laplacekorea Goto Github PK

lafak

The code of paper "Adversarial Label-Flipping Attack and Defense for Graph Neural Networks" (ICDM 2020)

lagraph

This is a library plus a test harness for collecting algorithms that use the GraphBLAS. For test coverage reports, see https://graphblas.org/LAGraph/ .

lairgpt

Inference code in Pytorch for GPT-like models, such as PAGnol, a family of models with up to 1.5B parameters, trained on datasets in French.

lambeq

A high-level Python library for Quantum Natural Language Processing

lancet

A comprehensive, efficient, and reusable util function library of go.

lanczos

Lanczos tridiagonalization on a GPU

language-model-recommendation

Resources accompanying the "Zero-Shot Recommendation as Language Modeling" paper (ECIR2022)

language-planner

Official Code for "Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents"

lapce

Lightning-fast and Powerful Code Editor written in Rust

lapjv

Linear Assignmment Problem solver using Jonker-Volgenant algorithm - Python 3 native module.

laplace

Laplace approximations for Deep Learning.

large_scale_cc

Matlab code implementing optimization algorithms presented in: Shai Bagon and Meirav Galun Large Scale Correlation Clustering Optimization (arXiv'2011).

laser_control

Laser for control mosquito, weed and pest

latent-diffusion

High-Resolution Image Synthesis with Latent Diffusion Models

latent_ode

Code for "Latent ODEs for Irregularly-Sampled Time Series" paper

latex-online

Online latex compiler. You give it a link, it gives you PDF

lattigo

A library for lattice-based homomorphic encryption in Go

lav

(CVPR 2022) A minimalist, mapless, end-to-end self-driving stack for joint perception, prediction, planning and control.

layr

A decentralized (p2p) file storage system built atop Kademlia DHT that enforces data integrity, privacy, and availability through sharding, proofs of retrievability, redundancy, and encryption, with smart-contract powered incentive scheme

lazycli

Turn static CLI commands into TUIs with ease

lbfgs-lite

A header-only LBFGS unconstrained optimizer.

lbfgsb.jl

Julia wrapper for L-BFGS-B Nonlinear Optimization Code

lbl2vec

Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.

lc3-vm

A LC3 virtual machine implementation in a few lines of C code

lda4rec

🧮 Matrix Factorization for Collaborative Filtering is just Solving an Adjoint Latent Dirichlet Allocation Model after All

ldc_downloader

Script to download corpora from the Linguistic Data Consortium (LDC)

learn-fpga

Learning FPGA, yosys, nextpnr, and RISC-V

learn-to-calibrate

CoRL 2020

learning-quantum-computing-course

⚛️ 💥 ⚙️ A project based in Quantum Physics/Mechanics and Quantum Computing. This projects contains Tutorials, Examples, Exercises, Papers, Posters and Illustrations covering several concepts of Quantum Physics/Mechanics and Quantum Computing, such as Quantum Information Science, Quantum Algorithms, Quantum Error Correction, Quantum Internet/Networks, Quantum Cryptography, Quantum Artificial Intelligence, Quantum Machine Learning and Quantum Game Theory. It works as a support for the Online Web Course of Learning Quantum Computing available in the following GitHub Page: https://rubenandrebarreiro.github.io/learning-quantum-computing. This project it's built using OpenQASM Framework (IBM's Open Quantum Assembly Language), Qiskit Framework (IBM Q Experience), Cirq Framework (Google AI Quantum), Q# Framework (Microsoft Quantum Development Kit - QDK), PyQuil Framework (Rigetti's Forest SDK), PennyLane Framework (Xanadu Quantum Technologies), Project Q Framework (ETHZ's Open-Source Software Framework for Quantum Computing) and Quantum Inspire (TU Delft's/QuTech's Quantum Computing Platform). The Tutorials, Examples and Exercises are mostly built in Python and Jupyter Notebooks. The Papers are built in TeX (LaTeX). The Posters and Illustrations are built in Adobe Photoshop, Adobe Illustrator and Adobe Photoshop Lightroom. Some Tutorials, Examples and Exercises use other important Packages, Libraries and Frameworks for Data Science, Cybersecurity, Cryptography, Quantum Physics/Mechanics and Quantum Computing, such as Bokeh, Cython, Dask, Jython, Matplotlib, Numba, NumPy, PyTorch, SciKit Learn, SciPy, SymPy, TensorFlow, Theano, QuTip, or even, OpenSSL and Python's Cryptography Library. Some Examples and Tutorials also use the NetSquid and SimulaQron Simulators Frameworks (TU Delft's/QuTech's Quantum Internet's/Network Simulators).

learntla-v2