Prompt Engineering Three New Best Prompting Practices For Coping With Generative AI Sensitivities
Generative AI is exasperating due to prompt sensitivities, i.e., that the AI misinterprets your entered prompt. Here are ...
IEEE16d
A Spatio-Temporal Approach With Self-Corrective Causal Inference for Flight Delay Prediction
In this paper, we leverage casual inference to precisely model inter-airport relationships and propose a self-corrective spatio-temporal graph neural network (named CausalNet) for flight delay ...
acm.org23d
An AI Learning Hierarchy
a machine limited to rule-based operations could not be expert. 2 Not even an enormous database of common-sense facts could make these systems as smart as experts. Many expert systems are useful ...
marktechpost25d
This AI Paper Introduces a Unified Perspective on the Relationship between Latent Space and Generative Models
Unlike traditional autoencoder approaches, DiGIT separates the training of encoders and decoders, starting with the encoder-only training through a discriminative self-supervised model ... After ...
GitHub26d
causal-machine-learning
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...
IEEE26d
Multiview Subgraph Neural Networks: Self-Supervised Learning With Scarce Labeled Data
To this end, we present a novel self-supervised learning (SSL) framework, called multiview subgraph neural networks ( Muse), for handling the long-range dependencies. In particular, we propose an ...
marktechpost29d
Meta AI Releases Cotracker3: A Semi-Supervised Tracker that Produces Better Results with Unlabelled Data and Simple Architecture
In the light of this situation, unsupervised learning shows great potential. This article delves into a new effort to take over the state of the art in tracking with a semi-supervised approach ...