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Sample Complexity of Kalman Filtering for Unknown Systems
In this paper, we consider the task of designing a Kalman Filter (KF) fo...
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SGP-DT: Semantic Genetic Programming Based on Dynamic Targets
Semantic GP is a promising approach that introduces semantic awareness d...
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EdgeNets:Edge Varying Graph Neural Networks
Driven by the outstanding performance of neural networks in the structur...
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Finite Sample Analysis of Stochastic System Identification
In this paper, we analyze the finite sample complexity of stochastic sys...
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Monocular 3D Pose Recovery via Nonconvex Sparsity with Theoretical Analysis
For recovering 3D object poses from 2D images, a prevalent method is to ...
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Optimal Algorithms for Submodular Maximization with Distributed Constraints
We consider a class of discrete optimization problems that aim to maximi...
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Obtaining Faithful Interpretations from Compositional Neural Networks
Neural module networks (NMNs) are a popular approach for modeling compos...
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Continual Learning for Sentence Representations Using Conceptors
Distributed representations of sentences have become ubiquitous in natur...
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Foreshadowing the Benefits of Incidental Supervision
Learning theory mostly addresses the standard learning paradigm, assumin...
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Genetic programming approaches to learning fair classifiers
Society has come to rely on algorithms like classifiers for important de...
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Minibatch Processing in Spiking Neural Networks
Spiking neural networks (SNNs) are a promising candidate for biologicall...
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Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates
Driven by a wide range of applications, many principal subspace estimati...
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EventGAN: Leveraging Large Scale Image Datasets for Event Cameras
Event cameras provide a number of benefits over traditional cameras, suc...
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Safe Predictors for Enforcing Input-Output Specifications
We present an approach for designing correct-by-construction neural netw...
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TransOMCS: From Linguistic Graphs to Commonsense Knowledge
Commonsense knowledge acquisition is a key problem for artificial intell...
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Automating Artifact Detection in Video Games
In spite of advances in gaming hardware and software, gameplay is often ...
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Cross-Domain 3D Equivariant Image Embeddings
Spherical convolutional networks have been introduced recently as tools ...
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Equivariant Multi-View Networks
Several approaches to 3D vision tasks process multiple views of the inpu...
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An Exploration of Embodied Visual Exploration
Embodied computer vision considers perception for robots in general, uns...
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On the Capabilities and Limitations of Reasoning for Natural Language Understanding
Recent systems for natural language understanding are strong at overcomi...
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Encoding High-Level Visual Attributes in Capsules for Explainable Medical Diagnoses
Deep neural networks are often called black-boxes due to their difficult...
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Learning Predictive Models From Observation and Interaction
Learning predictive models from interaction with the world allows an age...
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Depth Completion via Deep Basis Fitting
In this paper we consider the task of image-guided depth completion wher...
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Active Learning in Video Tracking
Active learning methods, like uncertainty sampling, combined with probab...
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Causal Feature Discovery through Strategic Modification
We consider an online regression setting in which individuals adapt to t...
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Spin-Weighted Spherical CNNs
Learning equivariant representations is a promising way to reduce sample...
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Predicting with Proxies
Predictive analytics is increasingly used to guide decision-making in ma...
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Semantic variation operators for multidimensional genetic programming
Multidimensional genetic programming represents candidate solutions as s...
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Cross-Lingual Ability of Multilingual BERT: An Empirical Study
Recent work has exhibited the surprising cross-lingual abilities of mult...
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Is deep learning necessary for simple classification tasks?
Automated machine learning (AutoML) and deep learning (DL) are two cutti...
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A Baseline for Few-Shot Image Classification
Fine-tuning a deep network trained with the standard cross-entropy loss ...
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All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks
Image feature matching is a fundamental part of many geometric computer ...
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Not All Claims are Created Equal: Choosing the Right Approach to Assess Your Hypotheses
Empirical research in Natural Language Processing (NLP) has adopted a na...
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Distributed Attack-Robust Submodular Maximization for Multi-Robot Planning
We aim to guard swarm-robotics applications against denial-of-service (D...
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Labeling Panoramas with Spherical Hourglass Networks
With the recent proliferation of consumer-grade 360 cameras, it is worth...
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Robustness Meets Deep Learning: An End-to-End Hybrid Pipeline for Unsupervised Learning of Egomotion
In this work, we propose a method that combines unsupervised deep learni...
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Black Box Submodular Maximization: Discrete and Continuous Settings
In this paper, we consider the problem of black box continuous submodula...
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Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification
CNNs, RNNs, GCNs, and CapsNets have shown significant insights in repres...
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Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
Datasets containing sensitive information are often sequentially analyze...
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Graph Neural Networks for Motion Planning
This paper investigates the feasibility of using Graph Neural Networks (...
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Equal Opportunity in Online Classification with Partial Feedback
We study an online classification problem with partial feedback in which...
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Understanding Spatial Relations through Multiple Modalities
Recognizing spatial relations and reasoning about them is essential in m...
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Question Answering as Global Reasoning over Semantic Abstractions
We propose a novel method for exploiting the semantic structure of text ...
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Joint Reasoning for Temporal and Causal Relations
Understanding temporal and causal relations between events is a fundamen...
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Stochastic optimization approaches to learning concise representations
We propose and study a method for learning interpretable features via st...
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EBIC: an open source software for high-dimensional and big data biclustering analyses
Motivation: In this paper we present the latest release of EBIC, a next-...
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Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes
Multi-output Gaussian processes (GPs) are a flexible Bayesian nonparamet...
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Declarative Learning-Based Programming as an Interface to AI Systems
Data-driven approaches are becoming more common as problem-solving techn...
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Neural Embedding for Physical Manipulations
In common real-world robotic operations, action and state spaces can be ...
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Cross-lingual Entity Alignment for Knowledge Graphs with Incidental Supervision from Free Text
Much research effort has been put to multilingual knowledge graph (KG) e...
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