Zobrazeno 1 - 10
of 139
pro vyhledávání: '"Narodytska, Nina"'
Autor:
Narodytska, Nina, Vargaftik, Shay
Large Language Models (LLMs) have made significant progress in assisting users to query databases in natural language. While LLM-based techniques provide state-of-the-art results on many standard benchmarks, their performance significantly drops when
Externí odkaz:
http://arxiv.org/abs/2407.05153
Autor:
Mangal, Ravi, Narodytska, Nina, Gopinath, Divya, Hu, Boyue Caroline, Roy, Anirban, Jha, Susmit, Pasareanu, Corina
The analysis of vision-based deep neural networks (DNNs) is highly desirable but it is very challenging due to the difficulty of expressing formal specifications for vision tasks and the lack of efficient verification procedures. In this paper, we pr
Externí odkaz:
http://arxiv.org/abs/2403.19837
The demonstrated code-understanding capability of LLMs raises the question of whether they can be used for automated program verification, a task that demands high-level abstract reasoning about program properties that is challenging for verification
Externí odkaz:
http://arxiv.org/abs/2310.04870
We present CrystalBox, a novel, model-agnostic, posthoc explainability framework for Deep Reinforcement Learning (DRL) controllers in the large family of input-driven environments which includes computer systems. We combine the natural decomposabilit
Externí odkaz:
http://arxiv.org/abs/2302.13483
Deep Reinforcement Learning (DRL) has shown promise in various networking environments. However, these environments present several fundamental challenges for standard DRL techniques. They are difficult to explore and exhibit high levels of noise and
Externí odkaz:
http://arxiv.org/abs/2302.12403
Autor:
Izza, Yacine, Huang, Xuanxiang, Ignatiev, Alexey, Narodytska, Nina, Cooper, Martin C., Marques-Silva, Joao
The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it is also the case with approaches based on saliency maps. One solution is to consider intrinsic interpr
Externí odkaz:
http://arxiv.org/abs/2212.05990
The rise of AI methods to make predictions and decisions has led to a pressing need for more explainable artificial intelligence (XAI) methods. One common approach for XAI is to produce a post-hoc explanation, explaining why a black box ML model made
Externí odkaz:
http://arxiv.org/abs/2206.09551
Decision trees (DTs) embody interpretable classifiers. DTs have been advocated for deployment in high-risk applications, but also for explaining other complex classifiers. Nevertheless, recent work has demonstrated that predictions in DTs ought to be
Externí odkaz:
http://arxiv.org/abs/2205.09569
Recently, Graph Neural Networks (GNNs) have been applied for scheduling jobs over clusters, achieving better performance than hand-crafted heuristics. Despite their impressive performance, concerns remain over whether these GNN-based job schedulers m
Externí odkaz:
http://arxiv.org/abs/2203.03153
Estimating the Kullback-Leibler (KL) divergence between two distributions given samples from them is well-studied in machine learning and information theory. Motivated by considerations of multi-group fairness, we seek KL divergence estimates that ac
Externí odkaz:
http://arxiv.org/abs/2202.13576