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cryptowealth-technology's Projects

restormer

[CVPR 2022--Oral] Restormer: Efficient Transformer for High-Resolution Image Restoration. SOTA for motion deblurring, image deraining, denoising (Gaussian/real data), and defocus deblurring.

revnet-public

Code for "The Reversible Residual Network: Backpropagation Without Storing Activations"

riskparity.py

Fast and scalable design of risk parity portfolios

rl-baselines3-zoo

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

rl-toolkit

RL-Toolkit: A Research Framework for Robotics

robo-vln

Pytorch code for ICRA'21 paper: "Hierarchical Cross-Modal Agent for Robotics Vision-and-Language Navigation"

robot_selfsound

Lab rotation project attempting to predict and subtract ego-noise using neural networks.

robust-log-optimal-strategy-with-reinforcement-learning

We propose a new Portfolio Management strategy combining Log-Optimal based Strategy and Reinforcement-Learning based Strategy.

root-finding-methods

Utilizing root-finding methods such as Bisection Method, Fixed-Point Method, Secant Method, and Newton's Method to solve for the roots of functions

rotatenetworks

The rep for the RotateNetworks in ICPR18, Beijing, China.

Convolutional neural network (2D index) approximate invariant properties implicitly by max-pooling. Thus filters fail to generalize with drastic changes in scale and rotation. In this page, I show 2D convolution on "Frequency" & "Direction" is a natural way to think of scaling and rotation.

rotational_invariance_data_augmentation

Code for "Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks"

rotational_variance

Code for "Measuring (in)variances in Convolutional Neural Networks Internal Representations"

rotationaldynamics

Code for creating recurrent neural network with rotational dynamics. Model is discussed in detail in "Rotational Dynamics Reduce Interference Between Sensory and Memory Representations" by Libby and Buschman.

rowdy_activation_functions

We propose Deep Kronecker Neural Network, which is a general framework for neural networks with adaptive activation functions. In particular we proposed Rowdy activation functions that inject sinusoidal fluctuations thereby allows the optimizer to exploit more and train the network faster. Various test cases ranging from function approximation, inferring the PDE solution, and the standard deep learning benchmarks like MNIST, CIFAR-10, CIFAR-100, SVHN etc are solved to show the efficacy of the proposed activation functions.

s2cnn

Spherical CNNs

sacred

Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

salib

Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.

salina

a Lightweight library for sequential learning agents, including reinforcement learning

sampling-free

Is heuristic sampling necessary in training deep object detectors? Try sampling-free object detectors!

satbot

an empathetic chatbot for self-attachment psychotherapy

scops

SCOPS: Self-Supervised Co-Part Segmentation (CVPR'19)

sdedit

PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

se3-transformer-public

code for the SE3 Transformers paper: https://arxiv.org/abs/2006.10503

se3cnn

Euclidean Neural Networks

second-wind

Public Repo for Download Files

sector-rotation-rnn

A simple, self-coded recurrent neural network that uses weekly changes in 10 major sector ETFs to predict which sectors will grow in the coming weeks.

security-and-robustness-of-deep-learning-in-wireless-communication-systems

A research oriented repository on the Security and Robustness of Deep Learning for Wireless Communication Systems

seg-uncertainty

IJCAI2020 & IJCV 2021 :city_sunrise: Unsupervised Scene Adaptation with Memory Regularization in vivo

selectionfromworkingmemory

Behavioral and custom analysis functions supporting Panichello and Buschman, 2021.

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