Datenportal SNF-Kennzahlen Datengeschichten Projektsuche Datensätze Über das Datenportal DE FR EN SNF-Kennzahlen Datengeschichten Projektsuche Datensätze Über das Datenportal DE FR EN Closing the loop: The role of feedback in neural processing and perception 01.05.2025 – 31.07.2025 Zusammenfassung Wissenschaftliches Abstract Feedforward processing is very powerful. For example, convolutional neural networks (CNNs) achieve supra-human accuracy in tasks like object recognition using only a one-way
Sparsity is an issue in neural representation and we think it should be measured in artificial neural networks to understand how they are representing information at each layers. For example, are a few units doing the work or is there a distributed pattern across all units (i.e., overlapping units taking part in the representations of cat, car, etc.). So in What the Success of Brain Imaging Implies about the Neural Code we decided to use the Gini coefficient, inspired by its use in evaluating voxel activati
Collection of Neural Network Visualization Tools
The text discusses the overreliance on neural networks and other algorithms in data analysis, criticizing the lack of innovation and the tendency to focus on minor improvements rather than exploring alternative methods. It also mentions the use of Python code from online sources and the importance of verifying algorithm effectiveness
田中専務 拓海先生、最近部下が『GeoHNN』という論文を推してきまして、現場に導入できるか迷っております。要…
# AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis ### Introduction Generating high-fidelity talking head video by fitting with the input audio sequence is a challenging problem that receives considerable attentions recently. In this paper, we address this problem with the aid of neural scene representation networks. Our method is completely different from existing methods that rely on intermediate representations like 2D landmarks or 3D face models to bridge the gap between audio i
In this work we investigate how to achieve equivariance to input transformations in deep networks, purely from data, without being given a model of those transformations. Convolutional Neural Networks (CNNs), for example, are equivariant to image translation, a transformation that can be easily modelled (by shifting the pixels vertically or horizontally). Other transformations, such as out-of-plane rotations, do not admit a simple analytic model. We propose an auto-encoder architecture whose embedding obeys
Skip to content Search this site - Open Source Platforms Infrastructure Systems Physical Infrastructure Video Engineering & AR/VR Artificial Intelligence Watch Videos POSTED ON MAY 9, 2017 TO AI Research , ML Applications A novel approach to neural machine translation Language translation is important to Facebook’s mission of making the world more open and connected, enabling everyone to consume posts or videos in their preferred language — all at the highest possible accuracy and speed. Today
Abstract page for arXiv paper 2108.05149: Logic Explained Networks
Can neural networks systematically capture discrete, compositional task structure despite their continuous, distributed nature? The impressive capabilities of large scale neural networks suggest that the answer to this question is yes. Howe