Best Paper Awards in Computer Science (since 1996)
A well maintained list: https://lnkd.in/e6_ks3E
#artificialintelligence #machinelearning #papers #research
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
A well maintained list: https://lnkd.in/e6_ks3E
#artificialintelligence #machinelearning #papers #research
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
A new study published in Nature shows we can predicting the genetic disorder directly from the face using deep learning. The network was trained on a dataset of 17,000 patient images representing more than 200 syndromes. The paper reports that model achieves 91% top-10-accuracy in identifying the correct syndrome on 502 images and outperformed expert clinicians in three experiments. The method can be used to diagnose dysmorphology syndromes which typically affect roughly 1 in 30,000 people.
While this work has a great potential to improve discovering rare diseases, insurance companies may use this technology to deny providing medical insurance or increase the policy fees for people with specific genes.
paper: https://lnkd.in/fUEpYRt
#artificialintelligence #syndrome #ai #deeplearning #research
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
While this work has a great potential to improve discovering rare diseases, insurance companies may use this technology to deny providing medical insurance or increase the policy fees for people with specific genes.
paper: https://lnkd.in/fUEpYRt
#artificialintelligence #syndrome #ai #deeplearning #research
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
Top 10 #deeplearning research papers as per this website
https://lnkd.in/dPYayt9
Of course the choice remains biased but we do like these besides a few hundred other papers.
Remember, it is not the popular but the meaningful and industry relevant research that is worth paying attention to.
Here's the list:
1. Universal Language Model Fine-tuning for Text Classification
https://lnkd.in/dhj5SyM
2. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
https://lnkd.in/d44kt3Q
3. Deep Contextualized Word Representations
https://lnkd.in/dkP68Fb
4. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://lnkd.in/dAhYzge
5. Delayed Impact of Fair Machine Learning
https://lnkd.in/dvTvG2s
6. World Models
7. Taskonomy: Disentangling Task Transfer Learning
https://lnkd.in/dYxMjAd
8. Know What You Donβt Know: Unanswerable Questions for SQuAD
https://lnkd.in/d--grME
9. Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://lnkd.in/dY6psf4
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://lnkd.in/dgtnD7n
#machinelearning #research #deeplearning #artificialintelligence
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
https://lnkd.in/dPYayt9
Of course the choice remains biased but we do like these besides a few hundred other papers.
Remember, it is not the popular but the meaningful and industry relevant research that is worth paying attention to.
Here's the list:
1. Universal Language Model Fine-tuning for Text Classification
https://lnkd.in/dhj5SyM
2. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
https://lnkd.in/d44kt3Q
3. Deep Contextualized Word Representations
https://lnkd.in/dkP68Fb
4. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://lnkd.in/dAhYzge
5. Delayed Impact of Fair Machine Learning
https://lnkd.in/dvTvG2s
6. World Models
7. Taskonomy: Disentangling Task Transfer Learning
https://lnkd.in/dYxMjAd
8. Know What You Donβt Know: Unanswerable Questions for SQuAD
https://lnkd.in/d--grME
9. Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://lnkd.in/dY6psf4
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://lnkd.in/dgtnD7n
#machinelearning #research #deeplearning #artificialintelligence
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
The Evolved Transformer
Paper by So et al.: https://lnkd.in/eNZ6ije
#artificalintelligence #MachineLearning #NeuralComputing #EvolutionaryComputing #research
βοΈ @AI_Python
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
Paper by So et al.: https://lnkd.in/eNZ6ije
#artificalintelligence #MachineLearning #NeuralComputing #EvolutionaryComputing #research
βοΈ @AI_Python
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
Our friends in Tampere University are hiring now:
15 fully-funded PhD job openings for 3 years within H2020 A-WEAR European Joint Doctorate
Target audience: fresh MSc graduates in various engineering fields (who have completed their first master no earlier than Fall 2015) and who are passionate about pursuing a PhD in a research field of high relevance to todayβs society (wearable computing & IoT).
Job description: fully funded 36 months PhD positions towards double/joint PhD programs in 5 top European technical universities in Finland, Italy, Spain, Czech Republic, and Romania
Gross salary (approx. in EUR/month): 3600 (FI), 2800 (ES), 2000 (RO), 2400 (CZ), 2900 (IT)
Application deadline: 28th of February 2019
Starting time of the PhD: Fall 2019
https://www.tuni.fi/en
https://lnkd.in/eyDattx
#universities #graduations #phd #funding #research
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
15 fully-funded PhD job openings for 3 years within H2020 A-WEAR European Joint Doctorate
Target audience: fresh MSc graduates in various engineering fields (who have completed their first master no earlier than Fall 2015) and who are passionate about pursuing a PhD in a research field of high relevance to todayβs society (wearable computing & IoT).
Job description: fully funded 36 months PhD positions towards double/joint PhD programs in 5 top European technical universities in Finland, Italy, Spain, Czech Republic, and Romania
Gross salary (approx. in EUR/month): 3600 (FI), 2800 (ES), 2000 (RO), 2400 (CZ), 2900 (IT)
Application deadline: 28th of February 2019
Starting time of the PhD: Fall 2019
https://www.tuni.fi/en
https://lnkd.in/eyDattx
#universities #graduations #phd #funding #research
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv