# Reduced-Precision Computation for Neural Network Training In our previous blog post, we discussed the process of training neural networks (NN) and briefly touched on NN training platforms and related memory bandwidth issues. As we noted, neural network training and inference performance are heavily contingent upon memory bandwidth. This is because the memory system is typically tasked with holding the neural network parameters – weights and biases – along with training data. With hardware optimized
The Case for GOOD Diagrams
We study training one-hidden-layer ReLU networks in the neural tangent kernel (NTK) regime, where the networks' biases are initialized to some constant rather than zero. We prove that under such initialization, the neural network will have sparse activation throughout the entire training process, which enables fast training procedures via some sophisticated computational methods. With such initialization, we show that the neural networks possess a different limiting kernel which we call \textit{bias-general
Miru Tracer, our first step towards mechanistic interpretability, performs confidence analysis and allows interaction with the token generation process.
This guide aims to demystify CNNs, providing insights into their structure, functionality + why they are so effective for image-related tasks
Improving the Generalization of Deep Neural Networks Through Regularization Techniques EasyChair Preprint 15810 12 pages•Date: February 11, 2025 Abstract Deep neural networks (DNNs) have demonstrated impressive performance across various domains, from computer vision to natural language processing. However, they are prone to overfitting, especially when the size of the training data is limited. Regularization techniques play a crucial role in improving the generalization ability of DNNs. In this paper, we
Abstract page for arXiv paper 1611.05128: Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
NeurIPS Proceedings Search Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension Moritz Haas, David Holzmüller, Ulrike V. Luxburg, Ingo Steinwart Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Main Conference Track Abstract The success of over-parameterized neural networks trained to near-zero training error has caused great interest in the phenomenon of benign overfitting, where estimators are statistically consistent even though they interpolate noisy
NeurIPS 2019 Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center Paper ID: 9068 Title: Universality and individuality in neural dynamics across large populations of recurrent networks Reviewer 1 UPDATE after rebuttal: authors have addressed some of my concerns, so I'm updating my score to 8. To summarize, this paper aims to shed light on the connections between artificial recurrent neural networks and biological networks, in order to gain insight into neural circuit functionality through s
# Forecasting with XGBoost embedded in Quasi-Randomized Neural Networks Posted on June 24, 2024 by T. Moudiki in Data science | 0 Comments This article was first published on T. Moudiki's Webpage - Python , and kindly contributed to python-bloggers . (You can report issue about the content on this page here ) Next week, I’ll present nnetsauce ’s (univariate and multivariate probabilistic) time series forecasting capabilities at the 44th International Symposium on Forecasting (ISF) (ISF) 2024. ISF is the