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

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

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Link prediction in complex networks: A survey

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

https://people.mpi-inf.mpg.de/~kberberi/publications/2010-mpii-tra.pdf

YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia

https://pure.mpg.de/pubman/item/item_1819068_3/component/file_1840695/MPI-I-2007-5-003.pdf

YAGO: A Large Ontology from Wikipedia and WordNet

RDF data management has received a lot of attention in the past decade due to the widespread growth of Semantic Web and Linked Open Data initiatives. RDF data is expressed in the form of triples (as Subject Predicate Object), with SPARQL used for querying it. Many novel database systems such as RDF-3X, TripleBit, etc. – store RDF in its native form or within traditional relational storage – have demonstrated their ability to scale to large volumes of RDF content. However, it is increasingly becoming obvious from the knowledge representation applications of RDF that it is equally important to integrate with RDF triples additional information such as source, time and place of occurrence, uncertainty, etc. Consider an RDF fact (BarackObama, isPresidentOf, UnitedStates). While this fact is useful for finding information regarding president of United States, it does not provide sufficient information for answering many challenging questions like what is the temporal validity of this fact?, where did this fact come from?, etc. Annotations like confidence, geolocation, time, etc. can be modeled in RDF through a techniques called reification, which is also a W3C recommendations. Reification, retains the triple nature of RDF and associates annotations using blank nodes. The focus of this thesis is on database aspects of storing and querying RDF graphs containing annotations like confidence, etc. on RDF triples. In this thesis, we start by developing an RDF database, named RQ-RDF-3X for efficiently querying these RDF graphs containing annotations over native RDF triples. Next, we noticed that more than 62% facts in real-world RDF datasets like YAGO, DBpedia, etc. have numerical object values. Suggesting the use of queries containing ORDER-BY clause on traditional graph pattern queries of SPARQL. State-of-the-art RDF processing systems such as Virtuoso, Jena, etc. handle such queries by first collecting the results and then sorting them in-memory based on the userspecified function, making them not very scalable. In order to efficiently retrieve results of top-k queries, i.e. queries returning the top-k results ordered by a user-defined scoring function, we developed a top-k query processing database named Quark-X. In Quark-X we propose indexing and query processing techniques for making top-k querying efficient. Motivated by the importance of geo-spatial data in critical applications such as emergency response, transportation, agriculture etc. In addition to its widespread use in knowl-

Indexing and query processing in RDF quad-stores

In modern NLP applications, word embeddings are a crucial backbone that can be read-ily shared across a number of tasks. However as the text distributions change and word semantics evolve over time, the downstream applications using the embeddings can suffer if the word representations do not conform to the data drift. Thus, maintaining word embeddings to be consistent with the underlying data distribution is a key problem. In this work, we tackle this problem and propose TransDrift, a transformer-based prediction model for word embeddings. Leveraging the ﬂexibility of transformer, our model accurately learns the dynamics of the embedding drift and predicts the future embedding. In experiments, we compare with existing methods and show that our model makes signiﬁcantly more accurate predictions of the word embedding than the baselines. Crucially, by applying the predicted embeddings as a backbone for downstream classiﬁcation tasks, we show that our embeddings lead to superior performance compared to the previous methods. We will release the code and datasets at https://github.com/transdrift/

TransDrift: Modeling Word-Embedding Drift using Transformer

Generating counterfactual test-cases is an important backbone for testing NLP models and making them as robust and reliable as traditional software. In generating the test-cases, a desired property is the ability to control the test-case generation in a flexible manner to test for a large variety of failure cases and to explain and repair them in a targeted manner. In this direction, significant progress has been made in the prior works by manually writing rules for generating controlled counterfactuals. However, this approach requires heavy manual supervision and lacks the flexibility to easily introduce new controls. Motivated by the impressive flexibility of the plug-and-play approach of PPLM, we propose bringing the framework of plug-and-play to counterfactual test case generation task. We introduce CASPer, a plug-and-play counterfactual generation framework to generate test cases that satisfy goal attributes on demand. Our plug-and-play model can steer the test case generation process given any attribute model without requiring attribute-specific training of the model. In experiments, we show that CASPer effectively generates counterfactual text that follow the steering provided by an attribute model while also being fluent, diverse and preserving the original content. We also show that the generated counterfactuals from CASPer can be used for augmenting the training data and thereby fixing and making the test model more robust.

Plug and Play Counterfactual Text Generation for Model Robustness

Every user carries their smartphones wherever they go – a crucial aspect ignored by the current models for spatial recommendations. In detail, the current approaches learn the points-of-interest (POI) preferences of a user via the standard spatial features, i.e. the POI coordinates and the social network, and thus ignore the features related to the smartphone usage of a user. Moreover, with growing privacy concerns, users refrain from sharing their exact geographical coordinates as well as their social media activity. In this paper, we present ReVAMP, a sequential POI recommendation approach that uses smartphone app-usage logs to identify the mobility preferences of a user. Our work aligns with the recent psychological studies of online urban users which show that their spatial mobility behavior is largely influenced by the activity of their smartphone apps. Specifically, our proposal of coarse-grained data refers to data logs collected in a privacy-conscious manner consisting only of (a) category of smartphone app-used and (b) category of check-in location. Thus, ReVAMP is not privy to precise geo-coordinates, social networks, and the specific app being used. Buoyed by the efficacy of self-attention models, ReVAMP learns the POI preferences of a user using two forms of positional encodings – absolute and relative – with each extracted from the inter-check-in dynamics in the check-in sequence of a user. Extensive experiments across two large-scale datasets from China show that ReVAMP outperforms the state-of-the-art sequential POI recommendation approaches and can be extended to app- and POI-category prediction.

Modeling Spatial Trajectories Using Coarse-Grained Smartphone Logs

In many applications of Bloom filters, it is possible to exploit the patterns present in the inserted and non-inserted keys to achieve more compression than the standard Bloom filter. A new class of Bloom filters called Learned Bloom filters use machine learning models to exploit these patterns in the data. In practice, these methods and their variants raise many questions: the choice of machine learning models, the training paradigm to achieve the desired results, the choice of thresholds, the number of partitions in case multiple partitions are used, and other such design decisions. In this paper, we present a simple partitioned Bloom filter that works as follows: we partition the Bloom filter into segments, each of which uses a simple projection-based hash function computed using the data. We also provide a theoretical analysis that provides a principled way to select the design parameters of our method: number of hash functions and number of bits per partition. We perform empirical evaluations of our methods on various real-world datasets spanning several applications. We show that it can achieve an improvement in false positive rates of up to two orders of magnitude over standard Bloom filters for the same memory usage, and upto 50% better compression (bytes used per key) for same FPR, and, consistently beats the existing variants of learned Bloom filters.

Srikanta Bedathur

Papers

Indexing and query processing in RDF quad-stores

TransDrift: Modeling Word-Embedding Drift using Transformer

Plug and Play Counterfactual Text Generation for Model Robustness

Modeling Spatial Trajectories Using Coarse-Grained Smartphone Logs

New Wine in an Old Bottle: Data-aware Hash Functions for Bloom Filters