#ReinforcementLearning for ArtiSynth
This repository holds the plugin for the #biomechanical simulation environment of ArtiSynth. The purpose of this work is to bridge in between the biomechanical and reinforcement learning domains of research.
Link
🔭 @DeepGravity
This repository holds the plugin for the #biomechanical simulation environment of ArtiSynth. The purpose of this work is to bridge in between the biomechanical and reinforcement learning domains of research.
Link
🔭 @DeepGravity
GitHub
GitHub - amir-abdi/artisynth-rl: Reinforcement Learning plugin and models for ArtiSynth
Reinforcement Learning plugin and models for ArtiSynth - GitHub - amir-abdi/artisynth-rl: Reinforcement Learning plugin and models for ArtiSynth
#StyleGANv2 Explained!
This video explores changes to the StyleGAN architecture to remove certain artifacts, increase training speed, and achieve a much smoother latent space interpolation! This paper also presents an interesting Deepfake detection algorithm enabled by their improvements to latent space interpolation.
YouTube
🔭 @DeepGravity
This video explores changes to the StyleGAN architecture to remove certain artifacts, increase training speed, and achieve a much smoother latent space interpolation! This paper also presents an interesting Deepfake detection algorithm enabled by their improvements to latent space interpolation.
YouTube
🔭 @DeepGravity
YouTube
StyleGANv2 Explained!
This video explores changes to the StyleGAN architecture to remove certain artifacts, increase training speed, and achieve a much smoother latent space inter...
The Pros and Cons of Using #JavaScript for #MachineLearning
There’s a misconception in the world of machine learning (ML)
Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. #Python and #Java often top the list.
Link
🔭 @DeepGravity
There’s a misconception in the world of machine learning (ML)
Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. #Python and #Java often top the list.
Link
🔭 @DeepGravity
DLabs
The Pros and Cons of Using JavaScript for Machine Learning - DLabs
There’s a misconception in the world of machine learning (ML) Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages. Python and Java often top the list. Python for its simplicity:…
Yoshua #Bengio: From System 1 #DeepLearning to System 2 Deep Learning ( #NeurIPS2019)
YouTube
🔭 @DeepGravity
YouTube
🔭 @DeepGravity
YouTube
Yoshua Bengio: From System 1 Deep Learning to System 2 Deep Learning (NeurIPS 2019)
This is a combined slide/speaker video of Yoshua Bengio's talk at NeurIPS 2019. Slide-synced non-YouTube version is here: https://slideslive.com/neurips/neur...
Self-regularizing restricted #Boltzmann machines
Focusing on the grand-canonical extension of the ordinary restricted Boltzmann machine, we suggest an energy-based model for feature extraction that uses a layer of hidden units with varying size. By an appropriate choice of the chemical potential and given a sufficiently large number of hidden resources the generative model is able to efficiently deduce the optimal number of hidden units required to learn the target data with exceedingly small generalization error. The formal simplicity of the grand-canonical ensemble combined with a rapidly converging ansatz in mean-field theory enable us to recycle well-established numerical algothhtims during training, like contrastive divergence, with only minor changes. As a proof of principle and to demonstrate the novel features of grand-canonical Boltzmann machines, we train our generative models on data from the Ising theory and #MNIST.
Paper
🔭 @DeepGravity
Focusing on the grand-canonical extension of the ordinary restricted Boltzmann machine, we suggest an energy-based model for feature extraction that uses a layer of hidden units with varying size. By an appropriate choice of the chemical potential and given a sufficiently large number of hidden resources the generative model is able to efficiently deduce the optimal number of hidden units required to learn the target data with exceedingly small generalization error. The formal simplicity of the grand-canonical ensemble combined with a rapidly converging ansatz in mean-field theory enable us to recycle well-established numerical algothhtims during training, like contrastive divergence, with only minor changes. As a proof of principle and to demonstrate the novel features of grand-canonical Boltzmann machines, we train our generative models on data from the Ising theory and #MNIST.
Paper
🔭 @DeepGravity
IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data
Learning from offline task demonstrations is a problem of great interest in robotics. For simple short-horizon manipulation tasks with modest variation in task instances, offline learning from a small set of demonstrations can produce controllers that successfully solve the task. However, leveraging a fixed batch of data can be problematic for larger datasets and longer-horizon tasks with greater variations. The data can exhibit substantial diversity and consist of suboptimal solution approaches. In this paper, we propose Implicit Reinforcement without Interaction at Scale (IRIS), a novel framework for learning from large-scale demonstration datasets. IRIS factorizes the control problem into a goal-conditioned low-level controller that imitates short demonstration sequences and a high-level goal selection mechanism that sets goals for the low-level and selectively combines parts of suboptimal solutions leading to more successful task completions. We evaluate IRIS across three datasets, including the RoboTurk Cans dataset collected by humans via crowdsourcing, and show that performant policies can be learned from purely offline learning. Additional results and videos at this https URL .
