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PyTorch for research, C++ for production?https://twitter.com/elonmusk/status/1224182478501482497 …
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If an algorithm has "mastered natural language", I would expect it to be able to do some of the things language is for -- communicating information, receiving information, acting on the world... Not merely output something that statistically sounds like language.
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How did no one think about that before? "The core idea of our approach is to transform existing, pre-trained word embeddings via semantic differentials to a new "polar" space with interpretable dimensions defined by such polar opposites"https://twitter.com/arXiv_Daily/status/1224139894403952641 …
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Added ImageNet validation results for 164 pretrained
#PyTorch models on several datasets, incl ImageNet-A, ImageNetV2, and Imagenet-Sketch. No surprise, models with exposure to more data do quite well. Without extra, EfficientNets are holding their own.https://github.com/rwightman/pytorch-image-models/tree/master/results …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
kaalam.ai proslijedio/la je TweetHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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kaalam.ai proslijedio/la je Tweet
Got graphs?

In episode 2 of Neural Structured Learning, Software Engineer Arjun Gopalan discusses what natural graphs are and how their data can be used to train neural networks.
Watch now → https://goo.gle/31fxlvX pic.twitter.com/QyW8n7TbxE
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Sentiment analysis is still mostly bullshit, friends.
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Today we announce a novel, open-source method for text generation tasks (e.g., summarization or sentence fusion), which uses edit operations instead of generating text from scratch, leading to less errors and faster model execution. Read about it below.https://goo.gle/38XfRXU
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What we saw in Graph Analytics in 2019https://www.bbvadata.com/what-we-saw-in-graph-analytics-in-2019/ …
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Uncertainty-based Modulation for Lifelong Learning https://deepai.org/publication/uncertainty-based-modulation-for-lifelong-learning … by Andrew Brna et al.
#MachineLearning#FeatureExtractionHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Examining the Benefits of Capsule Neural Networks https://deepai.org/publication/examining-the-benefits-of-capsule-neural-networks … by Arjun Punjabi et al.
#Vector#ConvolutionalNeuralNetworksHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Make sure to check out the KerasTuner implementation for
#rstats, as well!
https://github.com/henry090/kerastuneR …
Vignettes, documentation, and more available on Turgut's Github.pic.twitter.com/wm5GhcdKYZ
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Machine Unlearning “Once users have shared their data online, it is difficult to revoke access and ask for the data to be deleted. ML exacerbates this problem because any model trained with said data may have memorized it, putting users' privacy at risk.” https://arxiv.org/abs/1912.03817 pic.twitter.com/RHxOjbb624
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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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Procedural Content Generation via Reinforcement Learning “A new approach to procedural content generation in games, where level design is framed as a game (as a sequential task problem), and the content generator itself is learned.” https://arxiv.org/abs/2001.09212 pic.twitter.com/2B6P6tlh9d
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In this paper we present a simple yet powerful idea: when using a recurrent AE to perform online lossy compression of a highly temporally correlated signal, one should feedback the state of the decoder to the encoder. We compare FRAE to many natural auto encoder designs.pic.twitter.com/PoW6cA8c1s
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Learning to adapt class-specific features across domains for semantic segmentation https://deepai.org/publication/learning-to-adapt-class-specific-features-across-domains-for-semantic-segmentation … by Mikel Menta et al.
#NeuralNetwork#ComputerVisionHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift https://deepai.org/publication/partially-shared-variational-auto-encoders-for-unsupervised-domain-adaptation-with-target-shift … by Ryuhei Takahashi et al.
#Statistics#EstimatorHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Q-learning is difficult to apply when the number of available actions is large. We show that a simple extension based on amortized stochastic search allows Q-learning to scale to high-dimensional discrete, continuous or hybrid action spaces: https://arxiv.org/abs/2001.08116
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Machine learning is about fitting to a static distribution -- human learning is about gathering knowledge that may turn out to be useful in a future that is guaranteed to share little commonality with the past.
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