bluuebunny/crossref_metadata_2025 huggingface dataset:
Cross-Modal Similarity Learning via Pairs, Preferences, and Active Supervision
Authors: Zhen Yi , Rai Piyush , Zha Hongyuan , Carin Lawrence
Published: June 2022

We present a probabilistic framework for learning pairwise similarities between objects belonging to different modalities, such as drugs and proteins, or text and images. Our framework is based on learning a binary code based representation for objects in each modality, and has the following key properties: (i) it can leverage both pairwise as well as easy-to-obtain relative preference based cross-modal constraints, (ii) the probabilistic framework naturally allows querying for the most useful/informative constraints, facilitating an active learning setting (existing methods for cross-modal similarity learning do not have such a mechanism), and (iii) the binary code length is learned from the data. We demonstrate the effectiveness of the proposed approach on two problems that require computing pairwise similarities between cross-modal object pairs: cross-modal link prediction in bipartite graphs, and hashing based cross-modal similarity search.
Approximate MaxEnt Inverse Optimal Control and Its Application for Mental Simulation of Human Interactions
Authors: Huang De-An , Farahmand Amir-massoud , Kitani Kris , Bagnell James
Published: June 2022

Maximum entropy inverse optimal control (MaxEnt IOC) is an effective means of discovering the underlying cost function of demonstrated human activity and can be used to predict human behavior over low-dimensional state spaces (i.e., forecasting of 2D trajectories). To enable inference in very large state spaces, we introduce an approximate MaxEnt IOC procedure to address the fundamental computational bottleneck stemming from calculating the partition function via dynamic programming. Approximate MaxEnt IOC is based on two components: approximate dynamic programming and Monte Carlo sampling. We analyze this approximation approach and provide a finite-sample error upper bound on its excess loss. We validate the proposed method in the context of analyzing dual-agent interactions from video, where we use approximate MaxEnt IOC to simulate mental images of a single agents body pose sequence (a high-dimensional image space). We experiment with sequences image data taken from RGB and RGBD data and show that it is possible to learn cost functions that lead to accurate predictions in high-dimensional problems that were previously intractable.
Self-Paced Curriculum Learning
Authors: Jiang Lu , Meng Deyu , Zhao Qian , Shan Shiguang , Hauptmann Alexander
Published: June 2022

Curriculum learning (CL) or self-paced learning (SPL) represents a recently proposed learning regime inspired by the learning process of humans and animals that gradually proceeds from easy to more complex samples in training. The two methods share a similar conceptual learning paradigm, but differ in specific learning schemes. In CL, the curriculum is predetermined by prior knowledge, and remain fixed thereafter. Therefore, this type of method heavily relies on the quality of prior knowledge while ignoring feedback about the learner. In SPL, the curriculum is dynamically determined to adjust to the learning pace of the leaner. However, SPL is unable to deal with prior knowledge, rendering it prone to overfitting. In this paper, we discover the missing link between CL and SPL, and propose a unified framework named self-paced curriculum leaning (SPCL). SPCL is formulated as a concise optimization problem that takes into account both prior knowledge known before training and the learning progress during training. In comparison to human education, SPCL is analogous to "instructor-student-collaborative" learning mode, as opposed to "instructor-driven" in CL or "student-driven" in SPL. Empirically, we show that the advantage of SPCL on two tasks.
Shift-Pessimistic Active Learning Using Robust Bias-Aware Prediction
Authors: Liu Anqi , Reyzin Lev , Ziebart Brian
Published: June 2022

Existing approaches to active learning are generally optimistic about their certainty with respect to data shift between labeled and unlabeled data. They assume that unknown datapoint labels follow the inductive biases of the active learner. As a result, the most useful datapoint labels—ones that refute current inductive biases—are rarely solicited. We propose a shift-pessimistic approach to active learning that assumes the worst-case about the unknown conditional label distribution. This closely aligns model uncertainty with generalization error, enabling more useful label solicitation. We investigate the theoretical benefits of this approach and demonstrate its empirical advantages on probabilistic binary classification tasks.
Multi-Objective Reinforcement Learning with Continuous Pareto Frontier Approximation
Authors: Pirotta Matteo , Parisi Simone , Restelli Marcello
Published: June 2022

This paper is about learning a continuous approximation of the Pareto frontier in Multi-Objective Markov Decision Problems (MOMDPs).We propose a policy-based approach that exploits gradient information to generate solutions close to the Pareto ones.Differently from previous policy-gradient multi-objective algorithms, where n optimization routines are used to have n solutions, our approach performs a single gradient-ascent run that at each step generates an improved continuous approximation of the Pareto frontier.The idea is to exploit a gradient-based approach to optimize the parameters of a function that defines a manifold in the policy parameter space so that the corresponding image in the objective space gets as close as possible to the Pareto frontier.Besides deriving how to compute and estimate such gradient, we will also discuss the non-trivial issue of defining a metric to assess the quality of the candidate Pareto frontiers.Finally, the properties of the proposed approach are empirically evaluated on two interesting MOMDPs.
Convex Batch Mode Active Sampling via α-Relative Pearson Divergence
Authors: Wang Hanmo , Du Liang , Zhou Peng , Shi Lei , Shen Yi-Dong
Published: June 2022

