Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales

Terms and Conditions

Twitter GitHub LinkedIn Bluesky Youtube

© 2026 ResearchTrend.AI, All rights reserved.

Home
Papers
1502.00702
Cited By

Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to
Probe and Learn Neural Networks

v1v2 (latest)

Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks

3 February 2015

ArXiv (abs)PDF HTML

Papers citing "Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks"

4 / 4 papers shown

TensorProjection Layer: A Tensor-Based Dimensionality Reduction Method
in CNN

TensorProjection Layer: A Tensor-Based Dimensionality Reduction Method in CNNNeurocomputing (Neurocomputing), 2020

Toshinari Morimoto

63

1

0

09 Apr 2020

Compression-aware Training of Deep Networks

Compression-aware Training of Deep Networks

Mathieu Salzmann

346

181

0

07 Nov 2017

Feedforward Sequential Memory Networks: A New Structure to Learn
Long-term Dependency

Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency

354

82

0

28 Dec 2015

A Fixed-Size Encoding Method for Variable-Length Sequences with its
Application to Neural Network Language Models

A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models

243

13

0

06 May 2015

Page 1 of 1