**Session-based Recommendation with Graph Neural Network (SR-GNN)**

The problem of session-based recommendation aims to predict users’ actions based on anonymous sessions. Previous methods on the session-based recommendation most model a session as a sequence and capture users’ preference to make recommendations. Though achieved promising results, they fail to consider the complex items transitions among all session sequences, and are insufficient to obtain accurate users’ preference in the session. To better capture the structure of the user-click sessions and take complex transitions of items into account, we propose a novel method, i.e. Session-based Recommendation with Graph Neural Networks, SR-GNN for brevity. In the proposed method, session sequences are aggregated together and modeled as graph-structure data. Based on this graph, GNN can capture complex transitions of items, which are difficult to be revealed by the conventional sequential methods. Each session is then represented as the composition of the global preference and current interests of the session using an attention network. Extensive experiments conducted on two real datasets show that SR-GNN evidently outperforms the state-of-the-art session-based recommendation methods and always obtain stable performance with different connection schemes, session representations, and session lengths. … **GENESYS**

Modern deep learning systems rely on (a) a hand-tuned neural network topology, (b) massive amounts of labeled training data, and (c) extensive training over large-scale compute resources to build a system that can perform efficient image classification or speech recognition. Unfortunately, we are still far away from implementing adaptive general purpose intelligent systems which would need to learn autonomously in unknown environments and may not have access to some or any of these three components. Reinforcement learning and evolutionary algorithm (EA) based methods circumvent this problem by continuously interacting with the environment and updating the models based on obtained rewards. However, deploying these algorithms on ubiquitous autonomous agents at the edge (robots/drones) demands extremely high energy-efficiency due to (i) tight power and energy budgets, (ii) continuous/lifelong interaction with the environment, (iii) intermittent or no connectivity to the cloud to run heavy-weight processing. To address this need, we present GENESYS, an HW-SW prototype of an EA-based learning system, that comprises a closed loop learning engine called EvE and an inference engine called ADAM. EvE can evolve the topology and weights of neural networks completely in hardware for the task at hand, without requiring hand-optimization or backpropagation training. ADAM continuously interacts with the environment and is optimized for efficiently running the irregular neural networks generated by EvE. GENESYS identifies and leverages multiple unique avenues of parallelism unique to EAs that we term ‘gene’- level parallelism, and ‘population’-level parallelism. We ran GENESYS with a suite of environments from OpenAI gym and observed 2-5 orders of magnitude higher energy-efficiency over state-of-the-art embedded and desktop CPU and GPU systems. … **Pedometrics**

Pedometrics is a branch of soil science that applies mathematical and statistical methods for the study of the distribution and genesis of soils. The goal of pedometrics is to achieve a better understanding of the soil as a phenomenon that varies over different scales in space and time. This understanding is important, both for improved soil management and for our scientific appreciation of the soil and the systems (agronomic, ecological and hydrological) of which it is a part. For this reason much of pedometrics is concerned with predicting the properties of the soil in space and time, with sampling and monitoring the soil and with modelling the soil’s behaviour. Pedometricians are typically engaged in developing and applying quantitative methods to apply to these problems. These include geostatistical methods for spatial prediction, sampling designs and strategies, linear modelling methods and novel mathematical and computational techniques such as wavelet transforms, data mining and fuzzy logic. … **Hereditary Independence Gap**

The independence gap of a graph was introduced by Ekim et al. (2018) as a measure of how far a graph is from being well-covered. It is defined as the difference between the maximum and minimum size of a maximal independent set. We investigate the independence gap of a graph from structural and algorithmic points of view, with a focus on classes of perfect graphs. Generalizing results on well-covered graphs due to Dean and Zito (1994) and Hujdurovi\’c et al. (2018), we express the independence gap of a perfect graph in terms of clique partitions and use this characterization to develop a polynomial-time algorithm for recognizing graphs of constant independence gap in any class of perfect graphs of bounded clique number. Next, we introduce a hereditary variant of the parameter, which we call hereditary independence gap and which measures the maximum independence gap over all induced subgraphs of the graph. We show that determining whether a given graph has hereditary independence gap at most $k$ is polynomial-time solvable if $k$ is fixed and co-NP-complete if $k$ is part of input. We also investigate the complexity of the independent set problem in graph classes related to independence gap, showing that the problem is NP-complete in the class of graphs of independence gap at most one and polynomial-time solvable in any class of graphs with bounded hereditary independence gap. Combined with some known results on claw-free graphs, our results imply that the independent domination problem is solvable in polynomial time. …

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May 2020

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