Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

인공고관절의 세라믹 볼 헤드의 기계적 안정성 평가를 위한 3 차원 유한요소 해석

Thank you for downloading an introduction to the theory of point processes. As you may know, people have search hundreds times for their chosen novels like this an introduction to the theory of point processes, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some harmful virus inside their computer. an introduction to the theory of point processes is available in our digital library an online access to it is set as public so you can download it instantly. Our book servers hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the an introduction to the theory of point processes is universally compatible with any devices to read.

An Introduction To The Theory Of Point Processes

Current advances in Artificial Intelligence (AI) and Machine Learning (ML) have achieved unprecedented impact across research communities and industry. Nevertheless, concerns about trust, safety, interpretability and accountability of AI were raised by influential thinkers. Many have identified the need for well-founded knowledge representation and reasoning to be integrated with deep learning and for sound explainability. Neural-symbolic computing has been an active area of research for many years seeking to bring together robust learning in neural networks with reasoning and explainability via symbolic representations for network models. In this paper, we relate recent and early research results in neurosymbolic AI with the objective of identifying the key ingredients of the next wave of AI systems. We focus on research that integrates in a principled way neural network-based learning with symbolic knowledge representation and logical reasoning. The insights provided by 20 years of neural-symbolic computing are shown to shed new light onto the increasingly prominent role of trust, safety, interpretability and accountability of AI. We also identify promising directions and challenges for the next decade of AI research from the perspective of neural-symbolic systems.

Neurosymbolic AI: The 3rd Wave

With the continuous accumulation of users' check‐in data, we can gradually capture users' behavior patterns and mine users' preferences. Based on this, the next point‐of‐interest (POI) recommendation has attracted considerable attention. Its main purpose is to simulate users' behavior habits of check‐in behavior. Then, different types of context information are used to construct a personalized recommendation model. However, the users' check‐in data are extremely sparse, which leads to low performance in personalized model training using recurrent neural network. Therefore, we propose a category‐aware gated recurrent unit (GRU) model to mitigate the negative impact of sparse check‐in data, capture long‐range dependence between user check‐ins and get better recommendation results of POI category. We combine the spatiotemporal information of check‐in data and take the POI category as users' preference to train the model. Also, we develop an attention‐based category‐aware GRU (ATCA‐GRU) model for the next POI category recommendation. The ATCA‐GRU model can selectively utilize the attention mechanism to pay attention to the relevant historical check‐in trajectories in the check‐in sequence. We evaluate ATCA‐GRU using a real‐world data set, named Foursquare. The experimental results indicate that our ATCA‐GRU model outperforms the existing similar methods for next POI recommendation.

An attention‐based category‐aware GRU model for the next POI recommendation

We propose a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning). Every neuron has a meaning as a component of a formula in a weighted real-valued logic, yielding a highly intepretable disentangled representation. Inference is omnidirectional rather than focused on predefined target variables, and corresponds to logical reasoning, including classical first-order logic theorem proving as a special case. The model is end-to-end differentiable, and learning minimizes a novel loss function capturing logical contradiction, yielding resilience to inconsistent knowledge. It also enables the open-world assumption by maintaining bounds on truth values which can have probabilistic semantics, yielding resilience to incomplete knowledge.

Logical Neural Networks

Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other’s physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction etc. In this paper, we focus on the problem of location promotion and develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a joint model called LoCaTe, consisting of (i) user mobility model estimated using kernel density estimates; (ii) a model of the semantics of the location using topic models; and (iii) a model of time-gap between checkins using exponential distribution. We validate our model on a long-term crawl of Foursquare data collected between Jan 2015 – Feb 2016, as well as on publicly available LBSN datasets. Our experiments demonstrate that LoCaTe significantly outperforms state-of-the-art models for the same task.

/pdf/locate-influence-quantification-for-location-promotion-in-1h5o1oh6yq.pdf

LoCaTe: Influence Quantification for Location Promotion in Location-based Social Networks

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called "check-in" profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

Inferring and Exploiting Categories for Next Location Prediction

The location check-ins of users through various location-based services such as Foursquare, Twitter and Facebook Places, generate large traces of geo-tagged events. These event-traces often manifest in hidden (possibly overlapping) communities of users with similar interests. Inferring these implicit communities is crucial for forming user profiles for improvements in recommendation and prediction tasks. Given only time-stamped geo-tagged traces of users, can we find out these implicit communities, and characteristics of the underlying influence network? Can we use this network to improve the next location prediction task? In this paper, we focus on the problem of community detection as well as capturing the underlying diffusion process. We propose CoLAB, based on spatio-temporal point processes for information diffusion in continuous time but discrete space of locations. It simultaneously models the implicit communities of users based on their check-in activities, without making use of their social network connections. CoLAB captures the semantic features of the location, user-to-user influence along with spatial and temporal preferences of users. The latent community of users and model parameters are learnt through stochastic variational inference. To the best of our knowledge, this is the first attempt at jointly modeling the diffusion process with activity-driven implicit communities. We demonstrate CoLAB achieves upto 27% improvements in location prediction task over recent deep point-process based methods on geo-tagged event traces collected from Foursquare check-ins.

Modeling Implicit Communities from Geo-Tagged Event Traces Using Spatio-Temporal Point Processes

Shortest path querying is a fundamental graph problem which is computationally quite challenging when operating over massive scale graphs Recent results have addressed the problem of computing either exact or good approximate shortest path distances efficiently Some of these techniques also return the path corresponding to the estimated shortest path distance fastHowever, none of these techniques work very well when we have additional constraints on the labels associated with edges that constitute the path In this paper, we develop SkIt index structure, which supports a wide range of label constraints on paths, and returns an accurate estimation of the shortest path that satisfies the constraints We conduct experiments over graphs such as social networks, and knowledge graphs that contain millions of nodes/edges, and show that SkIt index is fast, accurate in the estimated distance and has a high recall for paths that satisfy the constraints

http://www.iiitd.edu.in/~ankital/cikm2013.pdf

Ankita Likhyani

Papers

Logical Neural Networks

LoCaTe: Influence Quantification for Location Promotion in Location-based Social Networks

Inferring and Exploiting Categories for Next Location Prediction

Modeling Implicit Communities from Geo-Tagged Event Traces Using Spatio-Temporal Point Processes

Label constrained shortest path estimation