#Fun

🔭 @DeepGravity

Our reinforcement learning architect designs have been just published on #NeurIPS2019 AI Art Gallery:

https://lnkd.in/dFZ37BN

Seems it draws like a baby now, but is growing and hopefully would be a skillful #RL artist very soon.

#reinforcementlearning #deeplearning #ai #artificialintelligence #art #deepreinforcementlearning #creativeart #neurips

🔭 @DeepGravity

#NeurIPS2019:

Lectures

Papers

🔭 @DeepGravity

P-CapsNets: a General Form of #ConvolutionalNeuralNetworks

We propose Pure CapsNets (P-CapsNets) which is a generation of normal CNNs structurally. Specifically, we make three modifications to current CapsNets. First, we remove routing procedures from CapsNets based on the observation that the coupling coefficients can be learned implicitly. Second, we replace the convolutional layers in CapsNets to improve efficiency. Third, we package the capsules into rank-3 tensors to further improve efficiency. The experiment shows that P-CapsNets achieve better performance than CapsNets with varied routing procedures by using significantly fewer parameters on MNIST&CIFAR10. The high efficiency of P-CapsNets is even comparable to some deep compressing models. For example, we achieve more than 99% percent accuracy on MNIST by using only 3888 parameters. We visualize the capsules as well as the corresponding correlation matrix to show a possible way of initializing CapsNets in the future. We also explore the adversarial robustness of P-CapsNets compared to #CNNs.

Paper

🔭 @DeepGravity

Seven differences between academia and industry for building machine learning and #deepLearning models

1) Approach to accuracy
2) Training vs serving
3) Emphasis on Engineering
4) Less emphasis on larger models
5) Understanding the baseline
6) Understanding the intricacies of data
7) Focusing on deep learning too early

Link

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Neural Design Network: Graphic Layout Generation with Constraints

Graphic design is essential for visual communication with layouts being fundamental to composing attractive designs. Layout generation differs from pixel-level image synthesis and is unique in terms of the requirement of mutual relations among the desired components. We propose a method for design layout generation that can satisfy user-specified constraints. The proposed neural design network (NDN) consists of three modules. The first module predicts a graph with complete relations from a graph with user-specified relations. The second module generates a layout from the predicted graph. Finally, the third module fine-tunes the predicted layout. Quantitative and qualitative experiments demonstrate that the generated layouts are visually similar to real design layouts. We also construct real designs based on predicted layouts for a better understanding of the visual quality. Finally, we demonstrate a practical application on layout recommendation.

Paper

🔭 @DeepGravity

#TensorFlow 2 Tutorial: Get Started in #DeepLearning With tf.keras

After completing this tutorial, you will know:

The difference between Keras and tf.keras and how to install and confirm TensorFlow is working.
The 5-step life-cycle of tf.keras models and how to use the sequential and functional APIs.
How to develop MLP, CNN, and RNN models with tf.keras for regression, classification, and time series forecasting.
How to use the advanced features of the tf.keras API to inspect and diagnose your model.
How to improve the performance of your tf.keras model by reducing overfitting and accelerating training.

#Keras

Link

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White Paper: #MachineLearning for #Physics and the Physics of Learning

Paper

🔭 @DeepGravity

Best #Data #Visualization Techniques for small and large data

Data visualization is used in many areas to model complex events and visualize phenomena that cannot be observed directly, such as weather patterns, medical conditions or mathematical relationships. Here we review basic data visualization tools and techniques.

Link

🔭 @DeepGravity

#GradientBased training of #Gaussian Mixture Models in High-Dimensional Spaces

We present an approach for efficiently training Gaussian Mixture Models (GMMs) with Stochastic Gradient Descent (SGD) on large amounts of high-dimensional data (e.g., images). In such a scenario, SGD is strongly superior in terms of execution time and memory usage, although it is conceptually more complex than the traditional Expectation-Maximization (EM) algorithm. For enabling #SGD training, we propose three novel ideas: First, we show that minimizing an upper bound to the GMM log likelihood instead of the full one is feasible and numerically much more stable way in high-dimensional spaces. Secondly, we propose a new annealing procedure that prevents SGD from converging to pathological local minima. We also propose an SGD-compatible simplification to the full #GMM model based on local principal directions, which avoids excessive memory use in high-dimensional spaces due to quadratic growth of covariance matrices. Experiments on several standard image datasets show the validity of our approach, and we provide a publicly available TensorFlow implementation.

Paper

🔭 @DeepGravity

Building #ConvolutionalNeuralNetwork using #NumPy from Scratch

In this article, #CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), #ReLU, and max pooling.

