IEEE6d
DSTIGCN: Deformable Spatial-Temporal Interaction Graph Convolution Network for Pedestrian Trajectory Prediction
To address these problems, we propose a Deformable ... through graph convolution and then predict the two-dimensional Gaussian distribution parameters of future trajectories using the Temporal ...
Forget Nvidia: Ndea wants to build AI that keeps improving on its own with ‘no bottlenecks in sight’
Unlike traditional deep learning, which interpolates between data points, program synthesis searches for discrete programs that explain data.
The New York Times1d
The Learning Network
By The Learning Network What do you think this image is communicating? By The Learning Network Choose three to five works of art or culture to group in some way, then tell us why we should ...
GitHub4d
a implement of LSTM using Keras for time series prediction regression problem
we use LSTM network to solve this problem for the implemention of code, we using Keras to establish LSTM network, as well as using numpy, pandas, so before you runing this tutorial, it is strongly ...
CNET1d
Best VPN Service for 2025: Our Top Pick in a Tight Race
Attila is a Staff Writer for CNET, covering software, apps and services with a focus on virtual private networks ... readers advice on how they can use the technology to protect their privacy ...
TechRadar5d
The best mesh Wi-Fi systems in 2025: our favorite systems for home and business use
If you just moved into a bigger home, or you've been in one for some time and have noticed weak spots when it comes to your Wi-Fi connection, you may very well benefit from setting up one of our ...
The Nvidia AI interview: Inside DLSS 4 and machine learning with Bryan Catanzaro
Alex Battaglia of Digital Foundry interviews Bryan Catanzaro, Nvidia's VP of applied deep learning about DLSS 4. How does it ...
IEEE4d
LTN-VINS: A Robust VSLAM in Blurring Environment Based on Lightweight Transformer Network
First, based on modulated deformable convolution network (MDCN) and inertial measurement unit (IMU) pre-integration between image frames, a self-adaptive image PSF estimator (SAPE) is proposed to ...
GitHub4d
Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach (CNN-TA)
In this study, we propose a novel algorithmic trading model CNN-TA using a 2-D convolutional neural network based on image processing properties. In order to convert financial time series into 2-D ...
ITU1d
Network 2030 – Gap analysis of Network 2030 new services, capabilities and use cases
with respect to the Network 2030 services, capabilities, and representative use cases. Based on these inputs, the report identifies gaps, namely issues and technologies that are not currently ...
ITU5d
Network 2030 – Representative use cases and key network requirements for Network 2030
To help identify core network requirements and shape the future networks’ design paradigm, this Technical Report summarizes some representative use cases for Network 2030. Part I presents the ...