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Prikvačeni tweet
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: https://arxiv.org/pdf/1910.14613.pdf … 1/4pic.twitter.com/BjptMZBHvm
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Arvind Neelakantan proslijedio/la je Tweet
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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Arvind Neelakantan proslijedio/la je Tweet
FixMatch: focusing on simplicity for semi-supervised learning and improving state of the art (CIFAR 94.9% with 250 labels, 88.6% with 40). https://arxiv.org/abs/2001.07685 Collaboration with Kihyuk Sohn,
@chunliang_tw@ZizhaoZhang Nicholas Carlini@ekindogus@Han_Zhang_@colinraffelpic.twitter.com/BmeYvpEHzX
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Check out our spotlight talk and poster describing the Neural Assistant work in the ConvAI workshop tomorrow
@NeurIPSConf#neurips19 http://alborz-geramifard.com/workshops/neurips19-Conversational-AI/Main.html … https://twitter.com/arvind_io/status/1190098953532059650 …pic.twitter.com/lq1tDJfOkn
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Arvind Neelakantan proslijedio/la je Tweet
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!
http://tiny.cc/t5-colab
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Arvind Neelakantan proslijedio/la je Tweet
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 https://arxiv.org/abs/1905.09272v2 …pic.twitter.com/cciL5Db73x
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Arvind Neelakantan proslijedio/la je Tweet
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: https://arxiv.org/abs/1911.04252 pic.twitter.com/0umSnX7wui
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Arvind Neelakantan proslijedio/la je Tweet
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.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Implementation of Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning:https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/neural_assistant.py …
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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.pic.twitter.com/REdOXsnMYT
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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.pic.twitter.com/TjFvXO9e4G
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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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Arvind Neelakantan proslijedio/la je Tweet
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: https://arxiv.org/abs/1910.10683 Code/models/data/etc: https://git.io/Je0cZ Summary
(1/14)pic.twitter.com/VP1nkkHefB
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Paper updated with experiments on image generation. https://twitter.com/arvind_io/status/1138663697591652352 …pic.twitter.com/oOoF68XE9i
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Arvind Neelakantan proslijedio/la je Tweet
Thanks to
@MRQA2019 for selecting this as the best paper!https://twitter.com/rajarshi_nitc/status/1174434884741410817 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Arvind Neelakantan proslijedio/la je Tweet
Delighted to announce the 2nd Conference on Automated Knowledge Base Construction (AKBC), to be held in Irvine, CA during June 22-24, 2020!
#AKBC#AKBC2020 https://www.akbc.ws/2020/cfp/Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Arvind Neelakantan proslijedio/la je Tweet
GRE no longer required. That took less than 24 hours. You can do it too!https://www.cics.umass.edu/admissions/application-instructions …
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Data: https://ai.google/tools/datasets/taskmaster-1 … Work done with many awesome colleagues at Google Assistant team and
@GoogleAI along with student researcher Chinnadhurai ShankarPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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 : https://arxiv.org/pdf/1909.05358v1.pdf …pic.twitter.com/gTj5ocfdrS
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