Code for the paper "A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses" (ECCV 2020 - Spotlight)
-
Updated
Nov 25, 2022 - Python
Code for the paper "A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses" (ECCV 2020 - Spotlight)
Easy to use class balanced cross entropy and focal loss implementation for Pytorch
Code lab for deep learning. Including rnn,seq2seq,word2vec,cross entropy,bidirectional rnn,convolution operation,pooling operation,InceptionV3,transfer learning.
A python metaheuristic optimization library. Currently supports Genetic Algorithms, Gravitational Search, Cross Entropy, and PBIL.
Numerically Stable Cross Entropy Loss Function Implemented with Python and Tensorflow
Implementation of Reinforcement Algorithms from scratch
compare the performance of cross entropy, focal loss, and dice loss in solving the problem of data imbalance
Song lyrics generation using Recurrent Neural Networks (RNNs)
ORDA research Triton kernel for logit-level forward-KL distillation with a fused Cross Entropy substrate.
We apply the noisy cross-entropy method to the game of Tetris to demonstrate its efficiency.
Towards Generalization in Subitizing with Neuro-Symbolic Loss using Holographic Reduced Representations
This repository contains a collection of PyTorch scripts that implement and explore the fundamental concepts of probability, information theory, and generative modeling from scratch. It serves as a practical "cookbook" for understanding the building blocks of modern generative AI.
A data classification using MLP
UCSD CSE 257: Search and Optimization (Winter 2025)
HACE is a drop-in replacement for cross-entropy that incorporates class hierarchies via prediction aggregation and ancestral label smoothing.
A pytorch implementation of Densenet for FashionMNIST dataset
PyTorch Implementation of Robust Cross Entropy Loss (Loss Correction for Label Noise)
A neural learning network built from pure mathematical theory
Add a description, image, and links to the cross-entropy topic page so that developers can more easily learn about it.
To associate your repository with the cross-entropy topic, visit your repo's landing page and select "manage topics."