“ what is the factorization of the Polynomial graph below? (Assume it has no constant factor) write each factor as a polynomial in descending order”
I’ve been tryna solve it forever lol. I know the roots are 1,3,5 which would make it (x-1)(x-3)(x-5) but Im kinda lost from here. Idk what to do with this info I tried these 2 answers and they were wrong
(x-1)(x-3)(x-5)
x3 - 9x2 + 23x+ 15
Processing img 4wkeak6yx6s41...
Link: https://github.com/benedekrozemberczki/awesome-graph-classification
The repository covers techniques such as deep learning, graph kernels, statistical fingerprints and factorization. I monthly update it with new papers when something comes out with code.
I remember hearing that dimension reduction algorithms can be either classified as "matrix factorization" (e.g. PCA) or "neighborhood graph methods" (e.g. tsne, umap).
(https://youtu.be/9iol3Lk6kyU @1:55)
I can understand why umap is considered as a graph based method - could someone please provide some intuition as to why tsne is considered a "graph" method (https://youtu.be/9iol3Lk6kyU @2:55)? As far as I understand, in tsne cross-entropy is minimzed between the original data and the lower dimensional embeddings in gaussian space. Where is tsne using a graph?
https://preview.redd.it/gicg4kh0zie21.png?width=580&format=png&auto=webp&s=f11f7754aa87a82c6db2594ea295c3ec518f4ba2
Paper: https://arxiv.org/abs/1901.09590
PyTorch Code: https://github.com/ibalazevic/TuckER
Key contributions:
- proposing TuckER, a relatively simple linear model for link prediction in knowledge graphs that achieves state-of-the-art results across all standard datasets;
- proving that TuckER is fully expressive and deriving a bound on the entity and relation embedding dimensionality for full expressiveness which is several orders of magnitude lower than the bound of previous state-of-the-art models ComplEx and SimplE; and
- showing that TuckER subsumes several previously proposed tensor factorization approaches to link prediction, i.e. that RESCAL, DistMult, ComplEx and SimplE are all special cases of our model.
Abstract:
Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively simple but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. TuckER outperforms all previous state-of-the-art models across standard link prediction datasets. We prove that TuckER is a fully expressive model, deriving the bound on its entity and relation embedding dimensionality for full expressiveness which is several orders of magnitude smaller than the bound of previous state-of-the-art models ComplEx and SimplE. We further show that several previously introduced linear models can be viewed as special cases of TuckER.
Right now other sources say btc costs 39265€ - price graph is going down. In crypto.com app it shows around 39750€ graph going upwards. Is the crypto.com app from the future? Is this spread? It feels misleading to me but maybe I am just uneducated.
If I wanted to compare energy levels to more than one factor at a time, such as both appointments and food factors, is that possible?
Said paper graph was made by a computer program and it was long like a scroll. I honestly don't remember the topic but it may have been climate chance or economics. I'm losing my mind a little trying to search through my history because I'm sure this video was linked in a comment chain and I didn't save it. Any help would be appreciated. (I sure hope I flared this post correctly)
https://preview.redd.it/w64s1gtvfga31.png?width=400&format=png&auto=webp&s=e7d4e79db8e3e13c6d39737b0d1b8db0c33f22d4
Link: https://github.com/benedekrozemberczki/awesome-graph-classification
The repository covers techniques such as deep learning, graph kernels, statistical fingerprints and factorization. I monthly update it with new papers when something comes out with code.
https://preview.redd.it/wh5blu1v8ah31.png?width=400&format=png&auto=webp&s=1119b9509b544e7b5078acd203f0c8842fab9cec
Link: https://github.com/benedekrozemberczki/awesome-graph-classification
The repository covers techniques such as deep learning, graph kernels, statistical fingerprints and factorization. I monthly update it with new papers when something comes out with code.
