subho406

4 ms·

1.
AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning(openreview.net)
1 points by subho406 2y ago|0 comments
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Recurrent Linear Transformers(arxiv.org)
1 points by subho406 3y ago|0 comments
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Don’t Panic Reinforcement learning is full of magical things(medium.com)
5 points by subho406 3y ago|0 comments
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Pytorch2Jax(github.com)
2 points by subho406 3y ago|0 comments
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Multi-page Document Representations using multi-modal multi-task pre-training(arxiv.org)
1 points by subho406 6y ago|0 comments
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OmniNet: A unified architecture for multi-modal multi-task learning(arxiv.org)
2 points by subho406 7y ago|0 comments
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by subho406 7y ago
The model was trained on video classification, image qa and image captioning. Video captioning and video qa is not trained, yet the model shows results on those tasks.
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by subho406 7y ago
As an author of this paper, I feel such neural networks are indeed a small step towards what we call AGI. By learning shared representations across a variety of tasks makes an AI system more robust to real-world datasets and makes it easy t
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by subho406 7y ago
That's a really interesting idea! Giving such neural networks access to more and more modalities helps in useful transfer of information across the various nodes of the network, making it capable of performing zero-shot learning on nev
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by subho406 7y ago
Full source code available at: http://github.com/subho406
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OmniNet: A unified architecture for multi-modal multi-task learning(arxiv.org)
14 points by subho406 7y ago|3 comments
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Text normalization using memory augmented neural networks (Published version)(authors.elsevier.com)
1 points by subho406 8y ago|0 comments
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by subho406 8y ago
Thank you for the review! We will surely correct these mistakes before submitting for final publication. The memory requirements of DNC is quite high. We used GTX 1060 for training. Increasing the context window anything more than 3 increas
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by subho406 8y ago
I'm sorry for the misunderstanding. The reason we added the sentence because the model used in the competition was also based on DNC. But, changes were made when writing the paper, for instance, we did not use any attention mechanism a
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by subho406 8y ago
The demo code used for test results in the paper are available in https://github.com/cognibit/Text-Normalization-Demo . The model implementation is located in src/lib/seq2seq.py. We did not code the DNC cell f
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by subho406 8y ago
The model specifications used for the Kaggle competition was a lot different than the one mentioned in the paper. The paper compares on the same test set used by https://arxiv.org/abs/1611.00068 . DNC showed significant
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by subho406 8y ago
Hi, we tried using a single model for the entire seq-to-seq task but the number of examples in PLAIN is huge which causes the model to perform worse on other classes. The reason we used XGBoost was to separate the two very different tasks (
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Text Normalization using Memory Augmented Neural Networks(arxiv.org)
152 points by subho406 8y ago|16 comments
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TF Speech Recognition Challenge Solution(github.com)
1 points by subho406 9y ago|0 comments
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Seq2Seq and Attention Mechanism from Scratch Using Tensorflow(github.com)
2 points by subho406 9y ago|0 comments
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Show HN: Predicting Housing Prices Using Various Regression Techniques(github.com)
1 points by subho406 10y ago|0 comments