A new approach to goals-based wealth management

…, A Radhakrishnan, D Srivastav - Available at SSRN …, 2018 - papers.ssrn.com
We introduce a novel framework for goals-based wealth management (GBWM), where risk
is understood as the probability of investors not attaining their goals, not just the standard …

Dynamic portfolio allocation in goals-based wealth management

…, D Ostrov, A Radhakrishnan, D Srivastav - Computational …, 2020 - Springer
We report a dynamic programming algorithm which, given a set of efficient (or even inefficient)
portfolios, constructs an optimal portfolio trading strategy that maximizes the probability of …

Dynamic optimization for multi-goals wealth management

…, D Ostrov, A Radhakrishnan, D Srivastav - Journal of Banking & …, 2022 - Elsevier
We develop a dynamic programming methodology that seeks to maximize investor outcomes
over multiple, potentially competing goals (such as upgrading a home, paying college …

[HTML][HTML] Artificial intelligence to deep learning: machine intelligence approach for drug discovery

R Gupta, D Srivastava, M Sahu, S Tiwari, RK Ambasta… - Molecular …, 2021 - Springer
… Machine learning and deep learning algorithms have been … implementation of artificial
intelligence and deep learning in this field. … In summary, artificial intelligence and deep learning …

[HTML][HTML] A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities

PK Mall, PK Singh, S Srivastav, V Narayan… - Healthcare …, 2023 - Elsevier
Artificial Intelligence (AI) solutions have been widely used in healthcare, and recent
developments in deep neural networks have contributed to significant advances in medical image …

A survey of deep learning techniques for vehicle detection from UAV images

S Srivastava, S Narayan, S Mittal - Journal of Systems Architecture, 2021 - Elsevier
… This paper presents a survey of deep learning techniques for performing on-ground vehicle
detection from aerial imagery captured using UAVs (also known as drones). We review the …

Training very deep networks

RK Srivastava, K Greff… - Advances in neural …, 2015 - proceedings.neurips.cc
… We propose to modify the architecture of very deep feedforward networks such that infor…
Our primary contribution is to show that extremely deep highway networks can be trained directly …

Multimodal learning with deep boltzmann machines

N Srivastava, RR Salakhutdinov - Advances in neural …, 2012 - proceedings.neurips.cc
… A Deep Boltzmann Machine is described for learning a … deep learning methods, including
autoencoders and deep … Second, we develop a Deep Boltzmann Machine as a generative …

Revisiting unreasonable effectiveness of data in deep learning era

C Sun, A Shrivastava, S Singh… - Proceedings of the …, 2017 - openaccess.thecvf.com
The success of deep learning in vision can be attributed to:(a) models with high capacity;(b)
increased computational power; and (c) availability of large-scale labeled data. Since 2012, …

Truth finding on the deep web: Is the problem solved?

…, XL Dong, K Lyons, W Meng, D Srivastava - arXiv preprint arXiv …, 2015 - arxiv.org
… In this paper, we study truthfulness of Deep Web data in two domains where we believed
data are fairly clean and data quality is important to people's lives: {\em Stock} and {\em Flight}. …