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celerite2

Fast & scalable Gaussian Processes in one dimension

celerity-runtime

High-level C++ for Accelerator Clusters

celestia

Real-time 3D visualization of space.

cello

Higher level programming in C

cen

[NeurIPS 2020] Code release for paper "Deep Multimodal Fusion by Channel Exchanging" (In PyTorch)

centerface

face detection

centerhmr

CenterHMR: a bottom-up single-shot method for multi-person 3D mesh recovery from a single image

centrifuge

Real-time messaging library for Go with scalability in mind

ceramist

Verified hash-based AMQ structures in Coq

cerberus

Lightweight, extensible data validation library for Python

cerberus-pytorch

Rough implementation of Simultaneous Separation and Transcription of Mixtures with Multiple Polyphonic and Percussive Instruments (Ethan Manilow et al., ICASSP2020)

cereal

A C++11 library for serialization

ceres-solver

A large scale non-linear optimization library

ceres-tutorials

personal practice

ceres_sandbox

My small examples of teaching myself ceres solver

cerl

CERL2.0 - Erlang Model for C++

cern-quantum-computing-course

Quantum computing is one the most promising new trends in information processing. In this course, we will introduce from scratch the basic concepts of the quantum circuit model (qubits, gates and measures) and use them to study some of the most important quantum algorithms and protocols, including those that can be implemented with a few qubits (BB84, quantum teleportation, superdense coding...) as well as those that require multi-qubit systems (Deutsch-Jozsa, Grover, Shor..). We will also cover some of the most recent applications of quantum computing in the fields of optimization and simulation (with special emphasis on the use of quantum annealing, the quantum approximate optimization algorithm and the variational quantum eigensolver) and quantum machine learning (for instance, through the use of quantum support vector machines and quantum variational classifiers). We will also give examples of how these techniques can be used in chemistry simulations and high energy physics problems. The focus of the course will be on the practical aspects of quantum computing and on the implementation of algorithms in quantum simulators and actual quantum computers (as the ones available on the IBM Quantum Experience and D-Wave Leap). No previous knowledge of quantum physics is required and, from the mathematical point of view, only a good command of basic linear algebra is assumed. Some familiarity with the python programming language would be helpful, but is not required either.

cervus

The Cervus Subsystem for Linux

cfdistutilities

Provides some utilities for computing VaR, ES, and EL given a CF

cfm

🌵 Simple and fast TUI file manager with no dependencies.

cfpq-on-gpgpu

cgcnn

Crystal graph convolutional neural networks for predicting material properties.

cgen

C/C++ source generation from an AST

cgmm

Official repository of "Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing", ICML 2018

chaidnn

HLS based Deep Neural Network Accelerator Library for Xilinx Ultrascale+ MPSoCs

chainrules.jl

forward-, reverse-, and mixed-mode automatic differentiation primitives for Julia Base + StdLibs

chalk

An implementation and definition of the Rust trait system using a PROLOG-like logic solver

chamferdistancepytorch

Chamfer Distance in Pytorch with f-score

champ

𓃡 Quadruped Robot based on MIT Cheetah I

chaosnli

Collective HumAn OpinionS on Natural Language Inference Data