Active learning is a machine learning technique that trains a classifier after selecting a subset from an unlabeled dataset for labeling and using the selected data for training. Recently, batch mode active learning, which selects a batch of samples to label in parallel, has attracted a lot of attention. Its challenge lies in the choice of criteria used for guiding the search of the optimal batch. In this paper, we propose a novel approach to selecting the optimal batch of queries by minimizing the α-relative Pearson divergence (RPE) between the labeled and the original datasets. This particular divergence is chosen since it can distinguish the optimal batch more easily than other measures especially when available candidates are similar. The proposed objective is a min-max optimization problem, and it is difficult to solve due to the involvement of both minimization and maximization. We find that the objective has an equivalent convex form, and thus a global optimal solution can be obtained. Then the subgradient method can be applied to solve the simplified convex problem. Our empirical studies on UCI datasets demonstrate the effectiveness of the proposed approach compared with the state-of-the-art batch mode active learning methods.
TODTLER: Two-Order-Deep Transfer Learning
Authors: Van Haaren Jan , Kolobov Andrey , Davis Jesse
Published: June 2022

The traditional way of obtaining models from data, inductive learning, has proved itself both in theory and in many practical applications. However, in domains where data is difficult or expensive to obtain, e.g., medicine, deep transfer learning is a more promising technique. It circumvents the model acquisition difficulties caused by scarce data in a target domain by carrying over structural properties of a model learned in a source domain where training data is ample. Nonetheless, the lack of a principled view of transfer learning so far has limited its adoption. In this paper, we address this issue by regarding transfer learning as a process that biases learning in a target domain in favor of patterns useful in a source domain. Specifically, we consider a first-order logic model of the data as an instantiation of a set of second-order templates. Hence, the usefulness of a model is partly determined by the learner's prior distribution over these template sets. The main insight of our work is that transferring knowledge amounts to acquiring a posterior over the second-order template sets by learning in the source domain and using this posterior when learning in the target setting. Our experimental evaluation demonstrates our approach to outperform the existing transfer learning techniques in terms of accuracy and runtime.
Adaptive Sampling with Optimal Cost for Class-Imbalance Learning
Authors: Peng Yuxin
Published: June 2022

Learning from imbalanced data sets is one of the challenging problems in machine learning, which means the number of negative examples is far more than that of positive examples. The main problems of existing methods are: (1) The degree of re-sampling, a key factor greatly affecting performance, needs to be pre-fixed, which is difficult to make the optimal choice; (2) Many useful negative samples are discarded in under-sampling; (3) The effectiveness of algorithm-level methods are limited because they just use the original training data for single classifier. To address the above issues, a novel approach of adaptive sampling with optimal cost is proposed for class-imbalance learning in this paper. The novelty of the proposed approach mainly lies in: adaptively over-sampling the minority positive examples and under-sampling the majority negative examples, forming different sub-classifiers by different subsets of training data with the best cost ratio adaptively chosen, and combining these sub-classifiers according to their accuracy to create a strong classifier. It aims to make full use of the whole training data and improve the performance of class-imbalance learning classifier. The solid experiments are conducted to compare the performance between the proposed approach and 12 state-of-the-art methods on challenging 16 UCI data sets on 3 evaluation metrics, and the results show the proposed approach can achieve superior performance in class-imbalance learning.
Learning Robust Locality Preserving Projection via p-Order Minimization
Authors: Wang Hua , Nie Feiping , Huang Heng
Published: June 2022

Locality preserving projection (LPP) is an effective dimensionality reduction method based on manifold learning, which is defined over the graph weighted squared L2-norm distances in the projected subspace. Since squared L2-norm distance is prone to outliers, it is desirable to develop a robust LPP method. In this paper, motivated by existing studies that improve the robustness of statistical learning models via L1-norm or not-squared L2-norm formulations, we propose a robust LPP (rLPP) formulation to minimize the p-th order of the L2-norm distances, which can better tolerate large outlying data samples because it suppress the introduced biased more than the L1-norm or not squared L2-norm minimizations. However, solving the formulated objective is very challenging because it not only non-smooth but also non-convex. As an important theoretical contribution of this work, we systematically derive an efficient iterative algorithm to solve the general p-th order L2-norm minimization problem, which, to the best of our knowledge, is solved for the first time in literature. Extensive empirical evaluations on the proposed rLPP method have been performed, in which our new method outperforms the related state-of-the-art methods in a variety of experimental settings and demonstrate its effectiveness in seeking better subspaces on both noiseless and noisy data.
Automatic Configuration of Sequential Planning Portfolios
Authors: Seipp Jendrik , Sievers Silvan , Helmert Malte , Hutter Frank
Published: June 2022

