#ArtificialIntelligence in 2020
This video recaps developments in #AI from 2019!
YouTube
🔭 @DeepGravity
This video recaps developments in #AI from 2019!
YouTube
🔭 @DeepGravity
YouTube
Artificial Intelligence in 2020
This video recaps developments in AI from 2019! Happy New Year! Thanks for watching! Please Subscribe! Links from Video Below: The Lottery Ticket Hypothesis:...
Long-Term Visitation Value for Deep Exploration in Sparse Reward #ReinforcementLearning
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Similarly, if the agent receives also rewards that create suboptimal modes of the objective function, it will likely prematurely stop exploring. More recent methods add auxiliary intrinsic rewards to encourage exploration. However, auxiliary rewards lead to a non-stationary target for the Q-function. In this paper, we present a novel approach that (1) plans exploration actions far into the future by using a long-term visitation count, and (2) decouples exploration and exploitation by learning a separate function assessing the exploration value of the actions. Contrary to existing methods which use models of reward and dynamics, our approach is off-policy and model-free. We further propose new tabular environments for benchmarking exploration in reinforcement learning. Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function. Results also suggest that our approach scales gracefully with the size of the environment. Source code is available at https://github.com/sparisi/visit-value-explore
Paper
🔭 @DeepGravity
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where this occurs very rarely the agent learns slowly or cannot learn at all. Similarly, if the agent receives also rewards that create suboptimal modes of the objective function, it will likely prematurely stop exploring. More recent methods add auxiliary intrinsic rewards to encourage exploration. However, auxiliary rewards lead to a non-stationary target for the Q-function. In this paper, we present a novel approach that (1) plans exploration actions far into the future by using a long-term visitation count, and (2) decouples exploration and exploitation by learning a separate function assessing the exploration value of the actions. Contrary to existing methods which use models of reward and dynamics, our approach is off-policy and model-free. We further propose new tabular environments for benchmarking exploration in reinforcement learning. Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function. Results also suggest that our approach scales gracefully with the size of the environment. Source code is available at https://github.com/sparisi/visit-value-explore
Paper
🔭 @DeepGravity
GitHub
GitHub - sparisi/visit-value-explore
Contribute to sparisi/visit-value-explore development by creating an account on GitHub.
Dive into Deep Learning
An interactive #DeepLearning #book with code, math, and discussions, based on the #NumPy interface.
Book
🔭 @DeepGravity
An interactive #DeepLearning #book with code, math, and discussions, based on the #NumPy interface.
Book
🔭 @DeepGravity
Research Fellow / Fellow at Australian National University. Mathematical Sciences Institute and Research School of Computer Science
Lecturer/Senior Lecturer in Computer Science (Industry 4.0 Analytics). Edge Hill University UK
Postdoctoral Researcher in Energy Analytics and Machine Learning at the University of Pennsylvania
Postdoc Computer Science, Computational Biology - Next Generation Sequencing Data Analysis (m/f/d). Genomik und Immunregulation (LIMES) Bonn
Postdoc Fellow in Large Scale Senor Fusion / Intelligent Infrastructure Systems
Post-Doc in "Large Scale Senor Fusion / Intelligent Infrastructure Systems" at TUM
Two postdoctoral positions at the University of Venice, Italy. Artificial Intelligence Unit
Postdoctoral Researcher at ETS Montreal - Deep Learning for Visual Recognition
Neural Network models for language and interactive robots
Machine Learning Research Scientist position at at the NYU School of Medicine
Postdoc Position at Qatar Computing Research Institute (QCRI)
5-Year Fellowships at RISE Cyprus on AI, Communications, Visual Sciences, Human Factors, Design
PhD position: Hybrid process modeling combining mechanistic transport equations with machine learning for thermodynamic equilibria, The Helmholtz School for Data Science in Life, Earth and Energy
Two PhD positions in Deep Probabilistic Programming and protein structure prediction, Copenhagen
PhD Student - Meteorologist, Physicist, Computer Scientist or EngineerInstitut für Geowissenschaften Tübingen
PhD Studentship Artificial Intelligence Enabling Next Generation Synthesis
Staff Scientist/Postdoctoral Scholar, Neural Computation Unit, Okinawa Institute of Science and Technology
FENS-SfN Summer School on Artificial and natural computations for sensory perception: what is the link? (7-13 June 2020, Italy)
Postdoctoral Researcher in Computer Vision and Deep Learning
Research Assistant Artificial Intelligence in Life Science Applications
PhD Studentship in Neural Data Science, Computational Neuromodulation and Metalearning
#Job
🔭 @DeepGravity
Lecturer/Senior Lecturer in Computer Science (Industry 4.0 Analytics). Edge Hill University UK
Postdoctoral Researcher in Energy Analytics and Machine Learning at the University of Pennsylvania
Postdoc Computer Science, Computational Biology - Next Generation Sequencing Data Analysis (m/f/d). Genomik und Immunregulation (LIMES) Bonn
