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NYC-2024-AI 2024 : New York Annual Conference on Artificial Intelligence 2024 | |||||||||||||
Link: https://conferences.americademic.org/NYC-2024-AI/index.html | |||||||||||||
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Call For Papers | |||||||||||||
Topics of interest for submission include but are not limited to:
Natural language processing Information extraction Machine translation Discourse, dialogue and pragmatics Natural language generation Speech recognition Lexical semantics Phonology / morphology Language resources Knowledge representation and reasoning Description logics Semantic networks Nonmonotonic, default reasoning and belief revision Probabilistic reasoning Vagueness and fuzzy logic Causal reasoning and diagnostics Temporal reasoning Cognitive robotics Ontology engineering Logic programming and answer set programming Spatial and physical reasoning Reasoning about belief and knowledge Planning and scheduling Planning for deterministic actions Planning under uncertainty Multi-agent planning Planning with abstraction and generalization Robotic planning Evolutionary robotics Search methodologies Heuristic function construction Discrete space search Continuous space search Randomized search Game tree search Abstraction and micro-operators Search with partial observations Control methods Robotic planning Evolutionary robotics Computational control theory Motion path planning Philosophical/theoretical foundations of artificial intelligence Cognitive science Theory of mind Distributed artificial intelligence Multi-agent systems Intelligent agents Mobile agents Cooperation and coordination Computer vision Computer vision tasks Image and video acquisition Computer vision representations Computer vision problems Machine learning Supervised learning Ranking Supervised learning by classification Supervised learning by regression Structured outputs Cost-sensitive learning Unsupervised learning Cluster analysis Anomaly detection Mixture modeling Topic modeling Source separation Motif discovery Dimensionality reduction and manifold learning Reinforcement learning Sequential decision making Inverse reinforcement learning Apprenticeship learning Multi-agent reinforcement learning Adversarial learning Multi-task learning Transfer learning Lifelong machine learning Learning under covariate shift Learning settings Batch learning Online learning settings Learning from demonstrations Learning from critiques Learning from implicit feedback Active learning settings Semi-supervised learning settings Machine learning approaches Classification and regression trees Kernel methods Support vector machines Gaussian processes Neural networks Logical and relational learning Inductive logic learning Statistical relational learning Learning in probabilistic graphical models Maximum likelihood modeling Maximum entropy modeling Maximum a posteriori modeling Mixture models Latent variable models Bayesian network models Learning linear models Perceptron algorithm Factorization methods Non-negative matrix factorization Factor analysis Principal component analysis Canonical correlation analysis Latent Dirichlet allocation Rule learning Instance-based learning Markov decision processes Partially-observable Markov decision processes Stochastic games Learning latent representations Deep belief networks Bio-inspired approaches Artificial life Evolvable hardware Genetic algorithms Genetic programming Evolutionary robotics Generative and developmental approaches Machine learning algorithms Dynamic programming for Markov decision processes Value iteration Q-learning Policy iteration Temporal difference learning Approximate dynamic programming methods Ensemble methods Boosting Bagging Spectral methods Feature selection Regularization Cross-validation |
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