Sequential planning portfolios exploit the complementary strengths of different planners. Similarly, automated algorithm configuration tools can customize parameterized planning algorithms for a given type of tasks. Although some work has been done towards combining portfolios and algorithm configuration, the problem of automatically generating a sequential planning portfolio from a parameterized planner for a given type of tasks is still largely unsolved. Here, we present Cedalion, a conceptually simple approach for this problem that greedily searches for the pair of parameter configuration and runtime which, when appended to the current portfolio, maximizes portfolio improvement per additional runtime spent. We show theoretically that Cedalion yields portfolios provably within a constant factor of optimal for the training set distribution. We evaluate Cedalion empirically by applying it to construct sequential planning portfolios based on component planners from the highly parameterized Fast Downward (FD) framework. Results for a broad range of planning settings demonstrate that -- without any knowledge of planning or FD -- Cedalion constructs sequential FD portfolios that rival, and in some cases substantially outperform, manually-built FD portfolios.
Factored Symmetries for Merge-and-Shrink Abstractions
Authors: Sievers Silvan , Wehrle Martin , Helmert Malte , Shleyfman Alexander , Katz Michael
Published: June 2022

Merge-and-shrink heuristics crucially rely on effective reduction techniques, such as bisimulation-based shrinking, to avoid the combinatorial explosion of abstractions. We propose the concept of factored symmetries for merge-and-shrink abstractions based on the established concept of symmetry reduction for state-space search. We investigate under which conditions factored symmetry reduction yields perfect heuristics and discuss the relationship to bisimulation. We also devise practical merging strategies based on this concept and experimentally validate their utility.
Discretization of Temporal Models with Application to Planning with SMT
Authors: Rintanen Jussi
Published: June 2022

The problem of planning or discrete control for timed system has earlier been solved with various constraint-based solution methods, including Constraint Programming, SAT solvers, SAT modulo Theories solvers, and Mixed Integer-Linear Programming. In this work we investigate the encoding of time in such constraint-based representations. A main issue with existing encodings is the necessity to allow arbitrary interleavings of concurrent actions' starting and ending times. The complex combinatorics of this can lead to poor scalability of leading search methods. We show how real or rational time in temporal models can in many practically important cases be replaced by integer time, and how this leads to far simpler encodings of planning as constraints. We demonstrate that the simplified encodings substantially improve the scalability of constraint-based planning.
Multi-Objective MDPs with Conditional Lexicographic Reward Preferences
Authors: Wray Kyle , Zilberstein Shlomo , Mouaddib Abdel-Illah
Published: June 2022

Sequential decision problems that involve multiple objectives are prevalent. Consider for example a driver of a semi-autonomous car who may want to optimize competing objectives such as travel time and the effort associated with manual driving. We introduce a rich model called Lexicographic MDP (LMDP) and a corresponding planning algorithm called LVI that generalize previous work by allowing for conditional lexicographic preferences with slack. We analyze the convergence characteristics of LVI and establish its game theoretic properties. The performance of LVI in practice is tested within a realistic benchmark problem in the domain of semi-autonomous driving. Finally, we demonstrate how GPU-based optimization can improve the scalability of LVI and other value iteration algorithms for MDPs.
An Efficient Forest-Based Tabu Search Algorithm for the Split-delivery Vehicle Routing Problem
Authors: Zhang Zizhen , He Huang , Luo Zhixing , Qin Hu , Guo Songshan
Published: June 2022

The split-delivery vehicle routing problem (SDVRP) is a natural extension of the classical vehicle routing problem (VRP) that allows the same customer to be served by more than one vehicle. This problem is a very challenging combinatorial optimization problem and has attracted much academic attention. To solve it, most of the literature articles adopted heuristic approaches in which the solution is represented by a set of delivery patterns, and the search operators were derived from the traditional VRP operators. Differently, our approach employs the combination of a set of routes and a forest to represent the solution. Several forest-based operators are accordingly introduced. We integrate the new operators into a simple tabu search framework and then demonstrate the efficiency of our approach by conducting experiments on existing benchmark instances.
Improving Exploration in UCT Using Local Manifolds
Authors: Srinivasan Sriram , Talvitie Erik , Bowling Michael
Published: June 2022

