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I have now seen several classification tasks where a logistic regression model with bag-of-character n-grams tied or even beat neural (MTL, LSTM, CNN+attention) models. Any similar observations?
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Check out Meena, a new state-of-the-art open-domain conversational agent, released along with a new evaluation metric, the Sensibleness and Specificity Average, which captures basic, but important attributes for normal conversation. Learn more below!https://goo.gle/36zB8Wj
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When I see someone succeed, it inspires me, it makes me happy, it makes me proud to be human. Envy, derision are human inclinations too, but in the long-run they are counter-productive. Enjoying the success of others is the easiest way to be happy, second only to Baby Yoda pics.
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Google Dataset Search is now officially out of beta. "Dataset Search has indexed almost 25 million of these datasets, giving you a single place to search for datasets & find links to where the data is." Nice work, Natasha Noy and everyone else involved!https://blog.google/products/search/discovering-millions-datasets-web/ …
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NLP community: Interpreting text models with Captum – an open source, extensible library for model interpretability built on PyTorch. Sentiment Analysis and interpreting BERT Models in the tutorials. https://captum.ai/tutorials/IMDB_TorchText_Interpret …pic.twitter.com/o0xbPPEsFb
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v1.4: customizable mobile builds, Distributed Model Parallelism via experimental RPC API, Java Bindings, Chaining LRSchedulers Summary: https://pytorch.org/blog/pytorch-1-dot-4-released-and-domain-libraries-updated/ … Release Notes: https://github.com/pytorch/pytorch/releases/tag/v1.4.0 … Last release for Python 2 (bye bye!)
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I often meet research scientists interested in open-sourcing their code/research and asking for advice. Here is a thread for you. First: why should you open-source models along with your paper? Because science is a virtuous circle of knowledge sharing not a zero-sum competitionpic.twitter.com/x16jgKmLFr
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I believe the goal of a research field should be to stop being primarily a research field, and instead move into the real world. Become an industry. Deep learning has achieved just that: academic research now represents less than 10-15% of users of deep learning.
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Embarrassing for all of CS that the
@TheOfficialACM is involved in this horseshit.https://newsroom.publishers.org/researchers-and-publishers-oppose-immediate-free-distribution-of-peer-reviewed-journal-articles …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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What ngrams most predict outcome Y controlling for confounds C? Blog post & python package.
#CausalInference for text via feature selection & adversarial learning. Predict Y from C then predict Y from Y_hat + text. Pull features from trained model weights.http://ai.stanford.edu/blog/text-causal-inference/ …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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I've been thinking about the software stack for machine learning. Tools I'd love to see. 1. Pip for pretrained models. 2. Version control for datasets. 3. GPU-friendly CI. Travis CI, Circe CI don't support GPUs. Jenkins is a pain. 4. Fast dataframes. Why is Pandas so slow?
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