Pinned Repositories
canallee.github.io
Home page for Jianan Canal Li
CLEAN
CLEAN: a contrastive learning model for high-quality functional prediction of proteins
Cyclical-Kernel-Adaptive-Metropolis
This repo contains the official code for the paper Cyclical Kernel Adaptive Metropolis
hgcn
Hyperbolic Graph Convolutional Networks in PyTorch.
MCF_hyperbolic_Learning
Official implementation (learning part) for paper: Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models
MCTensor
This is the official repo for MCTensor library
OCaml-neural-style-transfer
This is the final project for Cornell CS3110 - Data Structures and Functional Programming. This repo belongs to Canal Li, Thomas Cui and Canwen Zhang
poincare
PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"
PseudoMSA
This is the repo for the project "PseudoMSA: Towards High-fitness Protein Variant Generation Guided by Protein Language Models"
CLEAN
CLEAN: a contrastive learning model for high-quality functional prediction of proteins
canallee's Repositories
canallee/MCF_hyperbolic_Learning
Official implementation (learning part) for paper: Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models
canallee/canallee.github.io
Home page for Jianan Canal Li
canallee/CLEAN
CLEAN: a contrastive learning model for high-quality functional prediction of proteins
canallee/Cyclical-Kernel-Adaptive-Metropolis
This repo contains the official code for the paper Cyclical Kernel Adaptive Metropolis
canallee/hgcn
Hyperbolic Graph Convolutional Networks in PyTorch.
canallee/MCTensor
This is the official repo for MCTensor library
canallee/OCaml-neural-style-transfer
This is the final project for Cornell CS3110 - Data Structures and Functional Programming. This repo belongs to Canal Li, Thomas Cui and Canwen Zhang
canallee/poincare
PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"
canallee/PseudoMSA
This is the repo for the project "PseudoMSA: Towards High-fitness Protein Variant Generation Guided by Protein Language Models"