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Hi! Thank you for releasing the code! This is a very interesting piece of work. Congratsssss on the NeurIPS acceptance! 🎉
i met some problem when i use your code.
when directly run the demo.ipynb …
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I am running this code on:
anaconda 3
Ubuntu 18.04
I start by creating and activate a conda environment `densepose` and under this environment I install caffe2. I pass all test until `make…
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- https://arxiv.org/abs/2005.10636
- 2020
ここでは、診断フィードバック（コンパイラのエラーメッセージなど）からプログラムを修復することを学習する問題を考える。
プログラムの修復には2つの理由がある。
第一に、ソースコードと診断フィードバックの間でシンボルを推論し追跡する必要がある。
第二に、プログラム修復に利用できるラベル付きデータセットは比…
e4exp updated
3 years ago
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Dear developers,
I have been trying to use causal learn models with fMRI data. I used both the Tetrad implementation (using pyTetrad and JPype) and the causal-learn implementations.
What surpris…
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alternatively, we could could add more gender-based panels (like [this one](https://query.wikidata.org/#%0ASELECT%20%3Fcount%20%3Fgender%20%3FgenderLabel%20%0AWITH%20%7B%0A%20%20SELECT%20(COUNT(%3Faut…
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Dear Team,
Thank you for the amazing work. I was wondering if it was possible to release the dataset and the model checkpoints for the TOPA paper.
Thank you again for the amazing work.
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Hello, I saw a paragraph in the paper that simply stated that the attention operation was omitted in the encoder module, so the encoder only consists of FFN layers.
Here, we omit the attention mech…
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Hi, thank you for the great work! I have a few concerns after reading the paper:
1. There seem to be many similar problems in the test set, which raises a question: isn’t it problematic to use data…
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It could be very useful to have a (potentially differentiable) marching cubes implementation for projects that work with implicit shape representations. Is this something that there would be interest …
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Hi,
In an effort to implement a new generative model backbone in Dingo, I tried to run the toy example in a fresh environment to take it from there. The data generation and training stages run with…