Výsledky vyhledávání - "Kumar Satyam"

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Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness

Autor: Kumar, Satyam, Buddi, Sai Srujana, Sarawgi, Utkarsh Oggy, Garg, Vineet, Ranjan, Shivesh, Ognjen, Rudovic, Abdelaziz, Ahmed Hussen, Adya, Saurabh

Voice activity detection (VAD) is a critical component in various applications such as speech recognition, speech enhancement, and hands-free communication systems. With the increasing demand for personalized and context-aware technologies, the need

Externí odkaz: http://arxiv.org/abs/2406.09443

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Causal Inference for Banking Finance and Insurance A Survey

Autor: Kumar, Satyam, Vivek, Yelleti, Ravi, Vadlamani, Bose, Indranil

Causal Inference plays an significant role in explaining the decisions taken by statistical models and artificial intelligence models. Of late, this field started attracting the attention of researchers and practitioners alike. This paper presents a

Externí odkaz: http://arxiv.org/abs/2307.16427

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Application of Causal Inference to Analytical Customer Relationship Management in Banking and Insurance

Autor: Kumar, Satyam, Ravi, Vadlamani

Of late, in order to have better acceptability among various domain, researchers have argued that machine intelligence algorithms must be able to provide explanations that humans can understand causally. This aspect, also known as causability, achiev

Externí odkaz: http://arxiv.org/abs/2208.10916

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Explainable Reinforcement Learning on Financial Stock Trading using SHAP

Autor: Kumar, Satyam, Vishal, Mendhikar, Ravi, Vadlamani

Explainable Artificial Intelligence (XAI) research gained prominence in recent years in response to the demand for greater transparency and trust in AI from the user communities. This is especially critical because AI is adopted in sensitive fields s

Externí odkaz: http://arxiv.org/abs/2208.08790

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Explainable Deep Belief Network based Auto encoder using novel Extended Garson Algorithm

Autor: Kumar, Satyam, Ravi, Vadlamani

The most difficult task in machine learning is to interpret trained shallow neural networks. Deep neural networks (DNNs) provide impressive results on a larger number of tasks, but it is generally still unclear how decisions are made by such a traine

Externí odkaz: http://arxiv.org/abs/2207.08501

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