Faster Convex Lipschitz Regression

Overview

Faster Convex Lipschitz Regression

This reepository provides a python implementation of our Faster Convex Lipschitz Regression algorithm with GPU and CPU based on Pytorch.

Contact

You can contact us at [email protected]

You might also like...
PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM
PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM

Quasi-Recurrent Neural Network (QRNN) for PyTorch Updated to support multi-GPU environments via DataParallel - see the the multigpu_dataparallel.py ex

A pytorch implementation of faster RCNN detection framework (Use detectron2, it's a masterpiece)
A pytorch implementation of faster RCNN detection framework (Use detectron2, it's a masterpiece)

Notice(2019.11.2) This repo was built back two years ago when there were no pytorch detection implementation that can achieve reasonable performance.

shufflev2-yolov5:lighter, faster and easier to deploy
shufflev2-yolov5:lighter, faster and easier to deploy

shufflev2-yolov5: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~

🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~
🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~

YOLOv5-Lite:lighter, faster and easier to deploy Perform a series of ablation experiments on yolov5 to make it lighter (smaller Flops, lower memory, a

Optimized code based on M2 for faster image captioning training

Transformer Captioning This repository contains the code for Transformer-based image captioning. Based on meshed-memory-transformer, we further optimi

Faster RCNN with PyTorch

Faster RCNN with PyTorch Note: I re-implemented faster rcnn in this project when I started learning PyTorch. Then I use PyTorch in all of my projects.

Tacotron 2 - PyTorch implementation with faster-than-realtime inference
Tacotron 2 - PyTorch implementation with faster-than-realtime inference

Tacotron 2 (without wavenet) PyTorch implementation of Natural TTS Synthesis By Conditioning Wavenet On Mel Spectrogram Predictions. This implementati

LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.
LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image.

This project is based on ultralytics/yolov3. LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image. Download $ git clone http

A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.
A GPU-optional modular synthesizer in pytorch, 16200x faster than realtime, for audio ML researchers.

torchsynth The fastest synth in the universe. Introduction torchsynth is based upon traditional modular synthesis written in pytorch. It is GPU-option

Owner
Ali Siahkamari
Boston University, PhD Candidate in Electrical Engineering
Ali Siahkamari
Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression

Quantile Regression DQN Quantile Regression DQN a Minimal Working Example, Distributional Reinforcement Learning with Quantile Regression (https://arx

Arsenii Senya Ashukha 80 Sep 17, 2022
Hitters Linear Regression - Hitters Linear Regression With Python

Hitters_Linear_Regression Kullanacağımız veri seti Carnegie Mellon Üniversitesi'

AyseBuyukcelik 2 Jan 26, 2022
Riemannian Convex Potential Maps

Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this space are limited by representational and computational tradeoffs. We propose and study a class of flows that uses convex potentials from Riemannian optimal transport. These are universal and can model distributions on any compact Riemannian manifold without requiring domain knowledge of the manifold to be integrated into the architecture. We demonstrate that these flows can model standard distributions on spheres, and tori, on synthetic and geological data.

Facebook Research 61 Nov 28, 2022
Neural Fixed-Point Acceleration for Convex Optimization

Licensing The majority of neural-scs is licensed under the CC BY-NC 4.0 License, however, portions of the project are available under separate license

Facebook Research 27 Oct 6, 2022
Code in PyTorch for the convex combination linear IAF and the Householder Flow, J.M. Tomczak & M. Welling

VAE with Volume-Preserving Flows This is a PyTorch implementation of two volume-preserving flows as described in the following papers: Tomczak, J. M.,

Jakub Tomczak 87 Dec 26, 2022
Universal Probability Distributions with Optimal Transport and Convex Optimization

Sylvester normalizing flows for variational inference Pytorch implementation of Sylvester normalizing flows, based on our paper: Sylvester normalizing

Rianne van den Berg 172 Dec 13, 2022
Convex optimization for fun and profit.

CFMM Optimal Routing This repository contains the code needed to generate the figures used in the paper Optimal Routing for Constant Function Market M

Guillermo Angeris 183 Dec 29, 2022
ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

ESGD-M - A stochastic non-convex second order optimizer, suitable for training deep learning models, for PyTorch

Katherine Crowson 53 Dec 29, 2022
LeViT a Vision Transformer in ConvNet's Clothing for Faster Inference

LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference This repository contains PyTorch evaluation code, training code and pretrained

Facebook Research 504 Jan 2, 2023
SparseML is a libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

Neural Magic 1.5k Dec 30, 2022