Postdoc Fellow in Large Scale Senor Fusion / Intelligent Infrastructure Systems
Post-Doc in "Large Scale Senor Fusion / Intelligent Infrastructure Systems" at TUM
Two postdoctoral positions at the University of Venice, Italy. Artificial Intelligence Unit
Postdoctoral Researcher at ETS Montreal - Deep Learning for Visual Recognition
Neural Network models for language and interactive robots
Machine Learning Research Scientist position at at the NYU School of Medicine
Postdoc Position at Qatar Computing Research Institute (QCRI)
5-Year Fellowships at RISE Cyprus on AI, Communications, Visual Sciences, Human Factors, Design
PhD position: Hybrid process modeling combining mechanistic transport equations with machine learning for thermodynamic equilibria, The Helmholtz School for Data Science in Life, Earth and Energy
Two PhD positions in Deep Probabilistic Programming and protein structure prediction, Copenhagen
PhD Student - Meteorologist, Physicist, Computer Scientist or EngineerInstitut für Geowissenschaften Tübingen
PhD Studentship Artificial Intelligence Enabling Next Generation Synthesis
Staff Scientist/Postdoctoral Scholar, Neural Computation Unit, Okinawa Institute of Science and Technology
FENS-SfN Summer School on Artificial and natural computations for sensory perception: what is the link? (7-13 June 2020, Italy)
Postdoctoral Researcher in Computer Vision and Deep Learning
Research Assistant Artificial Intelligence in Life Science Applications
PhD Studentship in Neural Data Science, Computational Neuromodulation and Metalearning
#Job
🔭 @DeepGravity
Computational model discovery with #ReinforcementLearning
The motivation of this study is to leverage recent breakthroughs in artificial intelligence research to unlock novel solutions to important scientific problems encountered in computational science. To address the human intelligence limitations in discovering reduced-order models, we propose to supplement human thinking with artificial intelligence. Our three-pronged strategy consists of learning (i) models expressed in analytical form, (ii) which are evaluated a posteriori, and iii) using exclusively integral quantities from the reference solution as prior knowledge. In point (i), we pursue interpretable models expressed symbolically as opposed to black-box neural networks, the latter only being used during learning to efficiently parameterize the large search space of possible models. In point (ii), learned models are dynamically evaluated a posteriori in the computational solver instead of based on a priori information from preprocessed high-fidelity data, thereby accounting for the specificity of the solver at hand such as its numerics. Finally in point (iii), the exploration of new models is solely guided by predefined integral quantities, e.g., averaged quantities of engineering interest in Reynolds-averaged or large-eddy simulations (LES). We use a coupled deep reinforcement learning framework and computational solver to concurrently achieve these objectives. The combination of reinforcement learning with objectives (i), (ii) and (iii) differentiate our work from previous modeling attempts based on machine learning. In this report, we provide a high-level denoscription of the model discovery framework with reinforcement learning. The method is detailed for the application of discovering missing terms in differential equations. An elementary instantiation of the method is described that discovers missing terms in the Burgers' equation.
Paper
🔭 @DeepGravity
The motivation of this study is to leverage recent breakthroughs in artificial intelligence research to unlock novel solutions to important scientific problems encountered in computational science. To address the human intelligence limitations in discovering reduced-order models, we propose to supplement human thinking with artificial intelligence. Our three-pronged strategy consists of learning (i) models expressed in analytical form, (ii) which are evaluated a posteriori, and iii) using exclusively integral quantities from the reference solution as prior knowledge. In point (i), we pursue interpretable models expressed symbolically as opposed to black-box neural networks, the latter only being used during learning to efficiently parameterize the large search space of possible models. In point (ii), learned models are dynamically evaluated a posteriori in the computational solver instead of based on a priori information from preprocessed high-fidelity data, thereby accounting for the specificity of the solver at hand such as its numerics. Finally in point (iii), the exploration of new models is solely guided by predefined integral quantities, e.g., averaged quantities of engineering interest in Reynolds-averaged or large-eddy simulations (LES). We use a coupled deep reinforcement learning framework and computational solver to concurrently achieve these objectives. The combination of reinforcement learning with objectives (i), (ii) and (iii) differentiate our work from previous modeling attempts based on machine learning. In this report, we provide a high-level denoscription of the model discovery framework with reinforcement learning. The method is detailed for the application of discovering missing terms in differential equations. An elementary instantiation of the method is described that discovers missing terms in the Burgers' equation.