🔭 @DeepGravity

Analysing #DeepReinforcementLearning Agents Trained with Domain Randomisation

Deep reinforcement learning has the potential to train robots to perform complex tasks in the real world without requiring accurate models of the robot or its environment. A practical approach is to train agents in simulation, and then transfer them to the real world. One of the most popular methods for achieving this is to use domain randomisation, which involves randomly perturbing various aspects of a simulated environment in order to make trained agents robust to the reality gap between the simulator and the real world. However, less work has gone into understanding such agents-which are deployed in the real world-beyond task performance. In this work we examine such agents, through qualitative and quantitative comparisons between agents trained with and without visual domain randomisation, in order to provide a better understanding of how they function. In this work, we train agents for Fetch and Jaco robots on a visuomotor control task, and evaluate how well they generalise using different unit tests. We tie this with interpretability techniques, providing both quantitative and qualitative data. Finally, we investigate the internals of the trained agents by examining their weights and activations. Our results show that the primary outcome of domain randomisation is more redundant, entangled representations, accompanied with significant statistical/structural changes in the weights; moreover, the types of changes are heavily influenced by the task setup and presence of additional proprioceptive inputs. Furthermore, even with an improved saliency method introduced in this work, we show that qualitative studies may not always correspond with quantitative measures, necessitating the use of a wide suite of inspection tools in order to provide sufficient insights into the behaviour of trained agents.

Paper

🔭 @DeepGravity

#Transferlearning in hybrid classical- #quantum #neuralNetworks

We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements. We propose different implementations of hybrid transfer learning, but we focus mainly on the paradigm in which a pre-trained classical network is modified and augmented by a final variational quantum circuit. This approach is particularly attractive in the current era of intermediate-scale quantum technology since it allows to optimally pre-process high dimensional data (e.g., images) with any state-of-the-art classical network and to embed a select set of highly informative features into a quantum processor. We present several proof-of-concept examples of the convenient application of quantum transfer learning for image recognition and quantum state classification. We use the cross-platform software library PennyLane to experimentally test a high-resolution image classifier with two different quantum computers, respectively provided by #IBM and Rigetti.

Paper

🔭 @DeepGravity

Analysing #DeepReinforcementLearning Agents Trained with Domain Randomisation

Deep reinforcement learning has the potential to train robots to perform complex tasks in the real world without requiring accurate models of the robot or its environment. A practical approach is to train agents in simulation, and then transfer them to the real world. One of the most popular methods for achieving this is to use domain randomisation, which involves randomly perturbing various aspects of a simulated environment in order to make trained agents robust to the reality gap between the simulator and the real world. However, less work has gone into understanding such agents-which are deployed in the real world-beyond task performance. In this work we examine such agents, through qualitative and quantitative comparisons between agents trained with and without visual domain randomisation, in order to provide a better understanding of how they function. In this work, we train agents for Fetch and Jaco robots on a visuomotor control task, and evaluate how well they generalise using different unit tests. We tie this with interpretability techniques, providing both quantitative and qualitative data. Finally, we investigate the internals of the trained agents by examining their weights and activations. Our results show that the primary outcome of domain randomisation is more redundant, entangled representations, accompanied with significant statistical/structural changes in the weights; moreover, the types of changes are heavily influenced by the task setup and presence of additional proprioceptive inputs. Furthermore, even with an improved saliency method introduced in this work, we show that qualitative studies may not always correspond with quantitative measures, necessitating the use of a wide suite of inspection tools in order to provide sufficient insights into the behaviour of trained agents.

Paper

🔭 @DeepGravity

Interview with #DeepLearning Researcher and The #GAN father: Dr. Ian #Goodfellow

Link

🔭 @DeepGravity

#ReinforcementLearning and #Control as #ProbabilisticInference: Tutorial and Review

The framework of reinforcement learning or #OptimalControl provides a mathematical formalization of intelligent decision making that is powerful and broadly applicable. While the general form of the reinforcement learning problem enables effective reasoning about uncertainty, the connection between reinforcement learning and inference in probabilistic models is not immediately obvious. However, such a connection has considerable value when it comes to algorithm design: formalizing a problem as probabilistic inference in principle allows us to bring to bear a wide array of approximate inference tools, extend the model in flexible and powerful ways, and reason about compositionality and partial observability. In this article, we will discuss how a generalization of the reinforcement learning or optimal control problem, which is sometimes termed maximum entropy reinforcement learning, is equivalent to exact probabilistic inference in the case of deterministic dynamics, and variational inference in the case of stochastic dynamics. We will present a detailed derivation of this framework, overview prior work that has drawn on this and related ideas to propose new reinforcement learning and control algorithms, and describe perspectives on future research.

