Welcome to BASIRA channel! Welcome researchers, learners, brainers, and all curious minds!

BASIRA (Brain And SIgnal Research & Analysis) aims to design novel AI technologies and methods across various application fields including neuroscience and computer vision.

FAQ: https://basira-lab.com/
GitHub: https://github.com/basiralab

[Paper Journey] Part 4 — Paper Submission, Response to Reviews and Rebuttal | MICCAI Case study
22:37
[Paper Journey] Part 4 — Paper Submission, Response to Reviews and Rebuttal | MICCAI Case study
[Paper Journey] Part 3 — Writing and Revision Techniques: Do's and Don'ts
44:50
[Paper Journey] Part 3 — Writing and Revision Techniques: Do's and Don'ts
[Paper Journey] Part 2 — The 4 principles of a compelling narrative
12:23
[Paper Journey] Part 2 — The 4 principles of a compelling narrative
[Paper Journey] Part 1 — Mastering Research Writing & Publication: From Draft to Journal Submission
1:00:39
[Paper Journey] Part 1 — Mastering Research Writing & Publication: From Draft to Journal Submission
Topology-aware Graph Neural Network | Distinguished* MSc | **Oral Presentation** | GRAIL MICCAI 2024
6:47
Topology-aware Graph Neural Network | Distinguished* MSc | **Oral Presentation** | GRAIL MICCAI 2024
Topology-Preserving GNNs for Brain Graph Super-Resolution | Distinguished* MSc | PRIME MICCAI 2024
7:26
Topology-Preserving GNNs for Brain Graph Super-Resolution | Distinguished* MSc | PRIME MICCAI 2024
Generative Hypergraph Neural Networks for Data Fusion | **Oral Presentation** | PRIME-MICCAI 2024
7:53
Generative Hypergraph Neural Networks for Data Fusion | **Oral Presentation** | PRIME-MICCAI 2024
Diversity-Based Hyperedge Sampling in GNN Learning | MICCAI MLMI 2024
9:44
Diversity-Based Hyperedge Sampling in GNN Learning | MICCAI MLMI 2024
A Universal Federation of a Mixture of Highly Heterogeneous Learning Tasks | MICCAI MLMI 2024
7:58
A Universal Federation of a Mixture of Highly Heterogeneous Learning Tasks | MICCAI MLMI 2024
[RISE-FIFAI Summer School 2024] Federated Learning Lab Session
56:35
[RISE-FIFAI Summer School 2024] Federated Learning Lab Session
[Deep Graph Learning] 6.7 Evaluation measures for generative GNNs
8:45
[Deep Graph Learning] 6.7 Evaluation measures for generative GNNs
[Deep Graph Learning] 6.6 Generative Graph U-Net
32:47
[Deep Graph Learning] 6.6 Generative Graph U-Net
[Deep Graph Learning] 6.5 Supervised conditional generation on graphs
9:30
[Deep Graph Learning] 6.5 Supervised conditional generation on graphs
[Deep Graph Learning] 6.4 Unconditional one-shot graph generation
35:55
[Deep Graph Learning] 6.4 Unconditional one-shot graph generation
[Deep Graph Learning] 6.3 Unconditional sequential graph generation
12:28
[Deep Graph Learning] 6.3 Unconditional sequential graph generation
[Deep Graph Learning] 6.2 Self-supervised/unsupervised generative GNNs
20:03
[Deep Graph Learning] 6.2 Self-supervised/unsupervised generative GNNs
[Deep Graph Learning] 6.1 Overview of supervised generative GNNs
33:21
[Deep Graph Learning] 6.1 Overview of supervised generative GNNs
[Deep Graph Learning] 5.4 Graph Isomorphism Network Expressive nets
20:54
[Deep Graph Learning] 5.4 Graph Isomorphism Network Expressive nets
[Deep Graph Learning] 5.3 GNN expressiveness
37:44
[Deep Graph Learning] 5.3 GNN expressiveness
[Deep Graph Learning] 5.2 Node permutation equivariance in GNNs
13:38
[Deep Graph Learning] 5.2 Node permutation equivariance in GNNs
[Deep Graph Learning] 5.1 Node permutation invariance in GNNs
33:53
[Deep Graph Learning] 5.1 Node permutation invariance in GNNs
[Deep Graph Learning] 4.6 GNN inductive vs transductive learning
20:46
[Deep Graph Learning] 4.6 GNN inductive vs transductive learning
[Deep Graph Learning] 4.5 Generalized GNN layer and Dropout
21:35
[Deep Graph Learning] 4.5 Generalized GNN layer and Dropout
[Deep Graph Learning] 4.4 GNN batch normalization layer
16:36
[Deep Graph Learning] 4.4 GNN batch normalization layer
[Deep Graph Learning] 4.3 Recap on GNN sampling methods
6:45
[Deep Graph Learning] 4.3 Recap on GNN sampling methods
[Deep Graph Learning] 4.2 Batching and GNN sampling methods
49:33
[Deep Graph Learning] 4.2 Batching and GNN sampling methods
[Deep Graph Learning] 4.1 Point, batch and mini-batch gradient descent
15:29
[Deep Graph Learning] 4.1 Point, batch and mini-batch gradient descent
[Deep Graph Learning] 3.5 Global and local aggregation methods
19:03
[Deep Graph Learning] 3.5 Global and local aggregation methods
[Deep Graph Learning] 3.4 GCN layer operations
12:35
[Deep Graph Learning] 3.4 GCN layer operations
[Deep Graph Learning] 3.3 Graph pooling & embedding aggregation
22:24
[Deep Graph Learning] 3.3 Graph pooling & embedding aggregation