Rice University

∙ Houston , United States ∙ 0 followers

William Marsh Rice University, commonly known as Rice University, is a private research university in Houston, Texas.

Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

Neural-symbolic computing has now become the subject of interest of both...
02/29/2020 ∙ by Luis Lamb, et al. ∙ 69 ∙ share

read it
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach

Gradient-domain machine learning (GDML) is an accurate and efficient app...
05/04/2020 ∙ by Jiang Wang, et al. ∙ 62 ∙ share

read it
MomentumRNN: Integrating Momentum into Recurrent Neural Networks

Designing deep neural networks is an art that often involves an expensiv...
06/12/2020 ∙ by Tan M. Nguyen, et al. ∙ 62 ∙ share

read it
Out-of-Distribution Detection Using Neural Rendering Generative Models

Out-of-distribution (OoD) detection is a natural downstream task for dee...
07/10/2019 ∙ by Yujia Huang, et al. ∙ 35 ∙ share

read it
Learning Differentiable Programs with Admissible Neural Heuristics

We study the problem of learning differentiable functions expressed as p...
07/23/2020 ∙ by Ameesh Shah, et al. ∙ 20 ∙ share

read it
Stock Price Forecasting and Hypothesis Testing Using Neural Networks

In this work we use Recurrent Neural Networks and Multilayer Perceptrons...
08/28/2019 ∙ by Kerda Varaku, et al. ∙ 15 ∙ share

read it
Data-driven super-parameterization using deep learning: Experimentation with multi-scale Lorenz 96 systems and transfer-learning

To make weather/climate modeling computationally affordable, small-scale...
02/25/2020 ∙ by Ashesh Chattopadhyay, et al. ∙ 14 ∙ share

read it
Using Local Experiences for Global Motion Planning

Sampling-based planners are effective in many real-world applications su...
03/20/2019 ∙ by Constantinos Chamzas, et al. ∙ 12 ∙ share

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...
02/25/2020 ∙ by Yehuda Dar, et al. ∙ 12 ∙ share

read it
SOLAR: Sparse Orthogonal Learned and Random Embeddings

Dense embedding models are commonly deployed in commercial search engine...
08/30/2020 ∙ by Tharun Medini, et al. ∙ 11 ∙ share

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 ...
02/14/2019 ∙ by Binhang Yuan, et al. ∙ 10 ∙ share

read it
LTLf Synthesis with Fairness and Stability Assumptions

In synthesis, assumptions are constraints on the environment that rule o...
12/17/2019 ∙ by Shufang Zhu, et al. ∙ 10 ∙ share

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...
06/19/2020 ∙ by Josue Ortega Caro, et al. ∙ 9 ∙ share

read it
A Spline Theory of Deep Networks (Extended Version)

We build a rigorous bridge between deep networks (DNs) and approximation...
05/17/2018 ∙ by Randall Balestriero, et al. ∙ 6 ∙ share

read it
Deep Mesh Projectors for Inverse Problems

We develop a new learning-based approach to ill-posed inverse problems. ...
05/29/2018 ∙ by Sidharth Gupta, et al. ∙ 6 ∙ share

read it
STORM: Foundations of End-to-End Empirical Risk Minimization on the Edge

Empirical risk minimization is perhaps the most influential idea in stat...
06/25/2020 ∙ by Benjamin Coleman, et al. ∙ 6 ∙ share

read it
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder

Generative models, such as GANs, learn an explicit low-dimensional repre...
01/23/2020 ∙ by Max Daniels, et al. ∙ 5 ∙ share

read it
Better accuracy with quantified privacy: representations learned via reconstructive adversarial network

The remarkable success of machine learning, especially deep learning, ha...
01/25/2019 ∙ by Sicong Liu, et al. ∙ 4 ∙ share

read it
Compressing Gradient Optimizers via Count-Sketches

Many popular first-order optimization methods (e.g., Momentum, AdaGrad, ...
02/01/2019 ∙ by Ryan Spring, et al. ∙ 4 ∙ share

read it
ADDMC: Exact Weighted Model Counting with Algebraic Decision Diagrams

We compute exact literal-weighted model counts of CNF formulas. Our algo...
07/11/2019 ∙ by Jeffrey M. Dudek, et al. ∙ 4 ∙ share

read it
Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics

Continuous control and planning remains a major challenge in robotics an...
08/13/2019 ∙ by Saurabh Daptardar, et al. ∙ 4 ∙ share

read it
How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?

Deep Reinforcement Learning is a promising paradigm for robotic control ...
09/20/2019 ∙ by W. Cannon Lewis II, et al. ∙ 4 ∙ share

read it
A Simultaneous Transformation and Rounding Approach for Modeling Integer-Valued Data

