要注意的是,这个公式是我们给整个l层定义的,也就是说上面的公式完成了如下图片所做的事情(完成了多次卷积运算) 图源http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/ 因此,实际在计算机上具体计算某一z值时,公式写成以下的形式会更容易理解 z_{ij}^{[l]} =w^{[l]}\otimes g...
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Beginner GAN : Generative Adversarial Nets (Tutorial) Goodfellow & et al. NeurIPS (NIPS) 2016 Tutorial link Beginner CGAN : Conditional Generative Adversarial Nets Mirza & et al. -- 2014 link Beginner InfoGAN : Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets ...
另一类用于化合物活性预测的方法是图卷积模型(graph convolution models),其基本思想是利用神经网络 NNs 自动生成一个分子描述向量,通过训练 NN 来学习向量值。受 Morgan 的圆形指纹方法(circular fingerprint method)启发 [31],Duvenaud 等人提出了神经指纹方法(neural fingerprint method),通过引入图卷积模型将神经指纹作...
Efficient Processing of Deep Neural Networks: A Tutorial and Survey. Proc. IEEE 2017, 105, 2295–2329. [Google Scholar] [CrossRef] [Green Version] Agarap, A.F. Deep learning using rectified linear units (relu). arXiv 2018, arXiv:1803.08375. [Google Scholar] Kepner, J.; Gadepally, V....
🔥🔥🔥 A collection of some awesome public CUDA, cuBLAS, cuDNN, CUTLASS, TensorRT, TensorRT-LLM, Triton, TVM, MLIR and High Performance Computing (HPC) projects. - coderonion/awesome-cuda-triton-hpc
2017-ICML Tutorial: interpretable machine learning 2018-AAAI 2018-ICLR 2018-ICML 2018-ICML Workshop: Efficient Credit Assignment in Deep Learning and Reinforcement Learning 2018-IJCAI 2018-BMVC 2018-NIPS CDNNRIA workshop: Compact Deep Neural Network Representation with Industrial Applications [1st: 2018...
The steps to run PyTorch-Kaldi on the Librispeech dataset are similar to that reported above for TIMIT. The following tutorial is based on the100h sub-set, but it can be easily extended to the full dataset (960h). Run the Kaldi recipe for librispeech at least until Stage 13 (included)...