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Name: AaltoML

Type: Organization

Bio: Machine learning group at Aalto University lead by Prof. Solin

Location: Finland

Blog: http://arno.solin.fi

AaltoML's Projects

accelerated-arrays

Lightweight accelerated tensor / array programming library for smart phone GPUs

android-viotester

Visual-Inertial Odometry (VIO) benchmark app for Android

apml

Advances in Probabilistic Machine Learning Seminar

bayesnewton

Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.

boundary-gp

Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features

calibrated-dnn

Repository containing code for the paper: Fixing Overconfidence in Dynamic Neural Networks

generative-inverse-heat-dissipation

Code release for the paper Generative Modeling With Inverse Heat Dissipation

gp-mvs

Multi-View Stereo by Temporal Nonparametric Fusion

gpflow

Gaussian processes in TensorFlow

gpt-2-pytorch

Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation

hamiltorch

PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks

hilbert-gp

Codes for Hilbert space reduced-rank GP regression

hybvio

HybVIO visual-inertial odometry and SLAM system

ihgp

Infinite-horizon Gaussian processes

improved-hyperparameter-learning

Codes for 'Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models' (ICML 2023)

iterative-smoothing-bridge

Code for 'Transport with Support: Data-Conditional Diffusion Bridges'

kalman-jax

Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX

magnetic-data

Example magnetometer data from a wheeled robot

mobile-cv-suite

A Computer Vision & real-time ML resarch library for mobile phones. The building blocks required to test new algorithms on mobile & embedded devices.

newt

Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's algorithm.

nonstationary-audio-gp

End-to-End Probabilistic Inference for Nonstationary Audio Analysis

orb_slam3

ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM

periodicbnn

Code for 'Periodic Activation Functions Induce Stationarity' (NeurIPS 2021)

psd-pc

Repository for code utilized in 'Encoding Negative Dependencies in Probabilistic Circuits' paper submitted to the Tractable Probabilistic Modelling 2023 workshop

scalable-inference-in-sdes

Methods and experiments for assumed density SDE approximations

sde

Example codes for the book Applied Stochastic Differential Equations

sequential-gp

Code for 'Memory-based dual Gaussian processes for sequential learning' (ICML 2023)

sfr

PyTorch implementation of Sparse Function-space Representation of Neural Networks

sfr-experiments

Code accompanying ICLR 2024 paper "Function-space Parameterization of Neural Networks for Sequential Learning"

slam-module

SLAM module

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