Paper
🔭 @DeepGravity
Learning from offline task demonstrations is a problem of great interest in robotics. For simple short-horizon manipulation tasks with modest variation in task instances, offline learning from a small set of demonstrations can produce controllers that successfully solve the task. However, leveraging a fixed batch of data can be problematic for larger datasets and longer-horizon tasks with greater variations. The data can exhibit substantial diversity and consist of suboptimal solution approaches. In this paper, we propose Implicit Reinforcement without Interaction at Scale (IRIS), a novel framework for learning from large-scale demonstration datasets. IRIS factorizes the control problem into a goal-conditioned low-level controller that imitates short demonstration sequences and a high-level goal selection mechanism that sets goals for the low-level and selectively combines parts of suboptimal solutions leading to more successful task completions. We evaluate IRIS across three datasets, including the RoboTurk Cans dataset collected by humans via crowdsourcing, and show that performant policies can be learned from purely offline learning. Additional results and videos at this https URL .
Paper
🔭 @DeepGravity
Deep Speech, a good #Persian podcasts about #AI
We will talk about #ArtificialIntelligence, #MachineLearning and DeepLearning news.
Link
🔭 @DeepGravity
We will talk about #ArtificialIntelligence, #MachineLearning and DeepLearning news.
Link
🔭 @DeepGravity
Castbox
Deep Speech | Listen Free on Castbox.
We will talk about artificial intelligence, machine learning and deep learning news.Millions of podcasts for all topics. Listen to the best free podcast...
Security of #DeepLearning Methodologies: Challenges and Opportunities
Despite the plethora of studies about security vulnerabilities and defenses of deep learning models, security aspects of deep learning methodologies, such as transfer learning, have been rarely studied. In this article, we highlight the security challenges and research opportunities of these methodologies, focusing on vulnerabilities and attacks unique to them.
Paper
🔭 @DeepGravity
Despite the plethora of studies about security vulnerabilities and defenses of deep learning models, security aspects of deep learning methodologies, such as transfer learning, have been rarely studied. In this article, we highlight the security challenges and research opportunities of these methodologies, focusing on vulnerabilities and attacks unique to them.
Paper
🔭 @DeepGravity
An interesting #game :)
#UniversalPaperclips
Link to the game
It's background story
An RL bot for playing it
🔭 @DeepGravity
#UniversalPaperclips
Link to the game
It's background story
An RL bot for playing it
🔭 @DeepGravity
Hackernoon
The Parable of the Paperclip Maximizer | HackerNoon
Once upon a time (bear with me if you’ve heard this one), there was a company which made a significant advance in artificial intelligence. Given their incredibly sophisticated new system, they started to put it to ever-wider uses, asking it to optimize their…
FootAndBall: Integrated player and ball detector
The paper describes a #deepNeuralNetwork -based detector dedicated for ball and players detection in high resolution, long shot, video recordings of soccer matches. The detector, dubbed FootAndBall, has an efficient fully convolutional architecture and can operate on input video stream with an arbitrary resolution. It produces ball confidence map encoding the position of the detected ball, player confidence map and player bounding boxes tensor encoding players' positions and bounding boxes. The network uses Feature Pyramid Network desing pattern, where lower level features with higher spatial resolution are combined with higher level features with bigger receptive field. This improves discriminability of small objects (the ball) as larger visual context around the object of interest is taken into account for the classification. Due to its specialized design, the network has two orders of magnitude less parameters than a generic deep neural network-based object detector, such as SSD or YOLO. This allows real-time processing of high resolution input video stream.
Paper
🔭 @DeepGravity
The paper describes a #deepNeuralNetwork -based detector dedicated for ball and players detection in high resolution, long shot, video recordings of soccer matches. The detector, dubbed FootAndBall, has an efficient fully convolutional architecture and can operate on input video stream with an arbitrary resolution. It produces ball confidence map encoding the position of the detected ball, player confidence map and player bounding boxes tensor encoding players' positions and bounding boxes. The network uses Feature Pyramid Network desing pattern, where lower level features with higher spatial resolution are combined with higher level features with bigger receptive field. This improves discriminability of small objects (the ball) as larger visual context around the object of interest is taken into account for the classification. Due to its specialized design, the network has two orders of magnitude less parameters than a generic deep neural network-based object detector, such as SSD or YOLO. This allows real-time processing of high resolution input video stream.
