A GOOD REPRESENTATION DETECTS NOISY LABELS

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Last update: Jan 04, 2023

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Overview

A GOOD REPRESENTATION DETECTS NOISY LABELS

This code is a PyTorch implementation of the paper:

Prerequisites

Python 3.6.9

PyTorch 1.7.1

Torchvision 0.8.2

Full list in ./requirements.txt

Datasets will be downloaded to ./data/.

Run HOC + Vote Based and Rank Based method

On CIFAR-10 .

sh ./test_c10_instance.sh

On CIFAR-100

sh ./test_c100_instance.sh

Owner

[email protected]

REsponsible & Accountable Learning (REAL) @ University of California, Santa Cruz

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GitHub Repository

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A GOOD REPRESENTATION DETECTS NOISY LABELS

Related tags

Overview

A GOOD REPRESENTATION DETECTS NOISY LABELS

Prerequisites

Run HOC + Vote Based and Rank Based method

Owner

[email protected]

Prompts - Read a textfile of prompts and import into anki via ankiconnect

Process text, including tokenizing and representing sentences as vectors and Applying some concepts like RNN, LSTM and GRU to create a classifier can detect the language in which a sentence is written from among 17 languages.

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Repositorio oficial del curso IIC2233 Programación Avanzada 🚀✨

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This is the official pytorch implementation for the paper: Instance Similarity Learning for Unsupervised Feature Representation.

Code for "LoRA: Low-Rank Adaptation of Large Language Models"

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code release for USENIX'22 paper `On the Security Risks of AutoML`

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