Monte-Carlo planning has been proven successful in manysequential decision-making settings, but it suffers from poorexploration when the rewards are sparse. In this paper, weimprove exploration in UCT by generalizing across similarstates using a given distance metric. We show that this algorithm,like UCT, converges asymptotically to the optimalaction. When the state space does not have a natural distancemetric, we show how we can learn a local manifold from thetransition graph of states in the near future. to obtain a distancemetric. On domains inspired by video games, empiricalevidence shows that our algorithm is more sample efficientthan UCT, particularly when rewards are sparse.
Crowdsourced Action-Model Acquisition for Planning
Authors: Zhuo Hankz
Published: June 2022

AI planning techniques often require a given set of action models provided as input. Creating action models is, however, a difficult task that costs much manual effort. The problem of action-model acquisition has drawn a lot of interest from researchers in the past. Despite the success of the previous systems, they are all based on the assumption that there are enough training examples for learning high-quality action models. In many real-world applications, e.g., military operation, collecting a large amount of training examples is often both difficult and costly. Instead of collecting training examples, we assume there are abundant annotators, i.e., the crowd, available to provide information learning action models. Specifically, we first build a set of soft constraints based on the labels (true or false) given by the crowd or annotators. We then builds a set of soft constraints based on the input plan traces. After that we put all the constraints together and solve them using a weighted MAX-SAT solver, and convert the solution of the solver to action models. We finally exhibit that our approach is effective in the experiment.
Lifted Probabilistic Inference for Asymmetric Graphical Models
Authors: Van den Broeck Guy , Niepert Mathias
Published: June 2022

Lifted probabilistic inference algorithms have been successfully applied to a large number of symmetric graphical models. Unfortunately, the majority of real-world graphical models is asymmetric. This is even the case for relational representations when evidence is given. Therefore, more recent work in the community moved to making the models symmetric and then applying existing lifted inference algorithms. However, this approach has two shortcomings. First, all existing over-symmetric approximations require a relational representation such as Markov logic networks. Second, the induced symmetries often change the distribution significantly, making the computed probabilities highly biased. We present a framework for probabilistic sampling-based inference that only uses the induced approximate symmetries to propose steps in a Metropolis-Hastings style Markov chain. The framework, therefore, leads to improved probability estimates while remaining unbiased. Experiments demonstrate that the approach outperforms existing MCMC algorithms.
An Exact Algorithm for Solving Most Relevant Explanation in Bayesian Networks
Authors: Zhu Xiaoyuan , Yuan Changhe
Published: June 2022

Most Relevant Explanation (MRE) is a new inference task in Bayesian networks that finds the most relevant partial instantiation of target variables as an explanation for given evidence by maximizing the Generalized Bayes Factor (GBF). No exact algorithm has been developed for solving MRE previously. This paper fills the void and introduces a breadth-first branch-and-bound MRE algorithm based on a novel upper bound on GBF. The bound is calculated by decomposing the computation of the score to a set of Markov blankets of subsets of evidence variables. Our empirical evaluations show that the proposed algorithm scales up exact MRE inference significantly.
Submodular Surrogates for Value of Information
Authors: Chen Yuxin , Javdani Shervin , Karbasi Amin , Bagnell J. , Srinivasa Siddhartha , Krause Andreas
Published: June 2022

How should we gather information to make effective decisions? A classical answer to this fundamental problem is given by the decision-theoretic value of information. Unfortunately, optimizing this objective is intractable, and myopic (greedy) approximations are known to perform poorly. In this paper, we introduce DiRECt, an efficient yet near-optimal algorithm for nonmyopically optimizing value of information. Crucially, DiRECt uses a novel surrogate objective that is: (1) aligned with the value of information problem (2) efficient to evaluate and (3) adaptive submodular. This latter property enables us to utilize an efficient greedy optimization while providing strong approximation guarantees. We demonstrate the utility of our approach on four diverse case-studies: touch-based robotic localization, comparison-based preference learning, wild-life conservation management, and preference elicitation in behavioral economics. In the first application, we demonstrate DiRECt in closed-loop on an actual robotic platform.
Solving Uncertain MDPs with Objectives that Are Separable over Instantiations of Model Uncertainty
Authors: Adulyasak Yossiri , Varakantham Pradeep , Ahmed Asrar , Jaillet Patrick
Published: June 2022

Markov Decision Problems, MDPs offer an effective mechanism for planning under uncertainty. However, due to unavoidable uncertainty over models, it is difficult to obtain an exact specification of an MDP. We are interested in solving MDPs, where transition and reward functions are not exactly specified. Existing research has primarily focussed on computing infinite horizon stationary policies when optimizing robustness, regret and percentile based objectives. We focus specifically on finite horizon problems with a special emphasis on objectives that are separable over individual instantiations of model uncertainty (i.e., objectives that can be expressed as a sum over instantiations of model uncertainty): (a) First, we identify two separable objectives for uncertain MDPs: Average Value Maximization (AVM) and Confidence Probability Maximisation (CPM). (b) Second, we provide optimization based solutions to compute policies for uncertain MDPs with such objectives. In particular, we exploit the separability of AVM and CPM objectives by employing Lagrangian dual decomposition(LDD). (c) Finally, we demonstrate the utility of the LDD approach on a benchmark problem from the literature.
Acronym Disambiguation Using Word Embedding
Authors: Li Chao , Ji Lei , Yan Jun
Published: June 2022

