deeplstm

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31.
Vokenization Improving Language Understanding [video](youtube.com)
5 points by deeplstm 6y ago|1 comments
32.
by deeplstm 6y ago
It's a super cool paper that invents "vokenization" to generate a large amount of visually-grounded language datasets and trains visually-grounded models on those. Most language models are trained on pure text data. Although
33.
Deep Bidirectional Transformers for Language Understanding [video](youtube.com)
6 points by deeplstm 6y ago|1 comments
34.
by deeplstm 6y ago
This video explains a legendary paper, BERT. It leverages the Transformer encoder and comes up with an innovative way to pre-training language models (masked language modeling). BERT has a significant influence on how people approach NLP pr
35.
by deeplstm 6y ago
I did skip many details in this video as Transformer architecture is itself a big topic. I would suggest breaking down this paper into 2 components. 1. Transformer 2. How to apply the Transformer to image data As for the first one, I made
36.
by deeplstm 6y ago
good to know!
37.
by deeplstm 6y ago
training on language and visual cue at the time is indeed the next important milestone to achieve.
38.
by deeplstm 6y ago
Thanks for watching! I am glad you find them helpful. And your written summary looks nice!
39.
by deeplstm 6y ago
thanks for adding it!
40.
Transformers for Image Recognition at Scale [video](youtu.be)
43 points by deeplstm 6y ago|11 comments
41.
by deeplstm 6y ago
I spent the weekend reading this super interesting paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale", and made this video to explain it a bit. I hope it can be helpful for those who're also in
42.
Improving Transformer Models by Reordering Their Sublayers(youtu.be)
5 points by deeplstm 6y ago|0 comments
43.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks(youtu.be)
4 points by deeplstm 6y ago|0 comments
44.
Well Read Students Learn Better: On the Important of Pre-Training Compact Models(youtu.be)
6 points by deeplstm 6y ago|0 comments
45.
Why Virtual Small Talk Is Intimidating(youtu.be)
1 points by deeplstm 6y ago|0 comments
46.
Transformer Architecture Explained(youtu.be)
1 points by deeplstm 6y ago|0 comments
47.
Question and Answer Test-Train Overlap in Open Domain Question Answering Data(youtu.be)
3 points by deeplstm 6y ago|0 comments
48.
LinkedIn's New Ranking Model – DeText: A Deep Text Ranking Framework with Bert(youtu.be)
6 points by deeplstm 6y ago|0 comments
49.
QpenQA State-of-the-Art – Realm: Retrieval-Augmented Language Model Pre-Training(youtu.be)
3 points by deeplstm 6y ago|1 comments
50.
by deeplstm 6y ago
https://news.ycombinator.com/submit
51.
GAN Bert: Generative Adversarial Learning for Text Classification (Explained)(youtu.be)
3 points by deeplstm 6y ago|0 comments
52.
Pre-Training Is (Almost) All You Need: An Application to Commonsense Reasoning(youtu.be)
2 points by deeplstm 6y ago|0 comments
53.
Quantifying Attention Flow in Transformers (Explained)(youtu.be)
4 points by deeplstm 6y ago|0 comments
54.
Revealing Dark Secrets of Bert (Analysis of BERT's Attention Heads) Explained(youtu.be)
2 points by deeplstm 6y ago|0 comments
55.
Distilling Task Specific Knowledge from Bert into Simple Neural Networks(youtu.be)
3 points by deeplstm 7y ago|0 comments
56.
Electra Pre-training Text Encoders as Discriminators (paper explained)(youtu.be)
3 points by deeplstm 7y ago|0 comments
57.
Molecule Attention Transformer (Deep Learning Paper Explained)(youtu.be)
2 points by deeplstm 7y ago|0 comments
58.
Molecule Attention Transformer
2 points by deeplstm 7y ago|0 comments
59.
by deeplstm 7y ago
Thanks for the reply. I agree there might be a lot of different approach to to symbolic AI. Probabilistic inductive logic programming sounds interesting but I am not familiar with it. Maybe I should check it out later. I am wondering if it
60.
Explainability and Deep Learning(medium.com)
2 points by deeplstm 7y ago|2 comments

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