We propose a simple yet powerful framework for modeling integer-valued d...
06/27/2019 ∙ by Daniel R. Kowal, et al. ∙ 1 ∙ share

read it
Semantic Similarity Based Softmax Classifier for Zero-Shot Learning

Zero-Shot Learning (ZSL) is a classification task where we do not have e...
09/10/2019 ∙ by Shabnam Daghaghi, et al. ∙ 1 ∙ share

read it
A One-Pass Private Sketch for Most Machine Learning Tasks

Differential privacy (DP) is a compelling privacy definition that explai...
06/16/2020 ∙ by Benjamin Coleman, et al. ∙ 1 ∙ share

read it
HoME: a Household Multimodal Environment

We introduce HoME: a Household Multimodal Environment for artificial age...
11/29/2017 ∙ by Simon Brodeur, et al. ∙ 0 ∙ share

read it
Sparse learning of stochastic dynamic equations

With the rapid increase of available data for complex systems, there is ...
12/06/2017 ∙ by Lorenzo Boninsegna, et al. ∙ 0 ∙ share

read it
On Hashing-Based Approaches to Approximate DNF-Counting

Propositional model counting is a fundamental problem in artificial inte...
10/14/2017 ∙ by Kuldeep S. Meel, et al. ∙ 0 ∙ share

read it
Semi-Supervised Learning via New Deep Network Inversion

We exploit a recently derived inversion scheme for arbitrary deep neural...
11/12/2017 ∙ by Randall Balestriero, et al. ∙ 0 ∙ share

read it
Zero-Shot Learning via Category-Specific Visual-Semantic Mapping

Zero-Shot Learning (ZSL) aims to classify a test instance from an unseen...
11/16/2017 ∙ by Li Niu, et al. ∙ 0 ∙ share

read it
FiLM: Visual Reasoning with a General Conditioning Layer

We introduce a general-purpose conditioning method for neural networks c...
09/22/2017 ∙ by Ethan Perez, et al. ∙ 0 ∙ share

read it
Skip Connections Eliminate Singularities

Skip connections made the training of very deep networks possible and ha...
01/31/2017 ∙ by A. Emin Orhan, et al. ∙ 0 ∙ share

read it
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer

Dependency graph, as a heterogeneous graph representing the intrinsic re...
08/25/2017 ∙ by Chen Luo, et al. ∙ 0 ∙ share

read it
Semi-Supervised Learning with the Deep Rendering Mixture Model

Semi-supervised learning algorithms reduce the high cost of acquiring la...
12/06/2016 ∙ by Tan Nguyen, et al. ∙ 0 ∙ share

read it
A Probabilistic Framework for Deep Learning

We develop a probabilistic framework for deep learning based on the Deep...
12/06/2016 ∙ by Ankit B. Patel, et al. ∙ 0 ∙ share

read it
Learning Visual Reasoning Without Strong Priors

Achieving artificial visual reasoning - the ability to answer image-rela...
07/10/2017 ∙ by Ethan Perez, et al. ∙ 0 ∙ share

read it
Neural Attribute Machines for Program Generation

Recurrent neural networks have achieved remarkable success at generating...
05/25/2017 ∙ by Matthew Amodio, et al. ∙ 0 ∙ share

read it
Centrality measures for graphons

Graphs provide a natural mathematical abstraction for systems with pairw...
07/28/2017 ∙ by Marco Avella-Medina, et al. ∙ 0 ∙ share

read it
Scalable and Sustainable Deep Learning via Randomized Hashing

Current deep learning architectures are growing larger in order to learn...
02/26/2016 ∙ by Ryan Spring, et al. ∙ 0 ∙ share

read it
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups

Anomaly detection is one of the frequent and important subroutines deplo...
06/20/2017 ∙ by Chen Luo, et al. ∙ 0 ∙ share

read it
Reinforcement Learning applied to Single Neuron

This paper extends the reinforcement learning ideas into the multi-agent...
05/15/2015 ∙ by Zhipeng Wang, et al. ∙ 0 ∙ share

read it
A Probabilistic Theory of Deep Learning

A grand challenge in machine learning is the development of computationa...
04/02/2015 ∙ by Ankit B. Patel, et al. ∙ 0 ∙ share

read it
Learned D-AMP: Principled Neural Network based Compressive Image Recovery

Compressive image recovery is a challenging problem that requires fast a...
04/21/2017 ∙ by Christopher A. Metzler, et al. ∙ 0 ∙ share

read it
Data-Mining Textual Responses to Uncover Misconception Patterns

An important, yet largely unstudied, problem in student data analysis is...
03/24/2017 ∙ by Joshua J. Michalenko, et al. ∙ 0 ∙ share

read it
Context-aware Sequential Recommendation

Since sequential information plays an important role in modeling user be...
09/19/2016 ∙ by Qiang Liu, et al. ∙ 0 ∙ share

read it
ShapeFit and ShapeKick for Robust, Scalable Structure from Motion

We introduce a new method for location recovery from pair-wise direction...
08/07/2016 ∙ by Thomas Goldstein, et al. ∙ 0 ∙ share

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...
08/31/2016 ∙ by David I. Inouye, et al. ∙ 0 ∙ share

read it
Revisiting Winner Take All (WTA) Hashing for Sparse Datasets

WTA (Winner Take All) hashing has been successfully applied in many larg...
12/06/2016 ∙ by Beidi Chen, et al. ∙ 0 ∙ share

read it
2-Bit Random Projections, NonLinear Estimators, and Approximate Near Neighbor Search

The method of random projections has become a standard tool for machine ...
02/21/2016 ∙ by Ping Li, et al. ∙ 0 ∙ share

read it
GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

This paper introduces a novel graph-analytic approach for detecting anom...
02/02/2016 ∙ by Christopher R. Harshaw, et al. ∙ 0 ∙ share

read it

Rice University

Featured Researchers