The success of deep neural networks in many real-world applications is leading to new challenges in building more efficient architectures. One effective way of making networks more efficient is neural network compression. We provide an overview of existing neural network compression methods that can be used to make neural networks more efficient by changing the architecture of the network. First, we introduce a new way to categorize all published compression methods, based on the amount of data and compute needed to make the methods work in practice. These are three 'levels of compression solutions'. Second, we provide a taxonomy of tensor factorization based and probabilistic compression methods. Finally, we perform an extensive evaluation of different compression techniques from the literature for models trained on ImageNet. We show that SVD and probabilistic compression or pruning methods are complementary and give the best results of all the considered methods. We also provide practical ways to combine them.

The optimization of multilayer neural networks typically leads to a solution with zero training error, yet the landscape can exhibit spurious local minima and the minima can be disconnected. In this paper, we shed light on this phenomenon: we show that the combination of stochastic gradient descent (SGD) and over-parameterization makes the landscape of multilayer neural networks approximately connected and thus more favorable to optimization. More specifically, we prove that SGD solutions are connected via a piecewise linear path, and the increase in loss along this path vanishes as the number of neurons grows large. This result is a consequence of the fact that the parameters found by SGD are increasingly dropout stable as the network becomes wider. We show that, if we remove part of the neurons (and suitably rescale the remaining ones), the change in loss is independent of the total number of neurons, and it depends only on how many neurons are left. Our results exhibit a mild dependence on the input dimension: they are dimension-free for two-layer networks and depend linearly on the dimension for multilayer networks. We validate our theoretical findings with numerical experiments for different architectures and classification tasks.

The implementation of Deep Convolutional Neural Networks (ConvNets) on tiny end-nodes with limited non-volatile memory space calls for smart compression strategies capable of shrinking the footprint yet preserving predictive accuracy. There exist a number of strategies for this purpose, from those that play with the topology of the model or the arithmetic precision, e.g. pruning and quantization, to those that operate a model agnostic compression, e.g. weight encoding. The tighter the memory constraint, the higher the probability that these techniques alone cannot meet the requirement, hence more awareness and cooperation across different optimizations become mandatory. This work addresses the issue by introducing EAST, Encoding-Aware Sparse Training, a novel memory-constrained training procedure that leads quantized ConvNets towards deep memory compression. EAST implements an adaptive group pruning designed to maximize the compression rate of the weight encoding scheme (the LZ4 algorithm in this work). If compared to existing methods, EAST meets the memory constraint with lower sparsity, hence ensuring higher accuracy. Results conducted on a state-of-the-art ConvNet (ResNet-9) deployed on a low-power microcontroller (ARM Cortex-M4) validate the proposal.

PARIS – France has officially deployed its first armed drones, three American-built Reapers fitted with laser-guided missiles, in its fight against a jihadi insurrection in Africa's Sahel region, Defense Minister Florence Parly announced Thursday. The drones, which have already since 2014 provided surveillance support to the French anti-jihadi Barkhane mission in Mali, Niger and Burkina Faso, will from now on also be able to strike targets, she said. France joins a small club of countries, including the United States, Britain and Israel, that use armed, distance-piloted aircraft in combat. The Reapers will each carry two 250-kg (550-pound) laser-guided bombs, and are entering service after a series of operational tests carried out from the airbase in the Niger capital Niamey. "Their main missions remain surveillance and intelligence … but these can be extended to strikes," Parly said.

With 51 workshops, 1428 accepted papers, and 13k attendees, saying that NeurIPS is overwhelming is an understatement. I did my best to summarize the key trends I got from the conference. This post is generously edited by the wonderful Andrey Kurenkov. Disclaimer: This post doesn't reflect the view of any of the organizations I'm associated with. NeurIPS is huge with a lot to take in, so I might get something wrong.

Dr. Iain Brown is the Head of Data Science for SAS UK&I and Adjunct Professor of Marketing Analytics at the University of Southampton. For the last decade he has been working closely with the financial services sector, providing thought leadership on the topics of risk, AI and machine learning. During his time at SAS he has been involved in driving innovation in AI and the corresponding fields of machine learning, computer vision and natural language understanding through the delivery of numerous projects. He is also a contributor to SAS' blog and an active member of the AI community on Twitter. In a wide-ranging conversation about the applications of machine learning in the financial services sector, Iain offered some helpful advice around the integration of AI into business models.

