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pro vyhledávání: '"SHEKHAR, Shashank"'
We present a substantial extension of our Human-Aware Task Planning framework, tailored for scenarios with intermittent shared execution experiences and significant belief divergence between humans and robots, particularly due to the uncontrollable n
Externí odkaz:
http://arxiv.org/abs/2409.18545
Autor:
Shekhar, Shashank, Kalyani, Sheetal
This work studies the product and ratio statistics of independent and non-identically distributed (i.n.i.d) $ \alpha-\kappa - \mu $ shadowed random variables. We derive the series expression for the probability density function (PDF), cumulative dist
Externí odkaz:
http://arxiv.org/abs/2407.10250
We explore a discrete-time, coined quantum walk on a quantum network where the coherent superposition of walker-moves originates from the unitary interaction of the walker-coin with the qubit degrees of freedom in the quantum network. The walk dynami
Externí odkaz:
http://arxiv.org/abs/2406.01558
Publikováno v:
Journal of Medical Internet Research, Vol 22, Iss 9, p e15916 (2020)
BackgroundIn recent years, online physician-rating websites have become prominent and exert considerable influence on patients’ decisions. However, the quality of these decisions depends on the quality of data that these systems collect. Thus, ther
Externí odkaz:
https://doaj.org/article/7aef0aa48fa1465fa9982b4e157d2bbe
Cellular actin dynamics result from collective action of hundreds of regulatory proteins, majority of which target actin filaments at their barbed ends. Three key actin binding proteins - profilin, cofilin and twinfilin individually depolymerize fila
Externí odkaz:
http://arxiv.org/abs/2311.06457
Counterfactual explanations (CFE) are methods that explain a machine learning model by giving an alternate class prediction of a data point with some minimal changes in its features. It helps the users to identify their data attributes that caused an
Externí odkaz:
http://arxiv.org/abs/2311.12825
Autor:
Bordes, Florian, Shekhar, Shashank, Ibrahim, Mark, Bouchacourt, Diane, Vincent, Pascal, Morcos, Ari S.
Synthetic image datasets offer unmatched advantages for designing and evaluating deep neural networks: they make it possible to (i) render as many data samples as needed, (ii) precisely control each scene and yield granular ground truth labels (and c
Externí odkaz:
http://arxiv.org/abs/2308.03977
The successful analysis of argumentative techniques from user-generated text is central to many downstream tasks such as political and market analysis. Recent argument mining tools use state-of-the-art deep learning methods to extract and annotate ar
Externí odkaz:
http://arxiv.org/abs/2307.01401
Joint-embedding based learning (e.g., SimCLR, MoCo, DINO) and reconstruction-based learning (e.g., BEiT, SimMIM, MAE) are the two leading paradigms for self-supervised learning of vision transformers, but they differ substantially in their transfer p
Externí odkaz:
http://arxiv.org/abs/2304.13089