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ctrl

Conditional Transformer Language Model for Controllable Generation

ctrl-gce

Set up the CTRL text-generating model on Google Compute Engine with just a few console commands.

cu2cl

A prototype CUDA-to-OpenCL source-to-source translator, built on the Clang compiler framework

cub

CUB is a flexible library of cooperative threadblock primitives and other utilities for CUDA kernel programming.

cubenet

3D Roto-translation equivariant convolutions

cubert

cuda-grep

grep on CUDA

cudadtw

GPU-Suite

cudalbfgs

This is a cross-platform, CUDA-based C++ library for general-purpose, unconstrained nonlinear optimization on the GPU. It implements the L-BFGS (“Limited-memory Broyden-Fletcher-Goldfarb-Shanno“) method, a popular Quasi-Newton variant with a low memory footprint.

cudart.jl

Julia wrapper for CUDA runtime API

cumf

CUDA-Acclerated ALS on mulitple GPUs

cunn_hip

cunvsm

Neural Vector Space Models

cupl

curator

Curator: Tending your Elasticsearch indices

curtail

curve-crypto-contract

Guess what

curve-gcn

Official PyTorch code for Curve-GCN (CVPR 2019)

cusha

CuSha is a CUDA-based vertex-centric graph processing framework that uses G-Shards and CW representations.

cutorch_hip

cvpr15deepcompare

Code and models for "Learning to Compare Image Patches via Convolutional Neural Networks"

cvpr2016

Learning Deep Representations of Fine-grained Visual Descriptions

cvprsanitypreserver

cvxpylayers

Differentiable convex optimization layers

cyborg-writer

An experimental text editor with an embedded neural text synthesizer

cyclegan

Software that generates photos from paintings, turns horses into zebras, performs style transfer, and more (from UC Berkeley)

cyclegan-tensorflow-simple

Tensorflow implementation of CycleGAN, mostly modified from https://github.com/XHUJOY/CycleGAN-tensorflow to a simpler version

cyclegan_forest_abstract_art_duke_zm

This is the code corresponding to my Duke AI for Art Competition submission.

d2l-en

Dive into Deep Learning (CV chapters coming soon)

d2rl

Code for the paper "D2RL: Deep Dense Architectures for Reinforcement Learning"