Many real world complex systems can be analyzed through a graph/ network prospective. There are two classical and well-known classes of complex networks, scale-fr ee networks and small world networks, which play a significant role in many domains such as social networks, b iology, cognitive science and signal processing [1, 4, 27, 44]. The human genome is a beautiful example of complex dynamic graph. The genome-wide chromosomal conformation (Hi-C) map represents the spatia l proximity of different parts of genome capturing the genome structure over time [40, 42]. When studying s uch dynamic graphs, one is often required to identify the pattern/couple changes including degree distributio n, path lengths, clustering coefficients, etc, in the graph topology in order to capture the dynamics [25, 33, 41]. The von Neumann entropy of a graph, first introduced by Braunst ein et al. [8], is a spectral measure used in structural pattern recognition. The intuition behind this me asure is linking the graph Laplacian to density matrices from quantum mechanics, and measuring the comp lexity of the graphs in terms of the von Neumman entropy of the corresponding density matrices [32]. In ad dition, the measure can be viewed as the information theoretic Shannon entropy, i.e., S null

The thesis contributes in several important ways to the research area of 3D object category learning and recognition. To cope with the mentioned limitations, we look at human cognition, in particular at the fact that human beings learn to recognize object categories ceaselessly over time. This ability to refine knowledge from the set of accumulated experiences facilitates the adaptation to new environments. Inspired by this capability, we seek to create a cognitive object perception and perceptual learning architecture that can learn 3D object categories in an open-ended fashion. In this context, ``open-ended'' implies that the set of categories to be learned is not known in advance, and the training instances are extracted from actual experiences of a robot, and thus become gradually available, rather than being available since the beginning of the learning process. In particular, this architecture provides perception capabilities that will allow robots to incrementally learn object categories from the set of accumulated experiences and reason about how to perform complex tasks. This framework integrates detection, tracking, teaching, learning, and recognition of objects. An extensive set of systematic experiments, in multiple experimental settings, was carried out to thoroughly evaluate the described learning approaches. Experimental results show that the proposed system is able to interact with human users, learn new object categories over time, as well as perform complex tasks. The contributions presented in this thesis have been fully implemented and evaluated on different standard object and scene datasets and empirically evaluated on different robotic platforms.

Deep Learning System Market report offers detailed analysis and a five-year forecast for the global Deep Learning System industry. Deep Learning System market report delivers the insights which will shape your strategic planning as you estimate geographic, product or service expansion within the Deep Learning System industry.. The Deep Learning System market accounted for $XX million in 2018, and is expected to reach $XX million by 2024, registering a CAGR of YY% from 2019 to 2024. The global Deep Learning System market is segmented based on product, end user, and region. Region wise, it is analyzed across North America (U.S., Canada, and Mexico), Europe (Germany, UK, Italy, Spain, France, and rest of Europe), Asia-Pacific (Japan, China, Australia, India, South Korea, Taiwan, and, rest of Asia-Pacific) and EMEA (Brazil, South Africa, Saudi Arabia, UAE, rest of EMEA). Ask more details or request custom reports to our experts at https://www.proaxivereports.com/pre-order/12206 Moreover, other factors that contribute toward the growth of the Deep Learning System market include favorable government initiatives related to the use of Deep Learning System.

Wild animals are experts at staying out of sight, but a new partnership between Google and the conservation organization Wildlife Insights will try to help scientists capture and analyze pictures of them in their natural habitat. The program will use an artificial intelligence program to sort through photographs taken by small sensor driven camera installations placed in wilderness areas around the world. Google's AI and Cloud services will help researchers analyse and archive the enormous volume of images captured through the program as part of an effort to improve animal conservation strategies all around the world. The camera traps were originally developed in 1990 and in the intervening years have been placed everywhere from Mexico to Madagascar. To date, 4.553 million pictures have been taken from 8,209 camera deployments.