Analyse magnitude-based neural network pruning online. Get per-layer sparsity, compression ratios, and sensitivity curves with R code
Featured Papers Events Sponsors Help Me Schedule Schedule Community Townhall Socials Mentoring Diversity Meetups Gather Cafe Rocket.Chat Featured Invited Talks Awards Orals Papers Browse Visualization Events Workshops Tutorials Demos Competitions Covid19 Symposium Memorials Sponsors Expo Sponsor Hall Help FAQ Committee Help Desk Me Profile Registration STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks Poster Session 4 ( more posters ) on 2020-12-09T09:00:00-08:00 - 2020
Listening in on neural activity is a promising way of restoring the ability to communicate for people whose bodies no longer can. Artificial neural networks are the key middleman in the process
# "My understanding is that [8qg neural nets alrea..." 8qf.json https://arbital.com/p/8qf by Travis Rivera Oct 7 2017 - Index - "My understanding is that [8qg neural nets alrea..." - Some Thoughts on Deep Neural Networks and Handwritten digit recognition - … > However, this sounds a little bit strange and anti\-intuitive: do we really need to map everything into such a high\-dimensional space, in order to just classify 10 different digits? Neural networks seems to be somehow a mimic of brain, but my b
Train object detection model without having any bounding boxes labels. This post shows the power of Explainable AI.
Graph Neural Networks (GNNs), which generalize the deep neural networks to graph-structured data, have achieved great success in modeling graphs. However, as an extension of deep learning for graphs, GNNs lack explainability, which largely limits their adoption in scenarios that demand the transparency of models. Though many efforts are taken to improve the explainability of deep learning, they mainly focus on i.i.d data, which cannot be directly applied to explain the predictions of GNNs because GNNs utili
Squeeze-and-Excitation Networks 提出了 SENet，进一步提高了 ResNet 的表达能力。 对于由卷积神经网络（Convolutional Neural Networks）得到的特征图（Feature Map），其每一层通道（Channel）由上一个特征图所有通道经过卷积操作然后加权相加得到。不同通道由不同组独立的参数得到，这些参数在当前层并无直接交互，互不影响。 且卷积操作是局部的，而 SENet 用全局的 Global pooling 操作计算权值
# ICLR 2023 firstbacksecondback Search All 2023 Events #### 24 Results Poster Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks Guangji Bai ⋅ Chen Ling ⋅ Liang Zhao Poster Wed 7:30 Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation David Lipshutz ⋅ Cengiz Pehlevan ⋅ Dmitri Chklovskii Workshop Stability of implicit neural networks for long-term forecasting in dynamical systems Léon Migus ⋅ Julien Salomon ⋅ patrick gallinari Pos
Bouyed by the surprisingly good performance of neural networks with quantization aware training on the CH32V003, I wondered how far this can be pushed. How much can we compress a neural network while still achieving good test accuracy on the MNIST dataset? When it comes to absolutely low-end microcontrollers, there is hardly a more compelling
This event is in Santa Clara, CA this Wednesday night at 6:30PM. Please RSVP if you are showing up in person. I am not certain this live-stream is going to happen because of several unknown complicating factors. But I a…