The Machine Learning report provides independent information about the Machine Learning industry supported by extensive research on factors such as industry segments size & trends, inhibitors, dynamics, drivers, opportunities & challenges, environment & policy, cost overview, porter's five force analysis, and key companies profiles including business overview and recent development. The research report on Machine Learning market thoroughly investigates historical data of this business sphere to lay out the future roadmap of the industry. The study attempts to predict a long-term picture of the market scenario with respect to the various growth indicators, hindrances, and opportunities that determine the industry expansion. Moreover, the report provides an exhaustive synopsis of the industry at a global and regional level. In addition, it covers the impact of COVID-19 pandemic on the leading industry players and various market segmentations.

As modern neural networks have grown to billions of parameters, meeting tight latency budgets has become increasingly challenging. Approaches like compression, sparsification and network pruning have proven effective to tackle this problem - but they rely on modifications of the underlying network. In this paper, we look at a complimentary approach of optimizing how tensors are mapped to on-chip memory in an inference accelerator while leaving the network parameters untouched. Since different memory components trade off capacity for bandwidth differently, a sub-optimal mapping can result in high latency. We introduce evolutionary graph reinforcement learning (EGRL) - a method combining graph neural networks, reinforcement learning (RL) and evolutionary search - that aims to find the optimal mapping to minimize latency. Furthermore, a set of fast, stateless policies guide the evolutionary search to improve sample-efficiency. We train and validate our approach directly on the Intel NNP-I chip for inference using a batch size of 1. EGRL outperforms policy-gradient, evolutionary search and dynamic programming baselines on BERT, ResNet-101 and ResNet-50. We achieve 28-78% speed-up compared to the native NNP-I compiler on all three workloads.

In this paper, we present an efficient neural network for end-to-end general purpose audio source separation. Specifically, the backbone structure of this convolutional network is the SUccessive DOwnsampling and Resampling of Multi-Resolution Features (SuDoRMRF) as well as their aggregation which is performed through simple one-dimensional convolutions. In this way, we are able to obtain high quality audio source separation with limited number of floating point operations, memory requirements, number of parameters and latency. Our experiments on both speech and environmental sound separation datasets show that SuDoRMRF performs comparably and even surpasses various state-of-the-art approaches with significantly higher computational resource requirements.

Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances. The practical side of theoretical computer science, such as computational complexity, then needs to be addressed. Relevant developments in machine learning research on graphs are surveyed for this purpose. We organize and compare the structures involved with learning to solve combinatorial optimization problems, with a special eye on the telecommunications domain and its continuous development of live and research networks.

Domain reduction is an essential tool for solving the constraint satisfaction problem (CSP). In the binary CSP, neighbourhood substitution consists in eliminating a value if there exists another value which can be substituted for it in each constraint. We show that the notion of neighbourhood substitution can be strengthened in two distinct ways without increasing time complexity. We also show the theoretical result that, unlike neighbourhood substitution, finding an optimal sequence of these new operations is NP-hard.

Recently, abstract argumentation-based models of case-based reasoning ($AA{\text -}CBR$ in short) have been proposed, originally inspired by the legal domain, but also applicable as classifiers in different scenarios, including image classification, sentiment analysis of text, and in predicting the passage of bills in the UK Parliament. However, the formal properties of $AA{\text -}CBR$ as a reasoning system remain largely unexplored. In this paper, we focus on analysing the non-monotonicity properties of a regular version of $AA{\text -}CBR$ (that we call $AA{\text -}CBR_{\succeq}$). Specifically, we prove that $AA{\text -}CBR_{\succeq}$ is not cautiously monotonic, a property frequently considered desirable in the literature of non-monotonic reasoning. We then define a variation of $AA{\text -}CBR_{\succeq}$ which is cautiously monotonic, and provide an algorithm for obtaining it. Further, we prove that such variation is equivalent to using $AA{\text -}CBR_{\succeq}$ with a restricted casebase consisting of all "surprising" cases in the original casebase.

We introduce T-Basis, a novel concept for a compact representation of a set of tensors, each of an arbitrary shape, which is often seen in Neural Networks. Each of the tensors in the set is modeled using Tensor Rings, though the concept applies to other Tensor Networks. Owing its name to the T-shape of nodes in diagram notation of Tensor Rings, T-Basis is simply a list of equally shaped three-dimensional tensors, used to represent Tensor Ring nodes. Such representation allows us to parameterize the tensor set with a small number of parameters (coefficients of the T-Basis tensors), scaling logarithmically with each tensor's size in the set and linearly with the dimensionality of T-Basis. We evaluate the proposed approach on the task of neural network compression and demonstrate that it reaches high compression rates at acceptable performance drops. Finally, we analyze memory and operation requirements of the compressed networks and conclude that T-Basis networks are equally well suited for training and inference in resource-constrained environments and usage on the edge devices.

The graph represents a network of 2,429 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 12 July 2020 at 02:55 UTC. The requested start date was Sunday, 12 July 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 10-day, 2-hour, 41-minute period from Wednesday, 01 July 2020 at 21:15 UTC to Saturday, 11 July 2020 at 23:56 UTC.

The "Artificial Intelligence in Medical Imaging Market" globally is a standout amongst the most emergent and astoundingly approved sectors. This worldwide market has been developing at a higher pace with the development of imaginative frameworks and a developing end-client tendency. Artificial Intelligence in Medical Imaging market reports deliver insight and expert analysis into key consumer trends and behaviour in marketplace, in addition to an overview of the market data and key brands. Artificial Intelligence in Medical Imaging market reports provides all data with easily digestible information to guide every businessman's future innovation and move business forward. This report gives an exhaustive appraisal of the Artificial Intelligence in Medical Imaging market driving components, which are perceived reliant on the requests of end-client, variable changes in the market, preventive components, and administrative understanding.

IBM has acquired robotic process automation firm WDG Automation. The financial terms of the deal were not disclosed. The Brazil-based company will help advance IBM's AI-backed automation efforts, including Watson AIOps and Cloud Pak for Multicloud Management. WDG Automation's portfolio includes RPA, automation, interactive voice response, and chatbots. The acquisition will also help IBM to use WDG Automation's services for client digital transformation efforts and artificial intelligence workloads.