Paper
🔭 @DeepGravity
A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns
Abstract
Here we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray powder diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.
Paper
🔭 @DeepGravity
Abstract
Here we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray powder diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.
Paper
🔭 @DeepGravity
Nature
A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns
Nature Communications - Identifying the composition of multiphase inorganic compounds from XRD patterns is challenging. Here the authors use a convolutional neural network to identify phases in...
تقریبا در همه جا عکسها و اسمهایی منتشر شده است، اما هنوز شهامت آن را پیدا نکردهام که لیست کسانی که پرگشودند را بخوانم.
گویی این دومینوی درد و مرگ دیگر گوشش بدهکار تسلیت و نیایشمان نیست. بیدرنگ و بیتردید غرق خویشتن خویش است. آنچه که برای اوست شاید انجام بدون چون چرای تکلیفش باشد، لیک برای ما جانهای عزیزی است که ستانده میشود هر روز، و چکههای اشک بی وقفهی ماست که نهر میشود هر روز. برای او شاید اقتضای طبیعتش باشد، اما برای ما دردها و اندوههایی است که تا ژرفای دلهای سردرگممان رخنه میکند، شگفتزدگی و آلام بیانتهایی است که یارای شکیباییمان را میرباید.
اگرچه ابراز همدردی هیچ سفرکردهای را نیروی بازگشت نشده است، اما شاید ذرهای دلگرمی برای بازماندگان باشد. پس به رسم ادب، همدردی - این کوچکترین نماد احساس اندوه عمیق - به پیشگاه شما ابراز میشود.
به امید فردایی بهتر، شادتر و آزادتر برای ایران و ایرانی ...
@Reza
🔭 @DeepGravity
گویی این دومینوی درد و مرگ دیگر گوشش بدهکار تسلیت و نیایشمان نیست. بیدرنگ و بیتردید غرق خویشتن خویش است. آنچه که برای اوست شاید انجام بدون چون چرای تکلیفش باشد، لیک برای ما جانهای عزیزی است که ستانده میشود هر روز، و چکههای اشک بی وقفهی ماست که نهر میشود هر روز. برای او شاید اقتضای طبیعتش باشد، اما برای ما دردها و اندوههایی است که تا ژرفای دلهای سردرگممان رخنه میکند، شگفتزدگی و آلام بیانتهایی است که یارای شکیباییمان را میرباید.
اگرچه ابراز همدردی هیچ سفرکردهای را نیروی بازگشت نشده است، اما شاید ذرهای دلگرمی برای بازماندگان باشد. پس به رسم ادب، همدردی - این کوچکترین نماد احساس اندوه عمیق - به پیشگاه شما ابراز میشود.
به امید فردایی بهتر، شادتر و آزادتر برای ایران و ایرانی ...
@Reza
🔭 @DeepGravity
Computational Engineering Division Student Intern
Multiple Lecturer/Senior Lecturer (equivalent to U.S. Assistant Professor tenure track) positions in machine learning and computer vision at the University of Melbourne’s School of Computing and Information Systems.
MS/PhD/Visiting scholar positions for Deep RL Ford-WVU
PhD Positions for Robot Learning Uni Freiburg
2 PhD positions available – University College Cork (UCC)
Machine Learning Researcher, UK
New Post-doc Opening at U. of Toronto on Deep Learning / RL for Traffic Prediction and Control
RL/LfD research positions (including interns) at Bosch / UT Austin, focusing on autonomous vehicles
#Job
🔭 @DeepGravity
Multiple Lecturer/Senior Lecturer (equivalent to U.S. Assistant Professor tenure track) positions in machine learning and computer vision at the University of Melbourne’s School of Computing and Information Systems.
MS/PhD/Visiting scholar positions for Deep RL Ford-WVU
PhD Positions for Robot Learning Uni Freiburg
2 PhD positions available – University College Cork (UCC)
Machine Learning Researcher, UK
New Post-doc Opening at U. of Toronto on Deep Learning / RL for Traffic Prediction and Control
RL/LfD research positions (including interns) at Bosch / UT Austin, focusing on autonomous vehicles
#Job
🔭 @DeepGravity