According to the website AcronymFinder.com which is one of the world's largest and most comprehensive dictionaries of acronyms, an average of 37 new human-edited acronym definitions are added every day. There are 379,918 acronyms with 4,766,899 definitions on that site up to now, and each acronym has 12.5 definitions on average. It is a very important research topic to identify what exactly an acronym means in a given context for document comprehension as well as for document retrieval. In this paper, we propose two word embedding based models for acronym disambiguation. Word embedding is to represent words in a continuous and multidimensional vector space, so that it is easy to calculate the semantic similarity between words by calculating the vector distance. We evaluate the models on MSH Dataset and ScienceWISE Dataset, and both models outperform the state-of-art methods on accuracy. The experimental results show that word embedding helps to improve acronym disambiguation.
Time-Sensitive Opinion Mining for Prediction
Authors: Tu Wenting , Cheung David , Mamoulis Nikos
Published: June 2022

Users commonly use Web 2.0 platforms to post their opinions and their predictions about future events (e.g., the movement of astock). Therefore, opinion mining can be used as a tool for predicting future events. Previous work on opinion mining extracts from the text only the polarity of opinions as sentiment indicators. We observe that a typical opinion post also contains temporal references which can improve prediction. This short paper presents our preliminary work on extracting reference time tagsand integrating them into an opinion mining model, in order to improvethe accuracy of future event prediction. We conduct anexperimental evaluation using a collection of microblogs posted by investors to demonstrate the effectiveness of our approach.
Query Abduction for ELH Ontologies
Authors: Chitsaz Mahsa , Wang Zhe , Wang Kewen
Published: June 2022

With the current upward trend in semantically annotated data, ontology-based data access (OBDA) was formulated to tackle the problem of data integration and query answering, where an ontology is formalized as a description logic TBox. In order to meet usability requirements set by users, efforts have been made to equip OBDA system with explanation facilities. One important explanation tool for DL ontologies, referred to as query abduction, can be formalised as abductive reasoning. In particular, given an ontology and an observation (i.e., a query with an answer), an explanation to the observation is a set of facts that together with the ontology can entail the observation. In this paper, we develop a sound and complete algorithm of query abduction for general conjunctive queries in ELH ontologies. This is achieved through ontology approximation and query rewriting. We implemented a prototypical system using the highly optimized Prolog engine XSB. We evaluated our algorithm over university benchmark ontology and our experimental results show that the system is capable of handling query abduction problems for ontology that has approximately 10 millions ABox assertions.
Accelerating SAT Solving by Common Subclause Elimination
Authors: Yan Yaowei , Gutierrez Chris , Jn-Charles Jeriah , Bao Forrest , Zhang Yuanlin
Published: June 2022

Boolean SATisfiability (SAT) is an important problem in AI. SAT solvers have been effectively used in important industrial applications including automated planning and verification. In this paper, we present novel algorithms for fast SAT solving by employing two common subclause elimination (CSE) approaches. Our motivation is that modern SAT solving techniques can be more efficient on CSE-processed instances. Empirical study shows that CSE can significantly speed up SAT solving.
Graphical Representation of Assumption-Based Argumentation
Authors: Schulz Claudia
Published: June 2022

Since Assumption-Based Argumentation (ABA) was introduced in the nineties,the structure and semantics of an ABA framework have been studied exclusively in logical termswithout any graphical representation.Here, we show how an ABA framework and its complete semantics can be displayed in a graph,clarifying the structure of the ABA framework as well as the resulting complete assumption labellings.Furthermore, we show that such an ABA graph can be used to represent the structureand semantics of a logic program (LP), based on the correspondence between the semantics of a LP and an ABA framework encoding this LP.
On Manipulablity of Random Serial Dictatorship in Sequential Matching with Dynamic Preferences
Authors: Hosseini Hadi , Larson Kate , Cohen Robin
Published: June 2022

We consider the problem of repeatedly matching a set of alternatives to a set of agents in the absence of monetary transfer. We propose a generic framework for evaluating sequential matching mechanisms with dynamic preferences, and show that unlike single-shot settings, the random serial dictatorship mechanism is manipulable.
Efficient Extraction of QBF (Counter)models from Long-Distance Resolution Proofs
Authors: Balabanov Valeriy , Jiang Jie-Hong , Janota Mikolas , Widl Magdalena
Published: June 2022

