In this repo we reproduce and extend results of Learning in High Dimension Always Amounts to Extrapolation by Balestriero et al. 2021

Overview

In this repo we reproduce and extend results of Learning in High Dimension Always Amounts to Extrapolation by Balestriero et al. 2021. Balestriero et al. did not retrain the embedding models in their experiments to predict embeddings of the selected dimension (see Table 1). In this repo, we go further to retrain a small CNN on MNIST across a range of embedding dimensions and compute the proportion of the test set embedddings that fall into the interpolation regime (i.e. the convex hull of the training set embeddings). Perhaps unsurprisingly, we reproduce their results, finding that the proportion drops quickly and is near 0 by embedding dimension 30.

Owner
Sean M. Hendryx
"everything that is not forbidden by laws of nature is achievable, given the right knowledge" - David Deutsch
Sean M. Hendryx
This repo contains the source code and a benchmark for predicting user's utilities with Machine Learning techniques for Computational Persuasion

Machine Learning for Argument-Based Computational Persuasion This repo contains the source code and a benchmark for predicting user's utilities with M

Ivan Donadello 4 Nov 07, 2022
Code for Learning Manifold Patch-Based Representations of Man-Made Shapes, in ICLR 2021.

LearningPatches | Webpage | Paper | Video Learning Manifold Patch-Based Representations of Man-Made Shapes Dmitriy Smirnov, Mikhail Bessmeltsev, Justi

Dima Smirnov 22 Nov 14, 2022
A simple baseline for 3d human pose estimation in tensorflow. Presented at ICCV 17.

3d-pose-baseline This is the code for the paper Julieta Martinez, Rayat Hossain, Javier Romero, James J. Little. A simple yet effective baseline for 3

Julieta Martinez 1.3k Jan 03, 2023
Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" (NeurIPS'20)

IGNN Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" [paper] [supp] Prepare datasets 1 Download training dataset

Shangchen Zhou 278 Jan 03, 2023
Meta Learning Backpropagation And Improving It (VSML)

Meta Learning Backpropagation And Improving It (VSML) This is research code for the NeurIPS 2021 publication Kirsch & Schmidhuber 2021. Many concepts

Louis Kirsch 22 Dec 21, 2022
Unified tracking framework with a single appearance model

Paper: Do different tracking tasks require different appearance model? [ArXiv] (comming soon) [Project Page] (comming soon) UniTrack is a simple and U

ZhongdaoWang 300 Dec 24, 2022
Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.

Pretrained Language Model This repository provides the latest pretrained language models and its related optimization techniques developed by Huawei N

HUAWEI Noah's Ark Lab 2.6k Jan 01, 2023
RetinaFace: Deep Face Detection Library in TensorFlow for Python

RetinaFace is a deep learning based cutting-edge facial detector for Python coming with facial landmarks.

Sefik Ilkin Serengil 512 Dec 29, 2022
Companion code for the paper Theoretical characterization of uncertainty in high-dimensional linear classification

Companion code for the paper Theoretical characterization of uncertainty in high-dimensional linear classification Usage The required packages are lis

0 Feb 07, 2022
[ACL 20] Probing Linguistic Features of Sentence-level Representations in Neural Relation Extraction

REval Table of Contents Introduction Overview Requirements Installation Probing Usage Citation License 🎓 Introduction REval is a simple framework for

13 Jan 06, 2023
A unet implementation for Image semantic segmentation

Unet-pytorch a unet implementation for Image semantic segmentation 参考网上的Unet做分割的代码,做了一个针对kaggle地盐识别的,请去以下地址获取数据集: https://www.kaggle.com/c/tgs-salt-id

Rabbit 3 Jun 29, 2022
Multi-agent reinforcement learning algorithm and environment

Multi-agent reinforcement learning algorithm and environment [en/cn] Pytorch implements multi-agent reinforcement learning algorithms including IQL, Q

万鲲鹏 7 Sep 20, 2022
Website which uses Deep Learning to generate horror stories.

Creepypasta - Text Generator Website which uses Deep Learning to generate horror stories. View Demo · View Website Repo · Report Bug · Request Feature

Dhairya Sharma 5 Oct 14, 2022
Accelerated NLP pipelines for fast inference on CPU and GPU. Built with Transformers, Optimum and ONNX Runtime.

Optimum Transformers Accelerated NLP pipelines for fast inference 🚀 on CPU and GPU. Built with 🤗 Transformers, Optimum and ONNX runtime. Installatio

Aleksey Korshuk 115 Dec 16, 2022
Disentangled Face Attribute Editing via Instance-Aware Latent Space Search, accepted by IJCAI 2021.

Instance-Aware Latent-Space Search This is a PyTorch implementation of the following paper: Disentangled Face Attribute Editing via Instance-Aware Lat

67 Dec 21, 2022
A deep learning CNN model to identify and classify and check if a person is wearing a mask or not.

Face Mask Detection The Model is designed to check if any human is wearing a mask or not. Dataset Description The Dataset contains a total of 11,792 i

1 Mar 01, 2022
People log into different sites every day to get information and browse through these sites one by one

HyperLink People log into different sites every day to get information and browse through these sites one by one. And they are exposed to advertisemen

0 Feb 17, 2022
Tech Resources for Academic Communities

Free tech resources for faculty, students, researchers, life-long learners, and academic community builders for use in tech based courses, workshops, and hackathons.

Microsoft 2.5k Jan 04, 2023
A simple python module to generate anchor (aka default/prior) boxes for object detection tasks.

PyBx WIP A simple python module to generate anchor (aka default/prior) boxes for object detection tasks. Calculated anchor boxes are returned as ndarr

thatgeeman 4 Dec 15, 2022
paper: Hyperspectral Remote Sensing Image Classification Using Deep Convolutional Capsule Network

DC-CapsNet This is a tensorflow and keras based implementation of DC-CapsNet for HSI in the Remote Sensing Letters R. Lei et al., "Hyperspectral Remot

LEI 7 Nov 29, 2022