Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond - NeurIPS 2023
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Updated
Oct 5, 2023 - Python
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond - NeurIPS 2023
we propose a novel FusionGDA model, which utilises a pre-training phase with a fusion module to enrich the gene and disease semantic representations encoded by pre-trained language models.
Code for the Paper : NBC-Softmax : Darkweb Author fingerprinting and migration tracking (https://arxiv.org/abs/2212.08184)
Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding (Findings of EMNLP'23)
Self-Supervised Contrastive Learning for Colon Pathology Classification
Contrastive-LSH Embedding and Tokenization Technique for Multivariate Time Series Classification
Contrastive Unlearning
RRCGAN:A Radiometric Resolution Compression Method for Optical Remote Sensing Images Using Contrastive Learning
This project has a comprehensive exploration of two key topics: Softmax Regression and Contrastive Representation Learning. The dataset used for this project is the CIFAR-10 dataset, which can be accessed by link given below
Implementation of modulated sigmoid pairwise contrastive loss for self-supervised learning on images
Neural inverted index for fast and effective information retrieval
Tumor detection and classification from abdominal ultrasound images using CenterNet with Contrastive Learning.
Code for the paper "Category-Level Pose Retrieval with Contrastive Features Learnt with Occlusion Augmentation"
Contrastive representation learning with PyTorch
[IMAGE24] Contrastive learning for deep tone mapping operator
A PyTorch-based system for highly accurate drug-target interaction predictions utilizing multi-modal large language models to discern structural affinities in drug-target pairs.
Official implementation of the ACL Findings 2023 paper: Multimedia Generative Script Learning for Task Planning
Comparing performance of different InfoNCE type losses used in contrastive learning.
Codes and data for ICDM 2020 Best Student Paper Runner-up "Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning"
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