Many computer science problems can be naturally and compactly expressed using quantified Boolean formulas (QBFs). Evaluating thetruth or falsity of a QBF is an important task, and constructing the corresponding model or countermodel can be as important and sometimes even more useful in practice. Modern search and learning based QBF solvers rely fundamentally on resolution and can be instrumented to produce resolution proofs, from which in turn Skolem-function models and Herbrand-function countermodels can be extracted. These (counter)models are the key enabler of various applications. Not until recently the superiority of long-distanceresolution (LQ-resolution) to short-distance resolution(Q-resolution) was demonstrated. While a polynomial algorithm exists for (counter)model extraction from Q-resolution proofs, it remains open whether it exists forLQ-resolution proofs. This paper settles this open problem affirmatively by constructing a linear-time extraction procedure. Experimental results show the distinct benefits of the proposed method in extracting high quality certificates from some LQ-resolution proofs that are not obtainable from Q-resolution proofs.
SMT-Based Validation of Timed Failure Propagation Graphs
Authors: Bozzano Marco , Cimatti Alessandro , Gario Marco , Micheli Andrea
Published: June 2022

Timed Failure Propagation Graphs (TFPGs) are a formalism used in industry to describe failure propagation in a dynamic partially observable system. TFPGs are commonly used to perform model-based diagnosis. As in any model-based diagnosis approach, however, the quality of the diagnosis strongly depends on the quality of the model. Approaches to certify the quality of the TFPG are limited and mainly rely on testing. In this work we address this problem by leveraging efficient Satisfiability Modulo Theories (SMT) engines to perform exhaustive reasoning on TFPGs. We apply model-checking techniques to certify that a given TFPG satisfies (or not) a property of interest. Moreover, we discuss the problem of refinement and diagnosability testing and empirically show that our technique can be used to efficiently solve them.
Building Strong Semi-Autonomous Systems
Authors: Zilberstein Shlomo
Published: June 2022

The vision of populating the world with autonomous systems that reduce human labor and improve safety is gradually becoming a reality. Autonomous systems have changed the way space exploration is conducted and are beginning to transform everyday life with a range of household products. In many areas, however, there are considerable barriers to the deployment of fully autonomous systems. We refer to systems that require some degree of human intervention in order to complete a task as semi-autonomous systems. We examine the broad rationale for semi-autonomy and define basic properties of such systems. Accounting for the human in the loop presents a considerable challenge for current planning techniques. We examine various design choices in the development of semi-autonomous systems and their implications on planning and execution. Finally, we discuss fruitful research directions for advancing the science of semi-autonomy.
Using Social Relationships to Control Narrative Generation
Authors: Porteous Julie , Charles Fred , Cavazza Marc
Published: June 2022

Narrative generation represents an application domain for AI planning where plan quality is related to properties such as shape of plan trajectory. In our work we have developed a plan-based approach to narrative generation that uses character relationships as a key determinant in controlling plan shape (relationships are key in genres such as serial dramas and soaps). Our approach is implemented in a demonstration Interactive Narrative, called NetworkING, set in the medical drama genre. The system features a user-friendly mechanism for specifying relationships between virtual characters, via a social network and real-time visualisation of generated narratives on a 3D stage.
Multi-View Point Registration via Alternating Optimization
Authors: Yan Junchi , Wang Jun , Zha Hongyuan , Yang Xiaokang , Chu Stephen
Published: June 2022

Multi-view point registration is a relatively less studied problem compared with two-view point registration. Directly applying pairwise registration often leads to matching discrepancy as the mapping between two point sets can be determined either by direct correspondences or by any intermediate point set. Also, the local two-view registration tends to be sensitive to noises. We propose a novel multi-view registration method, where the optimal registration is achieved via an efficient and effective alternating concave minimization process. We further extend our solution to a general case in practice of registration among point sets with different cardinalities. Extensive empirical evaluations of peer methods on both synthetic data and real images suggest our method is robust to large disturbance. In particular, it is shown that our method outperforms peer point matching methods and performs competitively against graph matching approaches. The latter approaches utilize the additional second-order information at the cost of exponentially increased run-time, thus usually being less efficient.
Learning Face Hallucination in the Wild
Authors: Zhou Erjin , Fan Haoqiang , Cao Zhimin , Jiang Yuning , Yin Qi
Published: June 2022

Face hallucination method is proposed to generate high-resolution images from low-resolution ones for better visualization. However, conventional hallucination methods are often designed for controlled settings and cannot handle varying conditions of pose, resolution degree, and blur. In this paper, we present a new method of face hallucination, which can consistently improve the resolution of face images even with large appearance variations. Our method is based on a novel network architecture called Bi-channel Convolutional Neural Network (Bi-channel CNN). It extracts robust face representations from raw input by using deep convolutional network, then adaptively integrates two channels of information (the raw input image and face representations) to predict the high-resolution image. Experimental results show our system outperforms the prior state-of-the-art methods.
Online Detection of Abnormal Events Using Incremental Coding Length
Authors: Dutta Jayanta , Banerjee Bonny
Published: June 2022

