or installing: the PC's hardware scores and the game's Steam store requirements. It is the FPS engine of the CanRun G-Assist plugin and runs with the Python standard library alone. Real-world accuracy: on independent benchmarks the model alone is a median 18.9% off on recent games and NVIDIA cards, and 37% to 39% off on other test sets. The full CanRun app, which adds benchmark curves and live Steam lookups, is 15.2% off end to end. Treat every prediction as a rough estimate, good for "will this run at 60 FPS?" and not for exact numbers. Try it: the CanRun…
Open weights
apache-2.0
generic
Predicts where a league-average goalkeeper would position themselves, including the in-possession high-sweeper regime that the standard Ghost-GK v1 model cannot represent. Point-estimate (ghostgkx/y) served as the exact pickle-free boosted HistGradientBoostingRegressor mean. Part of the silly-kicks soccer analytics library (TF-60 rest-defense; ADR-083). The bundled Ghost-GK default/full variants train on a fixed goal-relative box x ∈ [0, 30] m from the defended goal: a keeper who has swept far upfield falls outside it and is dropped as a "sweeper rush". That is correct for normal in-goal positioning, but it makes the model hard-saturate at 30 m — it cannot place an in-possession keeper who…
Open weights
mit
silly-kicks
Predicts where a league-average goalkeeper would position themselves given the current game state. Uses RFCDE (Random Forest Conditional Density Estimation) over HistGradientBoostingRegressor leaf assignments with weighted 2D kernel density estimation. Part of the silly-kicks soccer analytics library (GKDV research program, TF-18, Layer 2). Standard goalkeeper evaluation metrics (xGOT, save percentage, goals prevented) measure what happens after a shot is taken. Ghost-GK addresses the upstream question: given the current game state, where should the goalkeeper be standing? The model learns a league-average positional density from thousands of match frames across multiple tracking data…
Open weights
mit
silly-kicks
Predicts where a league-average outfield rearguard defender would position themselves, per tracking frame, team, and lateral slot. The outfield sibling of Ghost-GK v1: a point estimate (ghostgrx/y, goal-relative) served as the exact pickle-free boosted HistGradientBoostingRegressor mean. Part of the silly-kicks soccer analytics library (TF-60 rest-defense; ADR-087). It is the league-average baseline the rest-defense outfield counterfactual arm differences against ("how far does the actual rest-defense shape sit from average?"). For an in-possession team's rest defense (the rearguard it keeps while attacking, to blunt the counter after a loss), the model predicts the league-average position…
Open weights
mit
silly-kicks
R
Model · Tabular regression
Reddy
A multi-output Random Forest regressor for predicting residential property energy consumption and valuation, built for the Lloyds Green Home Score ESG intelligence platform. This model predicts three targets simultaneously from 13 property features: - Synthetic simulator consistency only, not real-world accuracy - Current value model validation is synthetic, not historical UK transaction validation - Value ranges are sensitivity bands, not calibrated confidence intervals Part of the Lloyds Green Home Score platform for ESG risk assessment, retrofit recommendation, and green lending analytics. Not intended for direct property valuation or energy certificate prediction. - multioutputrf.joblib…
Open weights
mit
sklearn
Sentinel-Mumbai Hybrid PINN is an attention-augmented Physics-Informed Neural Network (PINN) developed in Keras 3 and optimized for high-throughput GPU inference (Tesla T4 mixed-precision float16). The network incorporates a 24-hour temporal sequence lookback and enforces hydraulic mass conservation via a partial differential equation (PDE) continuity constraint during gradient descent: $$\text{PDE Residual} = \frac{\partial \hat{y}}{\partial t} - (\text{Rainfall} - \text{Infiltration}) = 0$$ Where $\text{Infiltration} \approx 0.0$ on impervious urban concrete surfaces (landcover class 80). Released under the MIT License.
Open weights
mit
keras
Sentinel-Mumbai PINN v1 is a high-capacity Physics-Informed Neural Network (PINN) engineered for hyper-local urban flood depth forecasting and storm surge dynamics across Mumbai / Salsette Island. Trained across multi-decadal telemetry (rainfall, cumulative precipitation, tidal surge oscillations, elevation, urban impermeability, and drainage networks), the model embeds physical boundary laws directly into its optimization objective, preventing unphysical mass disappearance during coastal hydraulic lock events. The model optimizes a composite objective balancing empirical data fidelity against hydraulic conservation of mass: $$\mathcal{L}{\text{total}} = \alpha \cdot…
Open weights
mit
pytorch
Sentinel-V7 Deep Hydrological Bi-LSTM is a specialized recurrent neural sequence model engineered for real-time flood risk scoring and inundated area forecasting. It maps multi-sensor remote sensing inputs (Copernicus Sentinel-1 SAR radar backscatter, digital elevation models, river network proximity, ECMWF GEOGloWS river discharge, and precipitation metrics) to a continuous flood vulnerability metric. The model is packaged in Keras 3 format along with its fitted Scikit-Learn MinMaxScaler (scaler.joblib), ensuring seamless deployment on edge systems and cloud microservices. Released under the MIT License.
Open weights
mit
keras
~425K 파라미터 4-Layer Dynamic GAU 기반 실전 주식 트레이딩 파운데이션 모델입니다. ATR 연동 동적 배리어 라벨링과 고확신 타점 필터링으로 손익비 1.5:1 이상을 타겟팅합니다.
Open weights
apache-2.0
490,602 parameters
safetensors
