IT-University of Copenhagen Home Support to ITU faculty Link opens in a new tab Search content at IT-University of Copenhagen Exploring Deep Learning Models for EEG Neural Decoding Laurits Dixen , Stefan Heinrich , Paolo Burelli Research output: Conference Article in Proceeding or Book/Report chapter › Book chapter › Research › peer-review Abstract Neural decoding is an important method in cognitive neuroscience that aims to decode brain representations from recorded neural activity using a

The secret sharer: evaluating and testing unintended memorization in neural networks Carlini et al., USENIX Security Symposium 2019 This is a really important paper for anyone working with language or generative models, and just in general for anyone interested in understanding some of the broader implications and possible unintended consequences of deep learning. There’s also

New research deciphers the geometric shape of neural representations to explain how our brains efficiently learn and apply knowledge across different tasks

TensorFlow is an open-source software library for data analysis and machine learning. In this blog post, we'll be using it to build a neural network for

10001 ideas Studying Data Science メインナビゲーション 論文読み：Squeeze-and-Excitation Networks 2020年2月20日By Hiro [Machine Learning][paper] , DeepLearning , programming , 機械学習 , 論文 元論文： Squeeze-and-Excitation Networks 最近良くCNN関連のタスクでよく使われる手法なので、論文を読んでみたときのメモ。 ググると他にも詳しい解説記事があるので、あくまでも個人的なメモとして残します。 概要 Squeeze-and

(2022) Madan et al. Nature Machine Intelligence. Object recognition and viewpoint estimation lie at the heart of visual understanding. Recent studies have suggested that convolutional neural networks (CNNs) fail to generalize to out-of-distribution (OOD) category–viewpoint combinations, that is

Hi everyone, I’m seeking suggestion on improving a simple Bernoulli model that uses a two-layer neural network as the link function for the Bernoulli distribution. Initially, I put normal priors on the weight and bias t

A feed forward neural network is the simplest type of artificial neural network, one where information moves in a single direction: from the input layer

A two-part neural network models reward-based training and provides a unified framework in which to study diverse computations that can be compared to electrophysiological recordings from behaving animals

Encoder, decoder and encoder-decoder transformers are the most popular neural network in NLP. Understand the differences and how to use them