arayabrain Goto Github PK

affordancemapping

barebone-studio

pipeline tool

binarysearchlstm

Code for the experiment using reinforcement learning and LSTM networks to learn binary search.

bytetrackfork

[ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box

classifiergenerators

Supporting code for the paper 'Learning to generate classifiers'.

codeqa

Dataset and code for Findings of EMNLP'21 paper "CodeQA: A Question Answering Dataset for Source Code Comprehension".

com.unity.reflect.viewer

Unity Reflect Viewer

conflictmonitoringnetwork

Conflict Monitoring Network

datmo

Detection and Tracking of Moving Objects (DATMO) using sensor_msgs/Lidar.

demtutorial

dnn

Scripts for Deep Neural Network (TensorFlow with Python)

elsiwinterschool

Machine learning examples for ELSI Winter School

fast_lio

A computationally efficient and robust LiDAR-inertial odometry (LIO) package

fewshotclassifier

Code for the blog post on few-shot classification via task representation and communication.

generativecontrol

Code for a generative controller for the AI Gym cartpole task

hokuyo3d

A ROS driver node for VSSP protocol compliant HOKUYO 3D LIDARs

livox_ros_driver2

Livox device driver under Ros(Compatible with ros and ros2), support Lidar HAP and Mid-360.

meg-decoding

Reimplementation of speech decoding 2022 paper by MetaAI for Yanagisawa MEG2Image Project

microscopes-reader-sample

models

Models and examples built with TensorFlow

mountaincar2d

mrianalysisstudioformouse

pipeline tool for mouse MRI analysis

multi-modal-integration

multimodal-global-workspace-agent

neural-coarse-graining

Reference implementation of the neural coarse-graining algorithm in Python/Theano/Lasagne.

neural-network-extrapolation-tests

Place for various notebooks testing the extrapolation properties of neural networks

on-premises-optinist-duplicate

calcium imaging pipeline tool for multiple users

optimizercomparisons

Some experiments comparing different optimizers.

optinist-for-server

pipeline tool

permutationalnetworks

Code for our paper on dynamics prediction using permutation-equivariant neural networks. The base of the repository contains an implementation of a drop-in Lasagne layer, and the subdirectories contain specific experiments from the paper.