We present an unsupervised approach for abnormal event detection in videos. We propose, given a dictionary of features learned from local spatiotemporal cuboids using the sparse coding objective, the abnormality of an event depends jointly on two factors: the frequency of each feature in reconstructing all events (or, rarity of a feature) and the strength by which it is used in reconstructing the current event (or, the absolute coefficient). The Incremental Coding Length (ICL) of a feature is a measure of its entropy gain. Given a dictionary, the ICL computation does not involve any parameter, is computationally efficient and has been used for saliency detection in images with impressive results. In this paper, the rarity of a dictionary feature is learned online as its average energy, a function of its ICL. The proposed approach is applicable to real world streaming videos. Experiments on three benchmark datasets and evaluations in comparison with a number of mainstream algorithms show that the approach is comparable to the state-of-the-art.
Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data
Authors: Liu Juan , Bier Eric , Wilson Aaron , Honda Tomo , Kumar Sricharan , Gilpin Leilani , Guerra-Gomez John , Davies Daniel
Published: June 2022

Detection of fraud, waste, and abuse (FWA) is an important yet difficult problem. In this paper, we describe a system to detect suspicious activities in large healthcare claims datasets. Each healthcare dataset is viewed as a heterogeneous network of patients, doctors, pharmacies, and other entities. These networks can be large, with millions of patients, hundreds of thousands of doctors, and tens of thousands of pharmacies, for example. Graph analysis techniques are developed to find suspicious individuals, suspicious relationships between individuals, unusual changes over time, unusual geospatial dispersion, and anomalous networks within the overall graph structure. The system has been deployed on multiple sites and data sets, both government and commercial, to facilitate the work of FWA investigation analysts.
Named Entity Recognition in Travel-Related Search Queries
Authors: Cowan Brooke , Zethelius Sven , Luk Brittany , Baras Teodora , Ukarde Prachi , Zhang Daodao
Published: June 2022

This paper addresses the problem of named entity recognition (NER) in travel-related search queries. NER is an important step toward a richer understanding of user-generated inputs in information retrieval systems. NER in queries is challenging due to minimal context and few structural clues. NER in restricted-domain queries is useful in vertical search applications, for example following query classification in general search. This paper describes an efficient machine learningbased solution for the high-quality extraction of semantic entities from query inputs in a restricted-domain information retrieval setting. We apply a conditional random field (CRF) sequence model to travel-domain search queries and achieve high-accuracy results. Our approach yields an overall F1 score of 86.4% on a heldout test set, outperforming a baseline score of 82.0% on a CRF with standard features. The resulting NER classifier is currently in use in a real-life travel search engine.
Leveraging Ontologies to Improve Model Generalization Automatically with Online Data Sources
Authors: Janpuangtong Sasin , Shell Dylan
Published: June 2022

This paper describes an end-to-end learning framework that allows a novice to create a model from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. Prudence must be exercised to avoid these hazards: certain conclusions may be supported by extra knowledge if, for example, there are reasons to trust a particular narrower set of hypotheses. This paper adopts the solution of using higher-level knowledge in order to allow this sort of domain knowledge to be inferred automatically, thereby selecting only relevant input attributes and thence constraining the hypothesis space. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. To validate our approach, models of four different problem domains were built using our implementation of the framework. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.
Elementary School Science and Math Tests as a Driver for AI: Take the Aristo Challenge!
Authors: Clark Peter
Published: June 2022

While there has been an explosion of impressive, data-driven AI applications in recent years, machines still largely lack a deeper understanding of the world to answer questions that go beyond information explicitly stated in text, and to explain and discuss those answers. To reach this next generation of AI applications, it is imperative to make faster progress in areas of knowledge, modeling, reasoning, and language. Standardized tests have often been proposed as a driver for such progress, with good reason: Many of the questions require sophisticated understanding of both language and the world, pushing the boundaries of AI, while other questions are easier, supporting incremental progress. In Project Aristo at the Allen Institute for AI, we are working on a specific version of this challenge, namely having the computer pass Elementary School Science and Math exams. Even at this level there is a rich variety of problems and question types, the most difficult requiring significant progress in AI. Here we propose this task as a challenge problem for the community, and are providing supporting datasets. Solutions to many of these problems would have a major impact on the field so we encourage you: Take the Aristo Challenge!
Agreement on Target-Bidirectional LSTMs for Sequence-to-Sequence Learning
Authors: Liu Lemao , Finch Andrew , Utiyama Masao , Sumita Eiichiro
Published: June 2022

