Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Dreossi, Tommaso"'
Autor:
Vin, Eric, Kashiwa, Shun, Rhea, Matthew, Fremont, Daniel J., Kim, Edward, Dreossi, Tommaso, Ghosh, Shromona, Yue, Xiangyu, Sangiovanni-Vincentelli, Alberto L., Seshia, Sanjit A.
We present a major new version of Scenic, a probabilistic programming language for writing formal models of the environments of cyber-physical systems. Scenic has been successfully used for the design and analysis of CPS in a variety of domains, but
Externí odkaz:
http://arxiv.org/abs/2307.03325
Classification of large multivariate time series with strong class imbalance is an important task in real-world applications. Standard methods of class weights, oversampling, or parametric data augmentation do not always yield significant improvement
Externí odkaz:
http://arxiv.org/abs/2110.07460
Autor:
Dreossi, Tommaso, Ballardin, Giorgio, Gupta, Parth, Bakus, Jan, Lin, Yu-Hsiang, Salaka, Vamsi
Publikováno v:
EPTCS 331, 2021, pp. 33-42
The timed position of documents retrieved by learning to rank models can be seen as signals. Signals carry useful information such as drop or rise of documents over time or user behaviors. In this work, we propose to use the logic formalism called Si
Externí odkaz:
http://arxiv.org/abs/2101.05415
Autor:
Fremont, Daniel J., Kim, Edward, Dreossi, Tommaso, Ghosh, Shromona, Yue, Xiangyu, Sangiovanni-Vincentelli, Alberto L., Seshia, Sanjit A.
We propose a new probabilistic programming language for the design and analysis of cyber-physical systems, especially those based on machine learning. Specifically, we consider the problems of training a system to be robust to rare events, testing it
Externí odkaz:
http://arxiv.org/abs/2010.06580
Deep neural networks have been shown to lack robustness to small input perturbations. The process of generating the perturbations that expose the lack of robustness of neural networks is known as adversarial input generation. This process depends on
Externí odkaz:
http://arxiv.org/abs/1903.10033
Autor:
Dreossi, Tommaso, Fremont, Daniel J., Ghosh, Shromona, Kim, Edward, Ravanbakhsh, Hadi, Vazquez-Chanlatte, Marcell, Seshia, Sanjit A.
We present VERIFAI, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components. VERIFAI particularly seeks to address challenges with applying formal methods to perc
Externí odkaz:
http://arxiv.org/abs/1902.04245
Autor:
Fremont, Daniel J., Dreossi, Tommaso, Ghosh, Shromona, Yue, Xiangyu, Sangiovanni-Vincentelli, Alberto L., Seshia, Sanjit A.
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing i
Externí odkaz:
http://arxiv.org/abs/1809.09310
Autor:
Dreossi, Tommaso, Ghosh, Shromona, Yue, Xiangyu, Keutzer, Kurt, Sangiovanni-Vincentelli, Alberto, Seshia, Sanjit A.
We present a novel framework for augmenting data sets for machine learning based on counterexamples. Counterexamples are misclassified examples that have important properties for retraining and improving the model. Key components of our framework inc
Externí odkaz:
http://arxiv.org/abs/1805.06962
Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural
Externí odkaz:
http://arxiv.org/abs/1804.07045
Autor:
Casagrande, Alberto, Dang, Thao, Dorigo, Luca, Dreossi, Tommaso, Piazza, Carla, Pippia, Eleonora
Publikováno v:
In Information and Computation November 2022 289 Part A
