A survey on explainable ai for 6g o-ran: Architecture, use cases, challenges and research directions
The recent O-RAN specifications promote the evolution of RAN architecture by function
disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop …
disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop …
Explainable AI in 6G O-RAN: A Tutorial and Survey on Architecture, Use Cases, Challenges, and Future Research
The recent o-ran specifications promote the evolution of ranran architecture by function
disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop …
disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop …
Learning transformer programs
Recent research in mechanistic interpretability has attempted to reverse-engineer
Transformer models by carefully inspecting network weights and activations. However, these …
Transformer models by carefully inspecting network weights and activations. However, these …
Assessing the quality of multiple-choice questions using gpt-4 and rule-based methods
Multiple-choice questions with item-writing flaws can negatively impact student learning and
skew analytics. These flaws are often present in student-generated questions, making it …
skew analytics. These flaws are often present in student-generated questions, making it …
Excelformer: A neural network surpassing gbdts on tabular data
Data organized in tabular format is ubiquitous in real-world applications, and users often
craft tables with biased feature definitions and flexibly set prediction targets of their interests …
craft tables with biased feature definitions and flexibly set prediction targets of their interests …
Defining and quantifying the emergence of sparse concepts in dnns
This paper aims to illustrate the concept-emerging phenomenon in a trained DNN.
Specifically, we find that the inference score of a DNN can be disentangled into the effects of …
Specifically, we find that the inference score of a DNN can be disentangled into the effects of …
Neuro-symbolic interpretable collaborative filtering for attribute-based recommendation
Recommender System (RS) is ubiquitous on today's Internet to provide multifaceted
personalized information services. While an enormous success has been made in pushing …
personalized information services. While an enormous success has been made in pushing …
Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today
Recent investigations show that large language models (LLMs), specifically GPT-4, not only
have remarkable capabilities in common Natural Language Processing (NLP) tasks but also …
have remarkable capabilities in common Natural Language Processing (NLP) tasks but also …
Feature-Enhanced Neural Collaborative Reasoning for Explainable Recommendation
Providing reasonable explanations for a specific suggestion given by the recommender can
help users trust the system more. As logic rule-based inference is concise, transparent, and …
help users trust the system more. As logic rule-based inference is concise, transparent, and …
Can a Deep Learning Model be a Sure Bet for Tabular Prediction?
Data organized in tabular format is ubiquitous in real-world applications, and users often
craft tables with biased feature definitions and flexibly set prediction targets of their interests …
craft tables with biased feature definitions and flexibly set prediction targets of their interests …