Recurrent neural networks, particularly the long short- term memory networks, are extremely appealing for sequence-to-sequence learning tasks. Despite their great success, they typically suffer from a fundamental short- coming: they are prone to generate unbalanced targets with good prefixes but bad suffixes, and thus perfor- mance suffers when dealing with long sequences. We propose a simple yet effective approach to overcome this shortcoming. Our approach relies on the agreement between a pair of target-directional LSTMs, which generates more balanced targets. In addition, we develop two efficient approximate search methods for agreement that are empirically shown to be almost optimal in terms of sequence-level losses. Extensive experiments were performed on two standard sequence-to-sequence trans- duction tasks: machine transliteration and grapheme-to- phoneme transformation. The results show that the proposed approach achieves consistent and substantial im- provements, compared to six state-of-the-art systems. In particular, our approach outperforms the best reported error rates by a margin (up to 9% relative gains) on the grapheme-to-phoneme task.
A Unified Bayesian Model of Scripts, Frames and Language
Authors: Ferraro Francis , Van Durme Benjamin
Published: June 2022

We present the first probabilistic model to capture all levels of the Minsky Frame structure, with the goal of corpus-based induction of scenario definitions. Our model unifies prior efforts in discourse-level modeling with that of Fillmore's related notion of frame, as captured in sentence-level, FrameNet semantic parses; as part of this, we resurrect the coupling among Minsky's frames, Schank's scripts and Fillmore's frames, as originally laid out by those authors. Empirically, our approach yields improved scenario representations, reflected quantitatively in lower surprisal and more coherent latent scenarios.
Fine-Grained Semantic Conceptualization of FrameNet
Authors: Park Jin-woo , Hwang Seung-won , Wang Haixun
Published: June 2022

Understanding verbs is essential for many natural language tasks. Tothis end, large-scale lexical resources such as FrameNet have beenmanually constructed to annotate the semantics of verbs (frames) andtheir arguments (frame elements or FEs) in example sentences.Our goal is to "semantically conceptualize" example sentences by connectingFEs to knowledge base (KB) concepts.For example, connecting Employer FE to company concept in the KB enables the understanding thatany (unseen) company can also be FE examples.However, a naive adoption of existing KB conceptualization technique, focusingon scenarios of conceptualizing a few terms,cannot 1) scale to many FE instances (average of 29.7 instances for all FEs) and 2) leverage interdependence betweeninstances and concepts.We thus propose a scalable k-truss clusteringand a Markov Random Field (MRF) model leveraging interdependence betweenconcept-instance, concept-concept, and instance-instance pairs. Our extensive analysis with real-life data validates that our approachimproves not only the quality of the identified concepts for FrameNet, but alsothat of applications such as selectional preference.
Verb Pattern: A Probabilistic Semantic Representation on Verbs
Authors: Cui Wanyun , Zhou Xiyou , Lin Hangyu , Xiao Yanghua , Wang Haixun , Hwang Seung-won , Wang Wei
Published: June 2022

Verbs are important in semantic understanding of natural language. Traditional verb representations, such as FrameNet, PropBank, VerbNet, focus on verbs' roles. These roles are too coarse to represent verbs' semantics. In this paper, we introduce verb patterns to represent verbs' semantics, such that each pattern corresponds to a single semantic of the verb. First we analyze the principles for verb patterns: generality and specificity. Then we propose a nonparametric model based on description length. Experimental results prove the high effectiveness of verb patterns. We further apply verb patterns to context-aware conceptualization, to show that verb patterns are helpful in semantic-related tasks.
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
Authors: Garg Sahil , Galstyan Aram , Hermjakob Ulf , Marcu Daniel
Published: June 2022

We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations. Our experiments show that our approach yields interaction extraction systems that are more robust in environments where there is a significant mismatch between training and test conditions.
Joint Word Representation Learning Using a Corpus and a Semantic Lexicon
Authors: Bollegala Danushka , Alsuhaibani Mohammed , Maehara Takanori , Kawarabayashi Ken-ichi
Published: June 2022

Methods for learning word representations using large text corpora have received much attention lately due to their impressive performancein numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection.Despite their success, these data-driven word representation learning methods do not considerthe rich semantic relational structure between words in a co-occurring context. On the other hand, already much manual effort has gone into the construction of semantic lexicons such as the WordNetthat represent the meanings of words by defining the various relationships that exist among the words in a language.We consider the question, can we improve the word representations learnt using a corpora by integrating theknowledge from semantic lexicons?. For this purpose, we propose a joint word representation learning method that simultaneously predictsthe co-occurrences of two words in a sentence subject to the relational constrains given by the semantic lexicon.We use relations that exist between words in the lexicon to regularize the word representations learnt from the corpus.Our proposed method statistically significantly outperforms previously proposed methods for incorporating semantic lexicons into wordrepresentations on several benchmark datasets for semantic similarity and word analogy.