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  1. Prikvačeni tweet
    31. lis 2019.

    We explore a simple approach to task-oriented dialog. A single neural network consumes conversation history and external knowledge as input and generates the next turn text response along with the action (when necessary) as output. Paper: 1/4

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  2. proslijedio/la je Tweet
    28. sij

    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!

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  3. proslijedio/la je Tweet
    22. sij

    FixMatch: focusing on simplicity for semi-supervised learning and improving state of the art (CIFAR 94.9% with 250 labels, 88.6% with 40). Collaboration with Kihyuk Sohn, Nicholas Carlini

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  4. 13. pro 2019.
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  5. proslijedio/la je Tweet
    9. pro 2019.

    As promised, we have made the Text-To-Text Transfer Transformer (T5) models much easier to fine-tune for new tasks, and we just released a Colab notebook where you can try it yourself on a free TPU! 👇 (1/3)

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  6. proslijedio/la je Tweet
    9. pro 2019.

    Unsupervised pre-training now outperforms supervised learning on ImageNet for any data regime (see figure) and also for transfer learning to Pascal VOC object detection

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  7. proslijedio/la je Tweet
    12. stu 2019.

    Want to improve accuracy and robustness of your model? Use unlabeled data! Our new work uses self-training on unlabeled data to achieve 87.4% top-1 on ImageNet, 1% better than SOTA. Huge gains are seen on harder benchmarks (ImageNet-A, C and P). Link:

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  8. proslijedio/la je Tweet
    6. stu 2019.

    A huge and public thank you to George Dahl, for doing 3 amazing things: 1) For being a master of his craft. 👍🏿 George is a research scientist on the Google Brain team, with a focus on machine perception. He received his PhD from U Toronto, under Geoff Hinton.

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  9. 3. stu 2019.

    Implementation of Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning:

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  10. 31. lis 2019.

    Work done with awesome intern Semih Yavuz and many awesome colleagues

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  11. 31. lis 2019.

    The model is trained at turn-level where the dialog history fed into model as input has previous ground-truth turns of the dialog. In the conversations here the actual text responses generated by model itself are used as the assistant’s side of dialog history to be fed as input.

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  12. 31. lis 2019.

    In our experiments we find that: 1) our model was able to incorporate external knowledge and generate factual text response with weak supervision signal. 2) our model can incorporate medium-size knowledge bases with only 8K training examples over multiple verticals.

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  13. 31. lis 2019.

    Previous approaches rely on a pipeline of individually optimized components containing state tracking, dialog policy/management etc requiring expensive annotations and are hard to scale.

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  14. proslijedio/la je Tweet
    23. lis 2019.

    New paper! We perform a systematic study of transfer learning for NLP using a unified text-to-text model, then push the limits to achieve SoTA on GLUE, SuperGLUE, CNN/DM, and SQuAD. Paper: Code/models/data/etc: Summary ⬇️ (1/14)

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  15. 22. lis 2019.
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  16. proslijedio/la je Tweet
    18. lis 2019.

    Thanks to for selecting this as the best paper!

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  17. proslijedio/la je Tweet
    3. lis 2019.

    Delighted to announce the 2nd Conference on Automated Knowledge Base Construction (AKBC), to be held in Irvine, CA during June 22-24, 2020!

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  18. proslijedio/la je Tweet
    2. lis 2019.

    GRE no longer required. That took less than 24 hours. You can do it too!

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  19. 13. ruj 2019.

    Data: Work done with many awesome colleagues at Google Assistant team and along with student researcher Chinnadhurai Shankar

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  20. 13. ruj 2019.

    We are excited to release Taskmaster-1, a new task-oriented dialog dataset. We explore two methods for data collection, two-person and self-dialogs. Surprisingly self-dialogs are an effective way to collect dialog. Paper accepted to @emnlp2019 :

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