deeplstm

6 ms·

1.
by deeplstm 6mo ago
it’s not always wrong. some of it is wrong. the trick is figuring out what’s actually correct
2.
by deeplstm 6mo ago
humans are still very needed to verify AI outputs
3.
by deeplstm 6mo ago
it was kind of a hard part, but not the hardest
4.
AI writes code 100x faster – why hasn't productivity?(deeptils.github.io)
2 points by deeplstm 6mo ago|8 comments
5.
Tiny Titans: Can Smaller LLMs Punch Above Their Weight?(arxiv.org)
1 points by deeplstm 3y ago|0 comments
6.
by deeplstm 3y ago
I don't think OpenAI and Google have no moat. The biggest moat they have is hardware-software integration, which allows them to serve the models at a very cheap price. My detailed thoughts in a video format https://youtu.be&
7.
by deeplstm 3y ago
* 1 point by deeplstm 0 minutes ago | prev | next | edit | delete [–] I don't think OpenAI and Google have no moat. The biggest moat they have is hardware-software integration, which allows them to serve the models at a very cheap pric
8.
by deeplstm 3y ago
I don't think OpenAI and Google have no moat. The biggest moat they have is hardware-software integration, which allows them to serve the models at a very cheap price. My detailed thoughts in a video format https://youtu.be
9.
by deeplstm 3y ago
Here's the video summary https://youtu.be/uPV9Gk3IC-g
10.
by deeplstm 4y ago
Blog: https://crfm.stanford.edu/2023/03/13/alpaca.html Demo: https://alpaca-ai0.ngrok.io/ Video summary: https://youtu.be/6qdzsDSduww Text summary Alpaca is a solution to NLP
11.
Wav2CLIP: Connecting Text, Images, and Audio [video](youtube.com)
2 points by deeplstm 5y ago|1 comments
12.
by deeplstm 5y ago
paper Wav2CLIP: Learning Robust Audio Representations From CLIP https://arxiv.org/abs/2110.11499
13.
16x smaller than GPT3 but better [video](youtube.com)
3 points by deeplstm 5y ago|1 comments
14.
by deeplstm 5y ago
Paper https://arxiv.org/abs/2110.08207 Code https://github.com/bigscience-workshop/promptsource/
15.
by deeplstm 5y ago
paper https://arxiv.org/abs/2110.00560?context=cs
16.
Leveraging Free Data to Improve Punctuation Model [video](youtube.com)
2 points by deeplstm 5y ago|1 comments
17.
BART: Denoising Seq2Seq Pre-training for NLG (explained)(youtube.com)
1 points by deeplstm 5y ago|1 comments
18.
by deeplstm 5y ago
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension https://arxiv.org/abs/1910.13461 (paper) Code (Facebook) https://github.com/pytorch&#x2F
19.
VideoCLIP: Contrastive Pre-Training ForZero-Shot Video-Text Understanding(youtu.be)
1 points by deeplstm 5y ago|0 comments
20.
Teach Computers to Understand Videos and Text – VideoClip(youtu.be)
1 points by deeplstm 5y ago|0 comments
21.
Self Training for Better Few Shot Learning (Video Explained)(youtube.com)
3 points by deeplstm 5y ago|0 comments
22.
Shortformer: Better Language Modeling Using Shorter Inputs (Paper Explained)(youtube.com)
3 points by deeplstm 6y ago|1 comments
23.
by deeplstm 6y ago
Modelling long sequences has always been hard for transformer-based models. This paper proposes a super innovative way for the transformer to cache previously processed tokens. And it makes generation 9X faster. This is truly mind-blowing P
24.
by deeplstm 6y ago
This is super impressive!! Those generated images are quite accurate and realistic. Here are some of my thoughts and explanation about how they do use discrete vocabulary to describe an image. https://youtu.be/UfAE-1vdj_E
25.
TLDR – Extreme Summarization of Scientific Documents(youtube.com)
4 points by deeplstm 6y ago|1 comments
26.
by deeplstm 6y ago
There are 300+ deep learning papers published every day. I find it very hard to keep up with. This paper introduces a cool way to summarize papers to extremely short summaries (TLDRs). More interestingly, Semantic Scholar uses the proposed
27.
AI Detects Covid-19 by Listening to Coughs [video](youtube.com)
4 points by deeplstm 6y ago|1 comments
28.
by deeplstm 6y ago
The AI system built by an MIT team can detect it with 97% accuracy. More interestingly, it's able to detect asymptomatic people 100% (sensitivity). The proposed model comprises 4 biomarkers (3 ResNet models and a Poisson mask). Each of
29.
Efficient End to End Entity Linking [video](youtube.com)
4 points by deeplstm 6y ago|1 comments
30.
by deeplstm 6y ago
How to perform full end-to-end entity linking has always been a challenging problem in NLP. The typical approach for this is to use a model to detect entities and then employ another model to perform entity disambiguation. And this paper be

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