Algorithmic Improvements for #DeepReinforcement #Learning applied to Interactive Fiction
Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.
Link
🔭 @DeepGravity
Text-based games are a natural challenge domain for deep reinforcement learning algorithms. Their state and action spaces are combinatorially large, their reward function is sparse, and they are partially observable: the agent is informed of the consequences of its actions through textual feedback. In this paper we emphasize this latter point and consider the design of a deep reinforcement learning agent that can play from feedback alone. Our design recognizes and takes advantage of the structural characteristics of text-based games. We first propose a contextualisation mechanism, based on accumulated reward, which simplifies the learning problem and mitigates partial observability. We then study different methods that rely on the notion that most actions are ineffectual in any given situation, following Zahavy et al.'s idea of an admissible action. We evaluate these techniques in a series of text-based games of increasing difficulty based on the TextWorld framework, as well as the iconic game Zork. Empirically, we find that these techniques improve the performance of a baseline deep reinforcement learning agent applied to text-based games.
Link
🔭 @DeepGravity
#Google Introduces New Metrics for #AI -Generated Audio and Video Quality
Google AI researchers published two new metrics for measuring the quality of audio and video generated by deep-learning networks, the Fréchet Audio Distance (FAD) and Fréchet Video Distance (FVD). The metrics have been shown to have a high correlation with human evaluations of quality.
Link
🔭 @DeepGravity
Google AI researchers published two new metrics for measuring the quality of audio and video generated by deep-learning networks, the Fréchet Audio Distance (FAD) and Fréchet Video Distance (FVD). The metrics have been shown to have a high correlation with human evaluations of quality.
Link
🔭 @DeepGravity
InfoQ
Google Introduces New Metrics for AI-Generated Audio and Video Quality
Google AI researchers published two new metrics for measuring the quality of audio and video generated by deep-learning networks, the Fréchet Audio Distance (FAD) and Fréchet Video Distance (FVD). The metrics have been shown to have a high correlation with…
Introducing TensorBoard.dev: a new way to share your #ML experiment results
TensorBoard, TensorFlow’s visualization toolkit, is often used by researchers and engineers to visualize and understand their ML experiments. It enables tracking experiment metrics, visualizing models, profiling ML programs, visualizing hyperparameter tuning experiments, and much more.
#TensorBoard
Link
🔭 @DeepGravity
TensorBoard, TensorFlow’s visualization toolkit, is often used by researchers and engineers to visualize and understand their ML experiments. It enables tracking experiment metrics, visualizing models, profiling ML programs, visualizing hyperparameter tuning experiments, and much more.
#TensorBoard
Link
🔭 @DeepGravity
blog.tensorflow.org
Introducing TensorBoard.dev: a new way to share your ML experiment results
The TensorFlow blog contains regular news from the TensorFlow team and the community, with articles on Python, TensorFlow.js, TF Lite, TFX, and more.
Procgen Benchmark
We’re releasing Procgen Benchmark, 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills.
#OpenAI
Link
🔭 @DeepGravity
We’re releasing Procgen Benchmark, 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills.
#OpenAI
Link
🔭 @DeepGravity
Openai
Procgen Benchmark
We’re releasing Procgen Benchmark, 16 simple-to-use procedurally-generated environments which provide a direct measure of how quickly a reinforcement learning agent learns generalizable skills.
Major trends in #NLP : a review of 20 years of #ACL research
The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) is starting this week in Florence, Italy. We took the opportunity to review major research trends in the animated NLP space and formulate some implications from the business perspective. The article is backed by a statistical and — guess what — NLP-based analysis of ACL papers from the last 20 years
Link
🔭 @DeepGravity
The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) is starting this week in Florence, Italy. We took the opportunity to review major research trends in the animated NLP space and formulate some implications from the business perspective. The article is backed by a statistical and — guess what — NLP-based analysis of ACL papers from the last 20 years
Link
🔭 @DeepGravity
Medium
Major trends in NLP: a review of 20 years of ACL research
The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) is starting this week in Florence, Italy. We took the…
Generalized Coefficient of Correlation for Non-Linear Relationships
What is the best correlation coefficient R(X, Y) to measure non-linear dependencies between two variables X and Y? Let's say that you want to assess weather there is a linear or quadratic relationship between X and Y. One way to do it is to perform a polynomial regression such as Y = a + bX + cX^2, and then measure the standard coefficient of correlation between the predicted and observed values. How good is this approach?
Link
🔭 @DeepGravity
What is the best correlation coefficient R(X, Y) to measure non-linear dependencies between two variables X and Y? Let's say that you want to assess weather there is a linear or quadratic relationship between X and Y. One way to do it is to perform a polynomial regression such as Y = a + bX + cX^2, and then measure the standard coefficient of correlation between the predicted and observed values. How good is this approach?