Paper
🔭 @DeepGravity
A Two-Stage Approach to #FewShotLearning for Image Recognition
This paper proposes a multi-layer neural network structure for few-shot image recognition of novel categories. The proposed multi-layer neural network architecture encodes transferable knowledge extracted from a large annotated dataset of base categories. This architecture is then applied to novel categories containing only a few samples. The transfer of knowledge is carried out at the feature-extraction and the classification levels distributed across the two training stages. In the first-training stage, we introduce the relative feature to capture the structure of the data as well as obtain a low-dimensional discriminative space. Secondly, we account for the variable variance of different categories by using a network to predict the variance of each class. Classification is then performed by computing the Mahalanobis distance to the mean-class representation in contrast to previous approaches that used the Euclidean distance. In the second-training stage, a category-agnostic mapping is learned from the mean-sample representation to its corresponding class-prototype representation. This is because the mean-sample representation may not accurately represent the novel category prototype. Finally, we evaluate the proposed network structure on four standard few-shot image recognition datasets, where our proposed few-shot learning system produces competitive performance compared to previous work. We also extensively studied and analyzed the contribution of each component of our proposed framework.
Paper
🔭 @DeepGravity
This paper proposes a multi-layer neural network structure for few-shot image recognition of novel categories. The proposed multi-layer neural network architecture encodes transferable knowledge extracted from a large annotated dataset of base categories. This architecture is then applied to novel categories containing only a few samples. The transfer of knowledge is carried out at the feature-extraction and the classification levels distributed across the two training stages. In the first-training stage, we introduce the relative feature to capture the structure of the data as well as obtain a low-dimensional discriminative space. Secondly, we account for the variable variance of different categories by using a network to predict the variance of each class. Classification is then performed by computing the Mahalanobis distance to the mean-class representation in contrast to previous approaches that used the Euclidean distance. In the second-training stage, a category-agnostic mapping is learned from the mean-sample representation to its corresponding class-prototype representation. This is because the mean-sample representation may not accurately represent the novel category prototype. Finally, we evaluate the proposed network structure on four standard few-shot image recognition datasets, where our proposed few-shot learning system produces competitive performance compared to previous work. We also extensively studied and analyzed the contribution of each component of our proposed framework.
Paper
🔭 @DeepGravity
Semantic #RL with Action Grammars: Data-Efficient Learning of Hierarchical Task Abstractions
Hierarchical Reinforcement Learning algorithms have successfully been applied to temporal credit assignment problems with sparse reward signals. However, state-of-the-art algorithms require manual specification of sub-task structures, a sample inefficient exploration phase or lack semantic interpretability. Humans, on the other hand, efficiently detect hierarchical sub-structures induced by their surroundings. It has been argued that this inference process universally applies to language, logical reasoning as well as motor control. Therefore, we propose a cognitive-inspired Reinforcement Learning architecture which uses grammar induction to identify sub-goal policies. By treating an on-policy trajectory as a sentence sampled from the policy-conditioned language of the environment, we identify hierarchical constituents with the help of unsupervised grammatical inference. The resulting set of temporal abstractions is called action grammar (Pastra & Aloimonos, 2012) and unifies symbolic and connectionist approaches to Reinforcement Learning. It can be used to facilitate efficient imitation, transfer and online learning.
Paper
🔭 @DeepGravity
Hierarchical Reinforcement Learning algorithms have successfully been applied to temporal credit assignment problems with sparse reward signals. However, state-of-the-art algorithms require manual specification of sub-task structures, a sample inefficient exploration phase or lack semantic interpretability. Humans, on the other hand, efficiently detect hierarchical sub-structures induced by their surroundings. It has been argued that this inference process universally applies to language, logical reasoning as well as motor control. Therefore, we propose a cognitive-inspired Reinforcement Learning architecture which uses grammar induction to identify sub-goal policies. By treating an on-policy trajectory as a sentence sampled from the policy-conditioned language of the environment, we identify hierarchical constituents with the help of unsupervised grammatical inference. The resulting set of temporal abstractions is called action grammar (Pastra & Aloimonos, 2012) and unifies symbolic and connectionist approaches to Reinforcement Learning. It can be used to facilitate efficient imitation, transfer and online learning.
Paper
🔭 @DeepGravity
#Reinforcement Learning as Probabilistic Modelling: A #VariationalInference Formulation
Reinforcement Learning is concerned with an agent attempting to acquire optimal behaviour in unknown environments that typically exhibit stochasticity. Though minimally supervised, reinforcement learning algorithms have shown numerous success ranging from solving ATARI games using Deep Q-Networks, to the triumphant victory against the world champions in the game of GO, and recently in Start Craft.
Article
🔭 @DeepGravity
Reinforcement Learning is concerned with an agent attempting to acquire optimal behaviour in unknown environments that typically exhibit stochasticity. Though minimally supervised, reinforcement learning algorithms have shown numerous success ranging from solving ATARI games using Deep Q-Networks, to the triumphant victory against the world champions in the game of GO, and recently in Start Craft.
Article
🔭 @DeepGravity
Medium
Reinforcement Learning as Probabilistic Modelling: A Variational Inference Formulation (Part I)
Reinforcement Learning is concerned with an agent attempting to acquire optimal behaviour in unknown environments that typically exhibit…