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Found 7 papers, 4 papers with code
Contrastive Abstraction for Reinforcement Learning
Contrastive abstraction learning first constructs clusters of state representations by contrastive learning and then applies modern Hopfield networks to determine the abstract states.
Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget
In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data.
Ranked #1 on
Image Clustering
on Imagenet-dog-15
(using extra training data)
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
We suggest to use modern Hopfield networks to tackle the problem of explaining away.
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs.
Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images
The aim of this study is to evaluate whether it is possible to detect basal cell carcinomas in histological sections using attention-based deep learning models and to overcome the ultra-high resolution and the weak labels of whole slide images.
End-to-end learning of pharmacological assays from high-resolution microscopy images
Surprisingly, we could predict 29% of the 209 pharmacological assays at high predictive performance (AUC > 0. 9).
Human-level Protein Localization with Convolutional Neural Networks
We present the largest comparison of CNN architectures including GapNet-PL for protein localization in HTI images of human cells.
