Machine learning books and papers
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chapter 5.pdf
2.5 MB
Automatic Interpretation of
Carotid Intima–Media
Thickness Videos Using
Convolutional Neural
Networks #Chapter5 @Machine_learn
chapter 6.pdf
1.4 MB
Deep Cascaded Networks for
Sparsely Distributed Object
Detection from Medical
Images #Chapter6 @Machine_learn
👍1
chapter 7.pdf
1 MB
Deep Voting and Structured
Regression for Microscopy
Image Analysis #Chapter7 @Machine_learn
Chapter 8.pdf
2 MB
Deep Learning Tissue
Segmentation in Cardiac
Histopathology Images #Chapter8 @Machine_learn
Chapter 9.pdf
3.8 MB
Deformable MR Prostate
Segmentation via Deep
Feature Learning and Sparse
Patch Matching #Chapter9 @Machine_learn
Chapter 11.pdf
3.1 MB
Scalable High
Performance Image
Registration Framework
by Unsupervised Deep
Feature Representations
Learning #Chapter11 @Machine_learn
Chapter 13.pdf
2.1 MB
Chest Radiograph
Pathology Categorization
via Transfer Learning #Chapter13 @Machine_learn
B978-0-12-810408-8.00013-4.pdf
1.2 MB
Characterization of Errors in Deep Learning-Based Brain MRI Segmentation #Chapter10 @Machine_learn
B978-0-12-810408-8.00019-5.pdf
1.8 MB
Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions1 #Chapter14 @Machine_learn
Weighted Deep Neural Network Ensemble Approach for.pdf
1.4 MB
Weighted Deep Neural Network Ensemble Approach for
Multi-Domain Sentiment Analysis Author: @Raminmousa Doi:https://dx.doi.org/10.22105/jarie.2021.288364.1332 cite: Mousa, Ramin, et al. "Weighted Deep Neural Network Ensemble Approach for Multi-Domain Sentiment Analysis." Journal of Applied Research on Industrial Engineering (2021). link: https://www.researchgate.net/publication/360645256_Weighted_Deep_Neural_Network_Ensemble_Approach_for_Multi-Domain_Sentiment_Analysis @Machine_learn
ConvMixer, an extremely simple model that is similar in spirit to the ViT and the even-more-basic MLP-Mixer

Github: https://github.com/locuslab/convmixer

Paper: https://arxiv.org/pdf/2201.09792v1.pdf

@Machine_learn
📝 Automated Crossword Solving

Pretrained models, precomputed FAISS embeddings, and a crossword clue-answer dataset.

Github: https://github.com/albertkx/berkeley-crossword-solver

Paper: https://arxiv.org/abs/2205.09665v1

Dataset: https://www.xwordinfo.com/JSON/

@Machine_learn
B978-0-12-810408-8.00020-1.pdf
754.9 KB
Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer’s Disease #Chapter15
@Machine_learn
B978-0-12-810408-8.00022-5.pdf
1.5 MB
Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image Synthesis #Chapter16
@Machine_learn
B978-0-12-810408-8.00023-7.pdf
1.7 MB
Natural Language Processing for Large-Scale Medical Image Analysis Using Deep Learning
#Chapter17
@Machine_learn