https://preview.redd.it/fj6royma9ub31.png?width=512&format=png&auto=webp&s=a316514d05174e00dadd69f8bf2cc4d106ce7702
Link: https://github.com/benedekrozemberczki/awesome-community-detection
The repository covers techniques such as deep learning, spectral clustering, edge cuts, factorization. I monthly update it with new papers when something comes out with code.
https://preview.redd.it/rcz5xao0csl31.png?width=512&format=png&auto=webp&s=d8774ef921aea6fb55831a68b608183c2cbf5c9c
Link: https://github.com/benedekrozemberczki/awesome-community-detection
The repository covers techniques such as deep learning, spectral clustering, edge cuts, factorization. I monthly update it with new papers when something comes out with code.
https://preview.redd.it/ma2mjrcwbf541.png?width=512&format=png&auto=webp&s=fb6fbe81524a5d9529a6034de2316af7411e5846
Link: https://github.com/benedekrozemberczki/awesome-community-detection
The repository covers techniques such as deep learning, spectral clustering, edge cuts, factorization. I monthly update it with new papers when something comes out with code.
https://preview.redd.it/am0otw9d4th31.png?width=512&format=png&auto=webp&s=b078b78d7e7de2a1770e2e660c04f3bf2421e791
Link: https://github.com/benedekrozemberczki/awesome-community-detection
The repository covers techniques such as deep learning, spectral clustering, edge cuts, factorization. I monthly update it with new papers when something comes out with code.
https://preview.redd.it/rchwbth30ln31.png?width=400&format=png&auto=webp&s=69db9d2e6d60efab85dc4c1f0af889db26e6a0b1
Link: https://github.com/benedekrozemberczki/awesome-graph-classification
The repository covers techniques such as deep learning, graph kernels, statistical fingerprints and factorization. I monthly update it with new papers when something comes out with code.
https://preview.redd.it/q489rgvlrin21.png?width=800&format=png&auto=webp&s=354860de9495b1237e721b51be4a4bd1da8b21b8
I curated this list and maintain it on a monthly basis. I try to include the best venues, but also promising new papers.
https://github.com/benedekrozemberczki/awesome-graph-embedding
https://preview.redd.it/etaagn6oisl31.png?width=512&format=png&auto=webp&s=1f5f6e10e20175e035815ba2c8cfa5c58a103fe0
Link: https://github.com/benedekrozemberczki/awesome-community-detection
The repository covers techniques such as deep learning, spectral clustering, edge cuts, factorization. I monthly update it with new papers when something comes out with code.
https://preview.redd.it/4iq63yxa7th31.png?width=512&format=png&auto=webp&s=5257f26bda5611d8ffce0ba5f672d95241d0e820
Link: https://github.com/benedekrozemberczki/awesome-community-detection
The repository covers techniques such as deep learning, spectral clustering, edge cuts, factorization. I monthly update it with new papers when something comes out with code.
https://preview.redd.it/tvbd8fxlc9h31.png?width=400&format=png&auto=webp&s=9e9e245f85de8333a4deab9adf4dea2ba7717241
Link: https://github.com/benedekrozemberczki/awesome-graph-classification
The repository covers techniques such as deep learning, graph kernels, statistical fingerprints and factorization. I monthly update it with new papers when something comes out with code.
https://preview.redd.it/2qpep1v14qk21.png?width=800&format=png&auto=webp&s=ea22c2d73e77cd27c122a43fbb14c1e1186e1ad1
I curated this list and maintain it on a monthly basis. Try to include the best venues, but also promising new papers.
https://github.com/benedekrozemberczki/awesome-graph-embedding
https://preview.redd.it/y6fj7jm8vin21.png?width=800&format=png&auto=webp&s=b86fd6760d8cb1d17e13dfb650671c8ab82dc61a
I curated this list and maintain it on a monthly basis. I try to include the best venues, but also promising new papers.
https://github.com/benedekrozemberczki/awesome-graph-embedding