Link
🔭 @DeepGravity
Datasciencecentral
Generalized Coefficient of Correlation for Non-Linear Relationships
What is the best correlation coefficient R(X, Y) to measure non-linear dependencies between two variables X and Y? Let's say that you want to assess weather th…
Research Fellow in Deep Reinforcement Learning for Machine Theory of Mind @ Oxford Brookes
New Post-doc Opening at U. of Toronto on Deep Learning / RL for Traffic Prediction and Control
Seeking PostDoctoral Fellow in Machine Learning (Survival Prediction; Medical Informatics), University of Alberta
2 full-time academic position vacancies on Data Science and related topics in ULB, Brussels, Belgium
Postdoc at Monash University (Melbourne) for probabilistic & deep learning
New Post-doc Opening at U. of Toronto on Deep Learning / RL for Traffic Prediction and Control
MERL is seeking a motivated and qualified individual to conduct research in safe reinforcement learning (RL) and deep learning algorithms for robotics applications.
Fully-funded Post Doctoral Position at InterDigitl, Information Theory for Understanding and Designing Flexible Deep Neural Networks
AI Scientist positions at AI Singapore
RL and LfD research positions (now including interns) at Bosch / UT Austin, focusing on autonomous vehicles
looking for an Integrated Master's cum PhD studentship position across the globe in the areas of Artificial Intelligence, Machine Learning, Data Science, Natural Language Processing
Permanent academic position - Lecturer/Senior Lecturer/Reader in Media & Data Science, University of Glasgow, School of Computing Science
PhD positions in Machine Learning in ECE at George Washington University, USA
2 PhD Candidates in Computer Science, paluno - The Ruhr Institute for Software Technology, Universität Duisburg-Essen
3-year fully funded PhD position on Multimodal Machine Learning for Mental Health (CNRS GREYC, France)
Research Fellow / Senior Research Fellow at the intersection of machine learning and robotics
Two postdoctoral positions are available in the lab of Carlos Fernandez-Granda at the Courant Institute and Center for Data Science at NYU
#Job
🔭 @DeepGravity
New Post-doc Opening at U. of Toronto on Deep Learning / RL for Traffic Prediction and Control
Seeking PostDoctoral Fellow in Machine Learning (Survival Prediction; Medical Informatics), University of Alberta
2 full-time academic position vacancies on Data Science and related topics in ULB, Brussels, Belgium
Postdoc at Monash University (Melbourne) for probabilistic & deep learning
New Post-doc Opening at U. of Toronto on Deep Learning / RL for Traffic Prediction and Control
MERL is seeking a motivated and qualified individual to conduct research in safe reinforcement learning (RL) and deep learning algorithms for robotics applications.
Fully-funded Post Doctoral Position at InterDigitl, Information Theory for Understanding and Designing Flexible Deep Neural Networks
AI Scientist positions at AI Singapore
RL and LfD research positions (now including interns) at Bosch / UT Austin, focusing on autonomous vehicles
looking for an Integrated Master's cum PhD studentship position across the globe in the areas of Artificial Intelligence, Machine Learning, Data Science, Natural Language Processing
Permanent academic position - Lecturer/Senior Lecturer/Reader in Media & Data Science, University of Glasgow, School of Computing Science
PhD positions in Machine Learning in ECE at George Washington University, USA
2 PhD Candidates in Computer Science, paluno - The Ruhr Institute for Software Technology, Universität Duisburg-Essen
3-year fully funded PhD position on Multimodal Machine Learning for Mental Health (CNRS GREYC, France)
Research Fellow / Senior Research Fellow at the intersection of machine learning and robotics
Two postdoctoral positions are available in the lab of Carlos Fernandez-Granda at the Courant Institute and Center for Data Science at NYU
#Job
🔭 @DeepGravity
DeepLearning Academy courses
Applied Deep Learning for Predictive Analytics
Deep Learning with #TensorFlow
#DeepLearning
#Course
🔭 @DeepGravity
Applied Deep Learning for Predictive Analytics
Deep Learning with #TensorFlow
#DeepLearning
#Course
🔭 @DeepGravity
Deeplearning-Academy
Courses
Advanced Deep Learning Education and mentoring platform | Learn and practice on real Data Science projects | Get prepared to work as a Deep Learning Engineer.
#DeepLearning models tend to increase their accuracy with the increasing amount of training data, where’s traditional #MachineLearning models such as #SVM and Naive #Bayes classifier stop improving after a saturation point.
Link
🔭 @DeepGravity
Link
🔭 @DeepGravity
#VariationalAutoencoder Theory
The Variational Autoencoder has taken the #MachineLearning community by storm since Kingma and Welling’s seminal paper was released in 20131.
