"1 new messages from ***"

new post on fanfix 😜 and check messages

Me mid lecture reading messages from my man

New features alert! 🚨 You can now edit previously... sent messages.show more

(audio from "Mixed messages" by Tom Cardy) #HazbinHotel #HazbinHotelVox #HazbinHotelValentino

Candace Owens thought the text messages between Tyler Robinson... and his boyfriend were from Discord. When she was informed by the CNN interviewer that they were text messages and not Discord messages Candace responded, "I actually did not read that they were text messages."show more

Love messages from POLARIX to Luminix💌 #POLARIX #XINCHE #PENTOR #HANGYUL

checking my #crypto bags more than messages from my... mom currentlyshow more

Tap (+) in Messages to send or request money... from your friends.show more

🚨More diesel trucks are being spotted with messages from... God 🙏show more

🚨 More diesel trucks are popping up with messages... from God 🙏show more

opening my phone to no new notifications no missed... calls and no unread messagesshow more

🔴 BREAKING: Regime television hacked again, now broadcasting messages... from the Shah.show more

New secret text messages with the Eversource CEO reveal... Lamont's cozy relationship with big utilities.show more

فكرت فيه قبل ما انام ويوم صحيت لقيته مرسل... +4 new messagesshow more

[Graph Convolutional Network] by hand ✍️ Graph Convolutional Networks... (GCNs), introduced by Thomas Kipf and Max Welling in 2017, have emerged as a powerful tool in the analysis and interpretation of data structured as graphs. This exercise demonstrates how GCN works in a simple application: binary classification. -- Goal -- Predict if a node in a graph is X. -- Architecture -- 🟪 Graph Convolutional Network (GCN) 1. GCN1(4,3) 2. GCN2(3,3) 🟦 Fully Connected Network (FCN) 1. Linear1(3,5) 2. ReLU 3. Linear2(5,1) 4. Sigmoid Simplications: • Adjacent matrices are not normalized. • ReLU is applied to messages directly. -- Walkthrough -- [1] Given ↳ A graph with five nodes A, B, C, D, E [2] 🟩 Adjacency Matrix: Neighbors ↳ Add 1 for each edge to neighbors ↳ Repeat in both directions (e.g., A->C, C->A) ↳ Repeat for both GCN layers [3] 🟩 Adjacency Matrix: Self ↳ Add 1's for each self loop ↳ Equivalent to adding the identity matrix ↳ Repeat for both GCN layers [4] 🟪 GCN1: Messages ↳ Multiply the node embeddings 🟨 with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [5] 🟪 GCN1: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The purpose is the pool messages from each node's neighbors as well as from the node itself. ↳ The result is a new feature per node [6] 🟪 GCN1: Visualize ↳ For node 1, visualize how messages are pooled to obtain a new feature for better understanding ↳ [3,0,1] + [1,0,0] = [4,0,1] [7] 🟪 GCN2: Messages ↳ Multiply the node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [8] 🟪 GCN2: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The result is a new feature per node [9] 🟪 GCN2: Visualize ↳ For node 3, visualize how messages are pooled to obtain a new feature for better understanding ↳ [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10] [10] 🟦 FCN: Linear 1 + ReLU ↳ Multiply node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is a new feature per node ↳ Unlike in GCN layers, no messages from other nodes are included. [11] 🟦 FCN: Linear 2 ↳ Multiply node features with weights and biases [12] 🟦 FCN: Sigmoid ↳ Apply the Sigmoid activation function ↳ The purpose is to obtain a probability value for each node ↳ One way to calculate Sigmoid by hand ✍️ is to use the approximation below: • >= 3 → 1 • 0 → 0.5 • <= -3 → 0 -- Outputs -- A: 0 (Very unlikely) B: 1 (Very likely) C: 1 (Very likely) D: 1 (Very likely) E: 0.5 (Neutral)show more

Some messages of support from our Victory family ahead... of tomorrow’s Grand Final 🤝show more