ACL Anthology About Announcements Communication channels Related work Copyright Credits Volunteer Development Feedback Using Citing papers Links in the Anthology Data access All FAQs Details Anthology identifiers Names ORCID iDs DOIs Verified authors Contributions Submissions Corrections Author pages Attachments GitHub Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP Yonatan Belinkov , Sophie Hao , Jaap Jumelet , Najoung Kim , Arya McCarthy , Hosein Mohebbi (Ed
NeurIPS Proceedings Search Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs Etienne Boursier, Loucas PILLAUD-VIVIEN, Nicolas Flammarion Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track Abstract The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution. Yet, despite some recent progress, a complete theory explaining its success is still missing. This article presents, for orthogona
CoSiNE combines deep neural networks with phylogenetic models to predict antibody variant effects while capturing epistatic interactions and enabling targeted affinity
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The text discusses the use of neural networks and machine learning models like MLP and RF for trading strategies, emphasizing the importance of predictor selection, linear independence, and training efficiency. It highlights the challenges of overfitting, the role of time delays, and the need for careful model tuning. The author also mentions experiments on training speed and the potential of neural networks to generalize nonlinear patterns
An in-depth analysis of the core mechanisms and architectural evolution of Convolutional Neural Networks (CNNs), featuring Three.js interactive 3D visualization, two Google Colab labs: MNIST handwritten digit recognition with feature map visualization, and TextCNN for text sentiment classification
Autoplay Autocomplete ## 321. Convolutional Neural Networks in One Dimension 1 .Introduction Get started 1.1 1D convolution for neural networks, part 1: Sliding dot product 1.2 1D convolution for neural networks, part 2: Convolution copies the kernel 1.3 1D convolution for neural networks, part 3: Sliding dot product equations longhand 1.4 1D convolution for neural networks, part 4: Convolution equation 1.5 1D convolution for neural networks, part 5: Backpropagation 1.6 1D convolution for neural n
Non-intrusive load monitoring or energy disaggregation involves estimating the power consumption of individual appliances from measurements of the total power consumption of a home. Deep neural networks have been shown to be effective for energy disaggregation. In this work, we present a deep neural network architecture which achieves state of the art disaggregation performance with substantially improved computational efficiency, reducing model training time by a factor of 32 and prediction time by a facto
# 机器学习如何运作 # 机器学习如何运作 How machine learning works ## 文章列表和翻译进度 英文标题 中文标题 进度 How linear regression works 线性回归 Linear Regression 95% How neural networks work 神经网络 Neural Networks Video How backpropagation works 反向传播 Backpropagation Video How deep learning works 深度学习 Deep Learning Video How convolutional neural networks work 卷积神经网络 Convolutional Neural Networks 95% How recurrent neural networks and
Why do some moments imprint themselves in memory while others vanish without a trace? This meta-analysis uncovers a marked dissociation in the brain’s large-scale networks during memory encoding: networks that impede encoding are largely task-invariant, whereas those that support it are finely tuned to the task at hand. Drawing on fMRI studies using the subsequent memory paradigm, the analysis contrasts neural activity during the encoding of later-remembered versus later-forgotten trials across verbal and