Methods › General › Initialization › Xavier Initialization › Papers, page 2

Xavier Initialization

Papers archive 2025-07-28

archive papers tagged: 121 · with a code link: 51 · where Syntology ran a sample: 12 (7 with a run with no instrument failure, 5 where every run was a failure of Syntology's instrument) Syntology

Show: all tagged papersonly where code ran (12 of 121 tagged: 7 with a run with no instrument failure, 5 where every run was a failure of Syntology's instrument)

Page 2 of 2: papers 101 to 121 of 121, newest first by the archive's date (ties by slug), in archive order.

Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code, as “N ran (of which C constructed an object rather than computing a result; K with no instrument failure: H honoured, V violated, P with no contract checked; I where Syntology's instrument failed) · U unverified”; the instrument figure counts failures of Syntology's instrument, not of the code. It is per sample and not a correctness claim. When the archive marks a repository official for the paper, the line starts with that repository's state (the archive's flag, not a verdict on who wrote the code; “community repositories only” when every sample that ran came from a community repository, “official: no sample here; runs from other or unrecorded repositories” when some came from a repository the paper names or has in its text, or from none recorded); hover it for the repositories the samples that ran came from.

• SqueezeJet: High-level Synthesis Accelerator Design for Deep Convolutional Neural Networks
  6 May 2018 · 0 repositories · arXiv:1805.08695
• SqueezeNext: Hardware-Aware Neural Network Design
  23 Mar 2018 · 8 repositories · arXiv:1803.10615
  Syntology community repositories only · 1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified (of 1 harvested sample)
• Task dependent Deep LDA pruning of neural networks
  21 Mar 2018 · 1 repository · arXiv:1803.08134
• Patch-based Fake Fingerprint Detection Using a Fully Convolutional Neural Network with a Small Number of Parameters and an Optimal Threshold
  21 Mar 2018 · 0 repositories · arXiv:1803.07817
• Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection
  19 Feb 2018 · 1 repository · arXiv:1802.06488
• Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet
  17 Feb 2018 · 1 repository · arXiv:1802.06205
• Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks
  23 Jan 2018 · 1 repository · arXiv:1801.07459
• SquishedNets: Squishing SqueezeNet further for edge device scenarios via deep evolutionary synthesis
  20 Nov 2017 · 0 repositories · arXiv:1711.07459
• Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations
  4 Oct 2017 · 1 repository · arXiv:1710.01494
• Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition
  8 Sep 2017 · 0 repositories · arXiv:1709.02601
• DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices
  16 Aug 2017 · 0 repositories · arXiv:1708.04728
• Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
  10 Jul 2017 · 2 repositories · arXiv:1707.02968
• Using Convolutional Neural Networks in Robots with Limited Computational Resources: Detecting NAO Robots while Playing Soccer
  20 Jun 2017 · 0 repositories · arXiv:1706.06702
• The Compressed Model of Residual CNDS
  15 Jun 2017 · 0 repositories · arXiv:1706.06419
• Deep Convolutional Neural Network Inference with Floating-point Weights and Fixed-point Activations
  8 Mar 2017 · 0 repositories · arXiv:1703.03073
• Fast and Energy-Efficient CNN Inference on IoT Devices
  22 Nov 2016 · 1 repository · arXiv:1611.07151
• FractalNet: Ultra-Deep Neural Networks without Residuals
  24 May 2016 · 4 repositories · arXiv:1605.07648
  Syntology 4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified (of 6 harvested samples) · 1 pointer-only (licence)
• Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks
  20 May 2016 · 2 repositories · arXiv:1605.06402
• Hardware-oriented Approximation of Convolutional Neural Networks
  11 Apr 2016 · 1 repository · arXiv:1604.03168
• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
  24 Feb 2016 · 59 repositories · arXiv:1602.07360
  Syntology community repositories only · 4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified (of 4 harvested samples) · 2 pointer-only (licence)
• Very Deep Convolutional Networks for Large-Scale Image Recognition
  4 Sep 2014 · 305 repositories · arXiv:1409.1556
  Syntology 81 ran (of which 0 constructed an object rather than computing a result; 71 with no instrument failure: 0 honoured, 0 violated, 71 with no contract checked; 10 where Syntology's instrument failed) · 41 unverified (of 122 harvested samples) · 8 pointer-only (licence)