D. DeCoste

发表

Training Invariant Support Vector Machines

Bernhard Schölkopf, Dennis DeCoste, B. Schölkopf, 2002, Machine Learning.

Large-scale kernel machines

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

Approximation Methods for Gaussian Process Regression

J. Weston, O. Chapelle, D. DeCoste, 2007 .

Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations

Dennis DeCoste, D. DeCoste, 2006, ICML.

A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs

S. Sathiya Keerthi, Dennis DeCoste, D. DeCoste, 2005, J. Mach. Learn. Res..

HD-CNN: Hierarchical Deep Convolutional Neural Network for Image Classification pdf

Robinson Piramuthu, Dennis DeCoste, Vignesh Jagadeesh, 2014, ArXiv.

Scaling Learning Algorithms toward AI

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

Support Vector Machine Solvers

J. Weston, L. Bottou, O. Chapelle, 2007 .

Building Support Vector Machines with Reduced Classifier Complexity

S. Sathiya Keerthi, Dennis DeCoste, Olivier Chapelle, 2006, J. Mach. Learn. Res..

Compact Random Feature Maps

Dennis DeCoste, Raffay Hamid, Alex Gittens, 2013, ICML.

Alpha seeding for support vector machines

Kiri Wagstaff, Dennis DeCoste, D. DeCoste, 2000, KDD '00.

Visualizing Mercer Kernel feature spaces via kernelized locally-linear embeddings

D. DeCoste, 2001 .

Training Invariant SVMs Using Selective Sampling

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

Large-Scale Kernel Machines (Neural Information Processing)

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

Large-Scale Learning with String Kernels

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

Dynamic Across-Time Measurement Interpretation

Dennis DeCoste, D. DeCoste, Dennis DeCoste, 1990, Artif. Intell..

The Institute for the Learning Sciences TOWARD A QUALITATIVE THEORY OF SAFETY CONTROL - WHEN AND HOW TO PANIC INTELLIGENTLY

D. DeCoste, 2003 .

Goal-directed qualitative reasoning with partial states

D. DeCoste, 1994 .

Dynamic Across-Time Measurement Interpretation: Maintaining Qualitative Understandings of Physical System Behavior

D. DeCoste, 1990 .

Anytime query-tuned kernel machine classifiers via Cholesky factorization

D. DeCoste, 2002 .

Building SVMs with Reduced Classifier Complexity

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

The Improved Fast Gauss Transform with Applications to Machine Learning

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

A Distributed Sequential Solver for Large-Scale SVMs

Jason Weston, Dennis DeCoste, Léon Bottou, 2007 .

Automated Knowledge Discovery from Simulators

Dominic Mazzoni, Dennis DeCoste, Michael C. Burl, 2006, SDM.

Automated Event Detection in Space Instruments: A Case Study Using IPEX-2 Data and Support Vector Ma

Marie B. Levine, Dennis Decoste, D. DeCoste, 2000 .

RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network

D. DeCoste, J. Hestness, Vitaliy Chiley, 2022, ArXiv.

Anytime Query-Tuned Kernel Machines via Cholesky Factorization

Dennis DeCoste, D. DeCoste, 2003, SDM.

Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum

Dennis DeCoste, Oncel Tuzel, Shreyas Saxena, 2019, NeurIPS.

Anytime Interval-Valued Outputs for Kernel Machines: Fast Support Vector Machine Classification via Distance Geometry

Dennis DeCoste, D. DeCoste, 2002, ICML.

Fast Query-Optimized Kernel Machine Classification Via Incremental Approximate Nearest Support Vectors

Dominic Mazzoni, Dennis DeCoste, D. DeCoste, 2003, ICML.

Machine learning for science: state of the art and future prospects.

E Mjolsness, D DeCoste, D. DeCoste, 2001, Science.

HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition

Robinson Piramuthu, Hao Zhang, Yizhou Yu, 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

Visualizing data mining models

Dennis DeCoste, Kurt Thearling, Barry G. Becker, 2001 .

Stochastic Weight Averaging in Parallel: Large-Batch Training that Generalizes Well pdf

Dennis DeCoste, Vipul Gupta, Santiago Akle Serrano, 2020, ICLR.

RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network

D. DeCoste, J. Hestness, Vitaliy Chiley, 2022, ArXiv.

Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation

D. DeCoste, J. Hestness, Vitaliy Chiley, 2021, BrainLes@MICCAI.

Naïve filterbots for robust cold-start recommendations

Nathaniel Good, David M. Pennock, Dennis DeCoste, 2006, KDD '06.

The Future of Chess-Playing Technologies and the Significance of Kasparov Versus deep Blue

Dennis DeCoste, D. DeCoste, 1997, Deep Blue Versus Kasparov: The Significance for Artificial Intelligence.

Making an Impact: Artificial Intelligence at the Jet Propulsion Laboratory

Richard J. Doyle, Dennis DeCoste, Steve A. Chien, 1997, AI Mag..

ic Across-Time Meas

Dennis DeCoste, D. DeCoste, 1990 .

Training With Data Dependent Dynamic Learning Rates pdf

Dennis DeCoste, Nidhi Vyas, Shreyas Saxena, 2021, ArXiv.

SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models

D. DeCoste, S. Saxena, Tianda Li, 2023, UAI.

Fast Approximate Matching of Videos from Hand-Held Cameras for Robust Background Subtraction

Neel Sundaresan, Dennis DeCoste, Raffay Hamid, 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

Fast Approximate Matching of Cell-Phone Videos for Robust Background Subtraction pdf

Neel Sundaresan, Dennis DeCoste, Raffay Hamid, 2014, ArXiv.

An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels

S. Sathiya Keerthi, Dennis DeCoste, Chong Jin Ong, 2004, IEEE Transactions on Neural Networks.

Dense Non-rigid Point-Matching Using Random Projections

Chih-Jen Lin, Dennis DeCoste, Raffay Hamid, 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

Training Invariant Support Vector Machines

D. DeCoste, B. Scholkopf, 2002, Machine Learning.

Dynamic Acioss-Time Measurement Interpretation

D. DeCoste, 1990, AAAI.