Link

🔭 @DeepGravity

Interestingness Elements for Explainable #ReinforcementLearning: Understanding Agents' Capabilities and Limitations

We propose an explainable reinforcement learning (XRL) framework that analyzes an agent's history of interaction with the environment to extract interestingness elements that help explain its behavior. The framework relies on data readily available from standard RL algorithms, augmented with data that can easily be collected by the agent while learning. We describe how to create visual explanations of an agent's behavior in the form of short video-clips highlighting key interaction moments, based on the proposed elements. We also report on a user study where we evaluated the ability of humans in correctly perceiving the aptitude of agents with different characteristics, including their capabilities and limitations, given explanations automatically generated by our framework. The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly identify the agents' aptitude in the task, and determine when they might need adjustments to improve their performance.

Link

🔭 @DeepGravity

Semantic Segmentation from Remote Sensor Data and the Exploitation of Latent Learning for Classification of Auxiliary Tasks

In this paper we address three different aspects of semantic segmentation from remote sensor data using deep neural networks. Firstly, we focus on the semantic segmentation of buildings from remote sensor data and propose ICT-Net. The proposed network has been tested on the INRIA and AIRS benchmark datasets and is shown to outperform all other state of the art by more than 1.5 1.8 Secondly, as the building classification is typically the first step of the reconstruction process, we investigate the relationship of the classification accuracy to the reconstruction accuracy. Finally, we present the simple yet compelling concept of latent learning and the implications it carries within the context of deep learning. We posit that a network trained on a primary task (i.e. building classification) is unintentionally learning about auxiliary tasks (e.g. the classification of road, tree, etc) which are complementary to the primary task. We extensively tested the proposed technique on the ISPRS benchmark dataset which contains multi-label ground truth, and report an average classification accuracy (F1 score) of 54.29 (SD=5.25) for trees, 42.74 for clutter. The source code and supplemental material is publicly available at http://www.theICTlab.org/lp/2019ICT-Net/.

Link

🔭 @DeepGravity

Neural Design Network: Graphic Layout Generation with Constraints

Graphic design is essential for visual communication with layouts being fundamental to composing attractive designs. Layout generation differs from pixel-level image synthesis and is unique in terms of the requirement of mutual relations among the desired components. We propose a method for design layout generation that can satisfy user-specified constraints. The proposed neural design network (NDN) consists of three modules. The first module predicts a graph with complete relations from a graph with user-specified relations. The second module generates a layout from the predicted graph. Finally, the third module fine-tunes the predicted layout. Quantitative and qualitative experiments demonstrate that the generated layouts are visually similar to real design layouts. We also construct real designs based on predicted layouts for a better understanding of the visual quality. Finally, we demonstrate a practical application on layout recommendation.

Link

🔭 @DeepGravity

P-CapsNets: a General Form of #ConvolutionalNeuralNetworks

We propose Pure CapsNets (P-CapsNets) which is a generation of normal #CNNs structurally. Specifically, we make three modifications to current CapsNets. First, we remove routing procedures from CapsNets based on the observation that the coupling coefficients can be learned implicitly. Second, we replace the convolutional layers in CapsNets to improve efficiency. Third, we package the capsules into rank-3 tensors to further improve efficiency. The experiment shows that P-CapsNets achieve better performance than CapsNets with varied routing procedures by using significantly fewer parameters on MNIST&CIFAR10. The high efficiency of P-CapsNets is even comparable to some deep compressing models. For example, we achieve more than 99% percent accuracy on MNIST by using only 3888 parameters. We visualize the capsules as well as the corresponding correlation matrix to show a possible way of initializing CapsNets in the future. We also explore the adversarial robustness of P-CapsNets compared to CNNs.

Paper

🔭 @DeepGravity

DerainCycleGAN: An Attention-guided Unsupervised Benchmark for Single Image Deraining and Rainmaking

Single image deraining (SID) is an important and challenging topic in emerging vision applications, and most of emerged deraining methods are supervised relying on the ground truth (i.e., paired images) in recent years. However, in practice it is rather common to have no un-paired images in real deraining task, in such cases how to remove the rain streaks in an unsupervised way will be a very challenging task due to lack of constraints between images and hence suffering from low-quality recovery results. In this paper, we explore the unsupervised SID task using unpaired data and propose a novel net called Attention-guided Deraining by Constrained CycleGAN (or shortly, DerainCycleGAN), which can fully utilize the constrained transfer learning abilitiy and circulatory structure of #CycleGAN. Specifically, we design an unsu-pervised attention guided rain streak extractor (U-ARSE) that utilizes a memory to extract the rain streak masks with two constrained cycle-consistency branches jointly by paying attention to both the rainy and rain-free image domains. As a by-product, we also contribute a new paired rain image dataset called Rain200A, which is constructed by our network automatically. Compared with existing synthesis datasets, the rainy streaks in Rain200A contains more obvious and diverse shapes and directions. As a result, existing supervised methods trained on Rain200A can perform much better for processing real rainy images. Extensive experiments on synthesis and real datasets show that our net is superior to existing unsupervised deraining networks, and is also very competitive to other related supervised networks.

Paper

🔭 @DeepGravity