An Introduction to Deep
Convolutional Neural Nets for
Computer Vision #Chapter2 @Machine_learn

Multi-Instance Multi-Stage
Deep Learning for Medical
Image Recognition #Chapter4 @Machine_learn

Automatic Interpretation of
Carotid Intima–Media
Thickness Videos Using
Convolutional Neural
Networks #Chapter5 @Machine_learn

Deep Cascaded Networks for
Sparsely Distributed Object
Detection from Medical
Images #Chapter6 @Machine_learn

Deep Voting and Structured
Regression for Microscopy
Image Analysis #Chapter7 @Machine_learn

Deep Learning Tissue
Segmentation in Cardiac
Histopathology Images #Chapter8 @Machine_learn

Deformable MR Prostate
Segmentation via Deep
Feature Learning and Sparse
Patch Matching #Chapter9 @Machine_learn

Scalable High
Performance Image
Registration Framework
by Unsupervised Deep
Feature Representations
Learning #Chapter11 @Machine_learn

Chest Radiograph
Pathology Categorization
via Transfer Learning #Chapter13 @Machine_learn

#Chapter 3, 10, 12 ? @Raminmousa

Characterization of Errors in Deep Learning-Based Brain MRI Segmentation #Chapter10 @Machine_learn

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
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

📹 VRT: A Video Restoration Transformer

Github: https://github.com/jingyunliang/vrt

Paper: https://arxiv.org/abs/2201.12288

Dataset: https://paperswithcode.com/dataset/gopro

@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

[CVPR 2022] PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision (Oral)

https://github.com/Garfield-kh/PoseTriplet

@Machine_lean

📍 Perturbation Augmentation for Fairer NLP

Responsible NLP projects from Meta AI.

Github: https://github.com/facebookresearch/responsiblenlp

Paper: https://arxiv.org/abs/2205.12586v1

Dataset: https://paperswithcode.com/dataset/glue

@Machine_learn

Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer’s Disease #Chapter15
@Machine_learn