
Graph Neural Networks Meet NeuralSymbolic Computing: A Survey and Perspective
Neuralsymbolic computing has now become the subject of interest of both...
read it

Ensemble Learning of CoarseGrained Molecular Dynamics Force Fields with a Kernel Approach
Gradientdomain machine learning (GDML) is an accurate and efficient app...
read it

MomentumRNN: Integrating Momentum into Recurrent Neural Networks
Designing deep neural networks is an art that often involves an expensiv...
read it

OutofDistribution Detection Using Neural Rendering Generative Models
Outofdistribution (OoD) detection is a natural downstream task for dee...
read it

Learning Differentiable Programs with Admissible Neural Heuristics
We study the problem of learning differentiable functions expressed as p...
read it

Stock Price Forecasting and Hypothesis Testing Using Neural Networks
In this work we use Recurrent Neural Networks and Multilayer Perceptrons...
read it

Datadriven superparameterization using deep learning: Experimentation with multiscale Lorenz 96 systems and transferlearning
To make weather/climate modeling computationally affordable, smallscale...
read it

Using Local Experiences for Global Motion Planning
Samplingbased planners are effective in many realworld applications su...
read it

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...
read it

SOLAR: Sparse Orthogonal Learned and Random Embeddings
Dense embedding models are commonly deployed in commercial search engine...
read it

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 ...
read it

LTLf Synthesis with Fairness and Stability Assumptions
In synthesis, assumptions are constraints on the environment that rule o...
read it

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...
read it

MPBoost: Minipatch Boosting via Adaptive Feature and Observation Sampling
Boosting methods are among the best generalpurpose and offtheshelf ma...
read it

A Spline Theory of Deep Networks (Extended Version)
We build a rigorous bridge between deep networks (DNs) and approximation...
read it

Deep Mesh Projectors for Inverse Problems
We develop a new learningbased approach to illposed inverse problems. ...
read it

STORM: Foundations of EndtoEnd Empirical Risk Minimization on the Edge
Empirical risk minimization is perhaps the most influential idea in stat...
read it

Reducing the Representation Error of GAN Image Priors Using the Deep Decoder
Generative models, such as GANs, learn an explicit lowdimensional repre...
read it

Better accuracy with quantified privacy: representations learned via reconstructive adversarial network
The remarkable success of machine learning, especially deep learning, ha...
read it

Compressing Gradient Optimizers via CountSketches
Many popular firstorder optimization methods (e.g., Momentum, AdaGrad, ...
read it

ADDMC: Exact Weighted Model Counting with Algebraic Decision Diagrams
We compute exact literalweighted model counts of CNF formulas. Our algo...
read it

Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics
Continuous control and planning remains a major challenge in robotics an...
read it

How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?
Deep Reinforcement Learning is a promising paradigm for robotic control ...
read it

A Simultaneous Transformation and Rounding Approach for Modeling IntegerValued Data
We propose a simple yet powerful framework for modeling integervalued d...
read it

Semantic Similarity Based Softmax Classifier for ZeroShot Learning
ZeroShot Learning (ZSL) is a classification task where we do not have e...
read it

A OnePass Private Sketch for Most Machine Learning Tasks
Differential privacy (DP) is a compelling privacy definition that explai...
read it

HoME: a Household Multimodal Environment
We introduce HoME: a Household Multimodal Environment for artificial age...
read it

Sparse learning of stochastic dynamic equations
With the rapid increase of available data for complex systems, there is ...
read it

On HashingBased Approaches to Approximate DNFCounting
Propositional model counting is a fundamental problem in artificial inte...
read it

SemiSupervised Learning via New Deep Network Inversion
We exploit a recently derived inversion scheme for arbitrary deep neural...
read it

ZeroShot Learning via CategorySpecific VisualSemantic Mapping
ZeroShot Learning (ZSL) aims to classify a test instance from an unseen...
read it

FiLM: Visual Reasoning with a General Conditioning Layer
We introduce a generalpurpose conditioning method for neural networks c...
read it

Skip Connections Eliminate Singularities
Skip connections made the training of very deep networks possible and ha...
read it

Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer
Dependency graph, as a heterogeneous graph representing the intrinsic re...
read it

SemiSupervised Learning with the Deep Rendering Mixture Model
Semisupervised learning algorithms reduce the high cost of acquiring la...
read it

A Probabilistic Framework for Deep Learning
We develop a probabilistic framework for deep learning based on the Deep...
read it

Learning Visual Reasoning Without Strong Priors
Achieving artificial visual reasoning  the ability to answer imagerela...
read it

Neural Attribute Machines for Program Generation
Recurrent neural networks have achieved remarkable success at generating...
read it

Centrality measures for graphons
Graphs provide a natural mathematical abstraction for systems with pairw...
read it

Scalable and Sustainable Deep Learning via Randomized Hashing
Current deep learning architectures are growing larger in order to learn...
read it

Arrays of (localitysensitive) Count Estimators (ACE): HighSpeed Anomaly Detection via Cache Lookups
Anomaly detection is one of the frequent and important subroutines deplo...
read it

Reinforcement Learning applied to Single Neuron
This paper extends the reinforcement learning ideas into the multiagent...
read it

A Probabilistic Theory of Deep Learning
A grand challenge in machine learning is the development of computationa...
read it

Learned DAMP: Principled Neural Network based Compressive Image Recovery
Compressive image recovery is a challenging problem that requires fast a...
read it

DataMining Textual Responses to Uncover Misconception Patterns
An important, yet largely unstudied, problem in student data analysis is...
read it

Contextaware Sequential Recommendation
Since sequential information plays an important role in modeling user be...
read it

ShapeFit and ShapeKick for Robust, Scalable Structure from Motion
We introduce a new method for location recovery from pairwise direction...
read it

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...
read it

Revisiting Winner Take All (WTA) Hashing for Sparse Datasets
WTA (Winner Take All) hashing has been successfully applied in many larg...
read it

2Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search
The method of random projections has become a standard tool for machine ...
read it
Rice University
William Marsh Rice University, commonly known as Rice University, is a private research university in Houston, Texas.