As always with deepfakes, there is a trade off between quality, likeness and Cage. In this video I put max points into Cage.

A theoretical performance analysis of the graph neural network (GNN) is presented. For classification tasks, the neural network approach has the advantage in terms of flexibility that it can be employed in a data-driven manner, whereas Bayesian inference requires the assumption of a specific model. A fundamental question is then whether GNN has a high accuracy in addition to this flexibility. Moreover, whether the achieved performance is predominately a result of the backpropagation or the architecture itself is a matter of considerable interest. To gain a better insight into these questions, a mean-field theory of a minimal GNN architecture is developed for the graph partitioning problem. This demonstrates a good agreement with numerical experiments.

Dynamic spectrum access (DSA) is regarded as an effective and efficient technology to share radio spectrum among different networks. As a secondary user (SU), a DSA device will face two critical problems: avoiding causing harmful interference to primary users (PUs), and conducting effective interference coordination with other secondary users. These two problems become even more challenging for a distributed DSA network where there is no centralized controllers for SUs. In this paper, we investigate communication strategies of a distributive DSA network under the presence of spectrum sensing errors. To be specific, we apply the powerful machine learning tool, deep reinforcement learning (DRL), for SUs to learn "appropriate" spectrum access strategies in a distributed fashion assuming NO knowledge of the underlying system statistics. Furthermore, a special type of recurrent neural network (RNN), called the reservoir computing (RC), is utilized to realize DRL by taking advantage of the underlying temporal correlation of the DSA network. Using the introduced machine learning-based strategy, SUs could make spectrum access decisions distributedly relying only on their own current and past spectrum sensing outcomes. Through extensive experiments, our results suggest that the RC-based spectrum access strategy can help the SU to significantly reduce the chances of collision with PUs and other SUs. We also show that our scheme outperforms the myopic method which assumes the knowledge of system statistics, and converges faster than the Q-learning method when the number of channels is large.

Dynamic spatiotemporal processes on the Earth can be observed by an increasing number of optical Earth observation satellites that measure spectral reflectance at multiple spectral bands in regular intervals. Clouds partially covering the surface is an omnipresent challenge for the majority of remote sensing approaches that are not robust regarding cloud coverage. In these approaches, clouds are typically handled by cherry-picking cloud-free observations or by pre-classification of cloudy pixels and subsequent masking. In this work, we demonstrate the robustness of a straightforward convolutional long short-term memory network for vegetation classification using all available cloudy and non-cloudy satellite observations. We visualize the internal gate activations within the recurrent cells and find that, in some cells, modulation and input gates close on cloudy pixels. This indicates that the network has internalized a cloud-filtering mechanism without being specifically trained on cloud labels. The robustness regarding clouds is further demonstrated by experiments on sequences with varying degrees of cloud coverage where our network achieved similar accuracies on all cloudy and non-cloudy datasets. Overall, our results question the necessity of sophisticated pre-processing pipelines if robust classification methods are utilized.

Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is no mechanism to monitor a patient's progress with CPAP. Accurate detection of sleep stages from CPAP flow signal is crucial for such a mechanism. We propose, for the first time, an automated sleep staging model based only on the flow signal. Deep neural networks have recently shown high accuracy on sleep staging by eliminating handcrafted features. However, these methods focus exclusively on extracting informative features from the input signal, without paying much attention to the dynamics of sleep stages in the output sequence. We propose an end-to-end framework that uses a combination of deep convolution and recurrent neural networks to extract high-level features from raw flow signal with a structured output layer based on a conditional random field to model the temporal transition structure of the sleep stages. We improve upon the previous methods by 10% using our model, that can be augmented to the previous sleep staging deep learning methods. We also show that our method can be used to accurately track sleep metrics like sleep efficiency calculated from sleep stages that can be deployed for monitoring the response of CPAP therapy on sleep apnea patients. Apart from the technical contributions, we expect this study to motivate new research questions in sleep science.

Learning and inference movement is a very challenging problem due to its high dimensionality and dependency to varied environments or tasks. In this paper, we propose an effective probabilistic method for learning and inference of basic movements. The motion planning problem is formulated as learning on a directed graphic model and deep generative model is used to perform learning and inference from demonstrations. An important characteristic of this method is that it flexibly incorporates the task descriptors and context information for long-term planning and it can be combined with dynamic systems for robot control. The experimental validations on robotic approaching path planning tasks show the advantages over the base methods with limited training data.

Understanding Human reasoning has been a focal point of a lot of AI research in the past decade. The article is an extremely well researched attempt to bring out the journey.

To accommodate the growing number of startups and international players moving into the city, Beijing officials announced a plan to build a $2.12bn AI development park in the west of the city. Set to house up to 400 businesses, the AI park will have an estimated output of CNY50bn ($7.19bn). According to Chinese news agency Xinhua, project developer Zhongguancun Development Group will partner with foreign universities to build a national-level AI lab in the area. Xinhua revealed that the new park would aim to attract enterprises working in the areas of cloud computing, big data, deep learning and biometric identification.

This is a PyTorch implementation of the original Lua code release. This codebase implements two approaches to learning discrete communication protocols for playing collaborative games: Reinforced Inter-Agent Learning (RIAL), in which agents learn a factorized deep Q-learning policy across game actions and messages, and Differentiable Inter-Agent Learning (DIAL), in which the message vectors are directly learned by backpropagating errors through a noisy communication channel during training, and discretized to binary vectors during test time. While RIAL and DIAL share the same individual network architecture, one would expect learning to be more efficient under DIAL, which directly backpropagates downstream errors during training, a fact that is verified in comparing the performance of the two approaches. This chart was generated by plotting an exponentially-weighted average across 20 trials for each curve. JSON configuration files passed to main.py should consist of the following key-value pairs: You can use analyze_results.py to graph results output by main.py.

Detecting lung nodules on CT scans is central to diagnosing cancer and the earlier that can be accomplished the better the outcome. Currently, only about two thirds of detectable nodules are actually spotted by trained professionals. Researchers at University of Central Florida have been working on an artificial intelligence program that can review CT scans and spot nodules with near perfect accuracy. Their software, called S4ND, relies on deep learning techniques, coupled with more than 1,000 previously captured lung CT scans, to point out suspect lesions. The Central Florida team built a 3D convolutional neural network to power S4ND so that it doesn't require any input from the user to produce the results.