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Found 11 papers, 6 papers with code
Deep Neural Networks via Complex Network Theory: a Perspective
For the novel metrics, in addition to the existing ones, we provide a mathematical formalisation for Fully Connected, AutoEncoder, Convolutional and Recurrent neural networks, of which we vary the activation functions and the number of hidden layers.
Graph-enhanced Large Language Models in Asynchronous Plan Reasoning
Reasoning about asynchronous plans is challenging since it requires sequential and parallel planning to optimize time costs.
Code Simulation Challenges for Large Language Models
We investigate the extent to which Large Language Models (LLMs) can simulate the execution of computer code and algorithms.
Language Models as a Service: Overview of a New Paradigm and its Challenges
This paper has two goals: on the one hand, we delineate how the aforementioned challenges act as impediments to the accessibility, replicability, reliability, and trustworthiness of LMaaS.
Language Model Tokenizers Introduce Unfairness Between Languages
Recent language models have shown impressive multilingual performance, even when not explicitly trained for it.
Emergent Linguistic Structures in Neural Networks are Fragile
In this paper, focusing on the ability of language models to represent syntax, we propose a framework to assess the consistency and robustness of linguistic representations.
Deep Neural Networks as Complex Networks
Deep Neural Networks are, from a physical perspective, graphs whose `links` and `vertices` iteratively process data and solve tasks sub-optimally.
The King is Naked: on the Notion of Robustness for Natural Language Processing
There is growing evidence that the classical notion of adversarial robustness originally introduced for images has been adopted as a de facto standard by a large part of the NLP research community.
Characterizing Learning Dynamics of Deep Neural Networks via Complex Networks
In this paper, we interpret Deep Neural Networks with Complex Network Theory.
On Guaranteed Optimal Robust Explanations for NLP Models
We build on abduction-based explanations for ma-chine learning and develop a method for computing local explanations for neural network models in natural language processing (NLP).
Assessing Robustness of Text Classification through Maximal Safe Radius Computation
Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction.
