
Graph Neural Networks Meet NeuralSymbolic Computing: A Survey and Perspective
Neuralsymbolic computing has now become the subject of interest of both...
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Ensemble Learning of CoarseGrained Molecular Dynamics Force Fields with a Kernel Approach
Gradientdomain machine learning (GDML) is an accurate and efficient app...
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MomentumRNN: Integrating Momentum into Recurrent Neural Networks
Designing deep neural networks is an art that often involves an expensiv...
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OutofDistribution Detection Using Neural Rendering Generative Models
Outofdistribution (OoD) detection is a natural downstream task for dee...
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Learning Differentiable Programs with Admissible Neural Heuristics
We study the problem of learning differentiable functions expressed as p...
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Stock Price Forecasting and Hypothesis Testing Using Neural Networks
In this work we use Recurrent Neural Networks and Multilayer Perceptrons...
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Datadriven superparameterization using deep learning: Experimentation with multiscale Lorenz 96 systems and transferlearning
To make weather/climate modeling computationally affordable, smallscale...
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Using Local Experiences for Global Motion Planning
Samplingbased planners are effective in many realworld applications su...
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Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors
We study the linear subspace fitting problem in the overparameterized se...
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SOLAR: Sparse Orthogonal Learned and Random Embeddings
Dense embedding models are commonly deployed in commercial search engine...
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WaveletFCNN: A Deep Time Series Classification Model for Wind Turbine Blade Icing Detection
Wind power, as an alternative to burning fossil fuels, is plentiful and ...
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LTLf Synthesis with Fairness and Stability Assumptions
In synthesis, assumptions are constraints on the environment that rule o...
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Using Learning Dynamics to Explore the Role of Implicit Regularization in Adversarial Examples
Recent work (Ilyas et al, 2019) suggests that adversarial examples are f...
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A Spline Theory of Deep Networks (Extended Version)
We build a rigorous bridge between deep networks (DNs) and approximation...
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Deep Mesh Projectors for Inverse Problems
We develop a new learningbased approach to illposed inverse problems. ...
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STORM: Foundations of EndtoEnd Empirical Risk Minimization on the Edge
Empirical risk minimization is perhaps the most influential idea in stat...
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Reducing the Representation Error of GAN Image Priors Using the Deep Decoder
Generative models, such as GANs, learn an explicit lowdimensional repre...
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Better accuracy with quantified privacy: representations learned via reconstructive adversarial network
The remarkable success of machine learning, especially deep learning, ha...
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Compressing Gradient Optimizers via CountSketches
Many popular firstorder optimization methods (e.g., Momentum, AdaGrad, ...
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ADDMC: Exact Weighted Model Counting with Algebraic Decision Diagrams
We compute exact literalweighted model counts of CNF formulas. Our algo...
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Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
Continuous control and planning remains a major challenge in robotics an...
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How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?
Deep Reinforcement Learning is a promising paradigm for robotic control ...
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A Simultaneous Transformation and Rounding Approach for Modeling IntegerValued Data
We propose a simple yet powerful framework for modeling integervalued d...
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Semantic Similarity Based Softmax Classifier for ZeroShot Learning
ZeroShot Learning (ZSL) is a classification task where we do not have e...
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A OnePass Private Sketch for Most Machine Learning Tasks
Differential privacy (DP) is a compelling privacy definition that explai...
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HoME: a Household Multimodal Environment
We introduce HoME: a Household Multimodal Environment for artificial age...
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Sparse learning of stochastic dynamic equations
With the rapid increase of available data for complex systems, there is ...
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On HashingBased Approaches to Approximate DNFCounting
Propositional model counting is a fundamental problem in artificial inte...
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SemiSupervised Learning via New Deep Network Inversion
We exploit a recently derived inversion scheme for arbitrary deep neural...
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ZeroShot Learning via CategorySpecific VisualSemantic Mapping
ZeroShot Learning (ZSL) aims to classify a test instance from an unseen...
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FiLM: Visual Reasoning with a General Conditioning Layer
We introduce a generalpurpose conditioning method for neural networks c...
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Skip Connections Eliminate Singularities
Skip connections made the training of very deep networks possible and ha...
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Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer
Dependency graph, as a heterogeneous graph representing the intrinsic re...
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SemiSupervised Learning with the Deep Rendering Mixture Model
Semisupervised learning algorithms reduce the high cost of acquiring la...
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A Probabilistic Framework for Deep Learning
We develop a probabilistic framework for deep learning based on the Deep...
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Learning Visual Reasoning Without Strong Priors
Achieving artificial visual reasoning  the ability to answer imagerela...
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Neural Attribute Machines for Program Generation
Recurrent neural networks have achieved remarkable success at generating...
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Centrality measures for graphons
Graphs provide a natural mathematical abstraction for systems with pairw...
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Scalable and Sustainable Deep Learning via Randomized Hashing
Current deep learning architectures are growing larger in order to learn...
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Arrays of (localitysensitive) Count Estimators (ACE): HighSpeed Anomaly Detection via Cache Lookups
Anomaly detection is one of the frequent and important subroutines deplo...
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Reinforcement Learning applied to Single Neuron
This paper extends the reinforcement learning ideas into the multiagent...
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A Probabilistic Theory of Deep Learning
A grand challenge in machine learning is the development of computationa...
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Learned DAMP: Principled Neural Network based Compressive Image Recovery
Compressive image recovery is a challenging problem that requires fast a...
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DataMining Textual Responses to Uncover Misconception Patterns
An important, yet largely unstudied, problem in student data analysis is...
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Contextaware Sequential Recommendation
Since sequential information plays an important role in modeling user be...
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ShapeFit and ShapeKick for Robust, Scalable Structure from Motion
We introduce a new method for location recovery from pairwise direction...
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A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution
The Poisson distribution has been widely studied and used for modeling u...
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Revisiting Winner Take All (WTA) Hashing for Sparse Datasets
WTA (Winner Take All) hashing has been successfully applied in many larg...
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2Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search
The method of random projections has become a standard tool for machine ...
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GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection
This paper introduces a novel graphanalytic approach for detecting anom...
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Rice University
William Marsh Rice University, commonly known as Rice University, is a private research university in Houston, Texas.