CNNs for deep learning
Explore research on learning and memory, focusing on hippocampal and cortical networks, neural oscillations, eye movements, aging, and human–animal translation
This study from Google Brain introduces the Neural GPU, a convolutional gated recurrent unit network, alongside novel training methods that allow neural ne
Selecting the appropriate architecture for a feedforward neural network is crucial for achieving project objectives
# Trained Quantization Thresholds for Accurate and Efficient Fixed-Point Inference of Deep Neural Networks We propose a method of training quantization thresholds (TQT) for uniform symmetric quantizers using standard backpropagation and gradient descent. Contrary to prior work, we show that a careful analysis of the straight-through estimator for threshold gradients allows for a natural range-precision trade-off leading to better optima. Our quantizers are constrained to use power-of-2 scale-factors and pe
NeurIPS Proceedings Search Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli Advances in Neural Information Processing Systems 33 (NeurIPS 2020) Abstract In suitably initialized wide networks, small learning rates transform deep neural networks (DNNs) into neural tangent kernel (NTK) machines, whose training dynamics
The world of neuroscience is abuzz with the recent discovery that face-selective neurons in the macaque visual cortex dynamically change their tuning properties, challenging long-held assumptions about how the brain processes visual information. This finding, published in Nature, not only sheds ligh...
Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret data dependencies. CA has found applications in fields ranging from epidemiology to social sciences. However, current methods used to perform CA do not scale to large, high-dimensional datasets. By re-interpreting the objective in CA using an information-theoretic tool called the principal inertia components, we demonstrate that performing CA is equivalent to solving a functional optimization problem over the spa
In this article, we will apply the concept of multi-label multi-class classification with neural networks from the last post, to classify movie posters by genre. First we import the usual suspects in python. import numpy as np import pandas as pd import glob import scipy
Deep neural networks using multimodal vision-language models outperform unimodal models in predicting SEEG recordings, identifying sites of multimodal integration in the