Using Nucleus and TensorFlow for DNA Sequencing Error Correction
This is a new #tutorial of the Genomics team in #GoogleBrain. Good place to start with #deeplearning for #genomics using Nucleus and #Tensorflow.
Link: https://medium.com/tensorflow/using-nucleus-and-tensorflow-for-dna-sequencing-error-correction-47f3f7fc1a50
Google colab: https://colab.research.google.com/github/google/nucleus/blob/master/nucleus/examples/dna_sequencing_error_correction.ipynb
#Google #healthcare
This is a new #tutorial of the Genomics team in #GoogleBrain. Good place to start with #deeplearning for #genomics using Nucleus and #Tensorflow.
Link: https://medium.com/tensorflow/using-nucleus-and-tensorflow-for-dna-sequencing-error-correction-47f3f7fc1a50
Google colab: https://colab.research.google.com/github/google/nucleus/blob/master/nucleus/examples/dna_sequencing_error_correction.ipynb
#Google #healthcare
Medium
Using Nucleus and TensorFlow for DNA Sequencing Error Correction
Posted by Gunjan Baid, Helen Li, and Pi-Chuan Chang
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Researchers at Google Brain and Carnegie Mellon introduce #XLNet, a pre-training algorithm for natural language processing systems. It helps NLP models (in this case, based on Transformer-XL) achieve state-of-the-art results in 18 diverse language-understanding tasks including question answering and sentiment analysis.
Article: https://towardsdatascience.com/what-is-xlnet-and-why-it-outperforms-bert-8d8fce710335
ArXiV: https://arxiv.org/pdf/1906.08237.pdf
#Google #GoogleBrain #CMU #NLP #SOTA #DL
Researchers at Google Brain and Carnegie Mellon introduce #XLNet, a pre-training algorithm for natural language processing systems. It helps NLP models (in this case, based on Transformer-XL) achieve state-of-the-art results in 18 diverse language-understanding tasks including question answering and sentiment analysis.
Article: https://towardsdatascience.com/what-is-xlnet-and-why-it-outperforms-bert-8d8fce710335
ArXiV: https://arxiv.org/pdf/1906.08237.pdf
#Google #GoogleBrain #CMU #NLP #SOTA #DL
Dream to Control: Learning Behaviors by Latent Imagination
Abstract: Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs are becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model. On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.
Dreamer learns long-horizon behaviors from images purely by latent imagination. For this, it backpropagates value estimates through trajectories imagined in the compact latent space of a learned world model. Dreamer solves visual control tasks using substantially fewer episodes than strong model-free agents.
Dreamer learns a world model from past experiences that can predict the future. It then learns action and value models in its compact latent space. The value model optimizes Bellman's consistency of imagined trajectories. The action model maximizes value estimates by propagating their analytic gradients back through imagined trajectories. When interacting with the environment, it simply executes the action model.
paper: https://arxiv.org/abs/1912.01603
github: https://github.com/google-research/dreamer
site: https://danijar.com/dreamer
#RL #Dreams #Imagination #DL #GoogleBrain #DeepMind
Abstract: Learned world models summarize an agent's experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs are becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model. On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.
Dreamer learns long-horizon behaviors from images purely by latent imagination. For this, it backpropagates value estimates through trajectories imagined in the compact latent space of a learned world model. Dreamer solves visual control tasks using substantially fewer episodes than strong model-free agents.
Dreamer learns a world model from past experiences that can predict the future. It then learns action and value models in its compact latent space. The value model optimizes Bellman's consistency of imagined trajectories. The action model maximizes value estimates by propagating their analytic gradients back through imagined trajectories. When interacting with the environment, it simply executes the action model.
paper: https://arxiv.org/abs/1912.01603
github: https://github.com/google-research/dreamer
site: https://danijar.com/dreamer
#RL #Dreams #Imagination #DL #GoogleBrain #DeepMind