Link
🔭 @DeepGravity
The Variational Autoencoder has taken the #MachineLearning community by storm since Kingma and Welling’s seminal paper was released in 20131.
Link
🔭 @DeepGravity
#DecisionTree vs #RandomForest vs #GradientBoostingMachines: Explained Simply
Decision Trees, Random Forests and Boosting are among the top 16 #data science and machine learning tools used by data scientists. The three methods are similar, with a significant amount of overlap. In a nutshell:
* A decision tree is a simple, decision making-diagram.
* Random forests are a large number of trees, combined (using averages or "majority rules") at the end of the process.
* Gradient boosting machines also combine decision trees, but start the combining process at the beginning, instead of at the end.
Link
🔭 @DeepGravity
Decision Trees, Random Forests and Boosting are among the top 16 #data science and machine learning tools used by data scientists. The three methods are similar, with a significant amount of overlap. In a nutshell:
* A decision tree is a simple, decision making-diagram.
* Random forests are a large number of trees, combined (using averages or "majority rules") at the end of the process.
* Gradient boosting machines also combine decision trees, but start the combining process at the beginning, instead of at the end.
Link
🔭 @DeepGravity
Semantic Image #Segmentation with #DeepLab in #TensorFlow
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person”, “dog”, to every pixel in an image enables numerous new applications, such as the synthetic shallow depth-of-field effect shipped in the portrait mode of the Pixel 2 and Pixel 2 XL smartphones and mobile real-time video segmentation. Assigning these semantic labels requires pinpointing the outline of objects, and thus imposes much stricter localization accuracy requirements than other visual entity recognition tasks such as image-level classification or bounding box-level detection.
Link
🔭 @DeepGravity
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person”, “dog”, to every pixel in an image enables numerous new applications, such as the synthetic shallow depth-of-field effect shipped in the portrait mode of the Pixel 2 and Pixel 2 XL smartphones and mobile real-time video segmentation. Assigning these semantic labels requires pinpointing the outline of objects, and thus imposes much stricter localization accuracy requirements than other visual entity recognition tasks such as image-level classification or bounding box-level detection.
Link
🔭 @DeepGravity
Datasciencecentral
Semantic Image Segmentation with DeepLab in TensorFlow
This article was written by Liang-Chieh Chen and Yukun Zhu.
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person…
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person…
Forwarded from Apply Time Positions
Dr. Mahdi Imani, an assistant professor at the Department of Electrical and Computer Engineering at the George Washington University, seeks for multiple PhD students with interests in Machine Learning, Reinforcement Learning and Statistics. Ideal candidates may have:
- Master’s degree in electrical/computer engineering or computer science.
- Strong background in mathematics and statistics.
- Good programming skills (e.g., Python).
Prospective students may email their CV, trannoscripts and English test scores at imani.gwu@gmail.com. For more information, see https://web.seas.gwu.edu/imani/.
Self-supported postdoctoral and visiting scholars are encouraged to contact as well.
--
Mahdi Imani, Ph.D.
Assistant Professor
Dept. of Electrical and Computer Eng.
George Washington University
https://web.seas.gwu.edu/imani/
✔️ @ApplyTime
- Master’s degree in electrical/computer engineering or computer science.
- Strong background in mathematics and statistics.
- Good programming skills (e.g., Python).
Prospective students may email their CV, trannoscripts and English test scores at imani.gwu@gmail.com. For more information, see https://web.seas.gwu.edu/imani/.
Self-supported postdoctoral and visiting scholars are encouraged to contact as well.
--
Mahdi Imani, Ph.D.
Assistant Professor
Dept. of Electrical and Computer Eng.
George Washington University
https://web.seas.gwu.edu/imani/
✔️ @ApplyTime
A very interesting paper by #Harvard University and #OpenAI
#DeepDoubleDescent: WHERE BIGGER MODELS AND MORE DATA HURT
ABSTRACT
We show that a variety of modern deep learning tasks exhibit a “double-descent” phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We unify the above phenomena by defining a new complexity measure we call the effective model complexity and conjecture a generalized double descent with respect to this measure. Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually hurts test performance.
Paper
Related article
#DeepLearning
🔭 @DeepGravity
#DeepDoubleDescent: WHERE BIGGER MODELS AND MORE DATA HURT
ABSTRACT
We show that a variety of modern deep learning tasks exhibit a “double-descent” phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We unify the above phenomena by defining a new complexity measure we call the effective model complexity and conjecture a generalized double descent with respect to this measure. Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually hurts test performance.
Paper
Related article
#DeepLearning
🔭 @DeepGravity
Openai
Deep double descent
We show that the double descent phenomenon occurs in CNNs, ResNets, and transformers: performance first improves, then gets worse, and then improves again with increasing model size, data size, or training time. This effect is often avoided through careful…