In the previous article, we explored the reward system in reinforcement learning In this article, we... Tagged with ai, machinelearning.
Common measures of neural representational (dis)similarity are designed to be insensitive to rotations and reflections of the neural activation space. Motivated by the premise that the tuning of individual units may be important, there has been recent interest in developing stricter notions of representational (dis)similarity that require neurons to be individually matched across networks. When two networks have the same size (i.e. same number of neurons), a distance metric can be formulated by optimizing o
Welcome to the Laboratory for Neural Statistics led by Alex Williams at the Center for Neural Science (CNS) at NYU and the Center for Computational Neuroscience (CCN) at the Flatiron Institute. ↓↓ Scroll down to read about our research , our publications , and current lab members ↓↓ Research Overview We develop statistical models and open-source computational tools to extract insights from neural data. We are particularly interested in characterizing flexibility and variability in neural circuits
An artificial neural network developed on an Arduino Uno. Includes tutorial and source code
Implementing a neural network from scratch involves designing its architecture, coding the forward and backward propagat
In recent years, a growing number of computer scientists have tried to develop computational methods inspired by the structure, function and plasticity of neural circuits in the human brain. Achieving a comprehensive understanding of biological neural circuits is of vital importance for the creation of these neuro-inspired computing systems
From image, text to video and audio, GANs are taking the world of data generation to new heights. Uncover the power of Generative Adversarial Networks and future
NeurIPS 2020 MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning Meta Review The paper proposes an approach for incorporating knowledge about symmetries or equivariances into neural network policies by providing a general purpose method for constructing network layers based on knowledge of the relevant transformations. The reviews are generally positive: Identifying effective ways of incorporating prior knowledge of this type into neural networks is an important research challenge that is o
Unveiling the Future of ALS Research: AI's Role in Predicting Neural Network Degeneration The Race Against Time: Unlocking ALS' Secrets with AI Imagine a world where we can predict and potentially halt the progression of a devastating disease like Amyotrophic Lateral Sclerosis (ALS). A groundbreakin
Stochastic gradient descent with a large initial learning rate is widely used for training modern neural net architectures. Although a small initial learning rate allows for faster training and