A Sample-based Criterion for Unsupervised Learning of Complex Models beyond Maximum Likelihood and Density Estimation

Manavalan, Mani; Donepudi, Praveen Kumar

doi:10.18034/abcjar.v5i2.581

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…Similar to natural language processing, these performances could be evaluated using the likelihood on a hold-out test set. We believe that if this parameter is relevant, it should be reported in future studies (Manavalan & Donepudi, 2016).…”

Section: Mechanisms Of Failure In Distribution-learningmentioning

confidence: 94%

Molecular Generators and Optimizers Failure Modes

Manavalan

2021

Malays. j. med. biol. res.

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In recent years, there has been an uptick in interest in generative models for molecules in drug development. In the field of de novo molecular design, these models are used to make molecules with desired properties from scratch. This is occasionally used instead of virtual screening, which is limited by the size of the libraries that can be searched in practice. Rather than screening existing libraries, generative models can be used to build custom libraries from scratch. Using generative models, which may optimize molecules straight towards the desired profile, this time-consuming approach can be sped up. The purpose of this work is to show how current shortcomings in evaluating generative models for molecules can be avoided. We cover both distribution-learning and goal-directed generation with a focus on the latter. Three well-known targets were downloaded from ChEMBL: Janus kinase 2 (JAK2), epidermal growth factor receptor (EGFR), and dopamine receptor D2 (DRD2) (Bento et al. 2014). We preprocessed the data to get binary classification jobs. Before calculating a scoring function, the data is split into two halves, which we shall refer to as split 1/2. The ratio of active to inactive users. Our goal is to train three bioactivity models with equal prediction performance, one to be used as a scoring function for chemical optimization and the other two to be used as performance evaluation models. Our findings suggest that distribution-learning can attain near-perfect scores on many existing criteria even with the most basic and completely useless models. According to benchmark studies, likelihood-based models account for many of the best technologies, and we propose that test set likelihoods be included in future comparisons.

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Section: Mechanisms Of Failure In Distribution-learningmentioning

confidence: 94%

Molecular Generators and Optimizers Failure Modes

Manavalan

2021

Malays. j. med. biol. res.

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“…Its overuse during the epidemic may have aided problematic internet usage behavior, as noted by (Islam et al, 2020). They discovered that people from nuclear families utilize the internet more than people from extended families (Manavalan & Donepudi, 2016).…”

Section: Impact On Social Lifementioning

confidence: 99%

“…Basic economic theory holds that during periods of economic expansion businesses experience increased demand, which in turn necessitates investment in more capital or labor (Manavalan, 2016). When businesses are experiencing growth, job confidence and security typically increase.…”

Section: Introductionmentioning

confidence: 99%

The Impact of COVID – 19 on Employee’s Job Security and Job Satisfaction of Bangladesh

Islam

Al-Shams

2021

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This paper presents a comprehensive diagnosis of Job Security and Job Satisfaction of different service sector in Bangladesh. The different service sector job holders are in a precarious situation in terms of their job security. The survey questionnaire was modeled to measure the job security & job satisfaction of an employee in this Covid-19 pandemic. In this study, we investigate the vacillation of job security for different service sector employees. Job security and Job Satisfaction is one of the vexed questions here in the COVID-19 pandemic situation. It focuses on the relative importance of Job Security and job satisfaction factors and their impact on the employment. The result shows that working conditions, chance to try my own methods and co workers harmony are the most important factors contributing to job satisfaction during COVID-19. For this purpose, we formed questionnaire of 300 service sector employees such as Teachers School, College and University; bankers, doctors, engineers etc. to find out the actual scenarios. It also investigates the influence of age, gender and managerial/non managerial differences on the attitude towards job satisfaction. The study also examines the differences between intrinsic and extrinsic job satisfaction factors. Based on the findings, the employees at different service sector they were slightly satisfied with their jobs. Service sector employees are agonizing mostly due to this problem.

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“…The difference between the two is that, when it comes to supervised learning, there is a requirement to formulate every input pattern in order to achieve a related pattern of output. For supervised learning, the model input has the tendency to output the raw data collected at one or many upstream gauges, wherein the said output is predicted to discharge when it is in a downstream station (Manavalan & Donepudi, 2016).…”

Section: Learning Considerationsmentioning

confidence: 99%

Do Internals of Neural Networks Make Sense in the Context of Hydrology?

Manavalan

2018

Asian j. appl. sci. eng.

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As of recent times, neural networks have drawn in a lot of attention and popularity because of their application to numerous dimensions, including computer visioning and the processing of natural language. Nevertheless, when it comes to the science applicable to the immediate environment, such as the run-off of rainfall modeling in the field of hydrology, and so being, neural networks have the tendency to have a surprisingly demeaning reputation. This bad record can be blamed on the reality that they are black-boxed in primal nature, as well as to the complication or impossibility to comprehend internals that culminates in a forecast. Neural systems make up a computational technology tailored for hydrological predictions. In spite of being extensively utilized in many other fields of research and application, the number of hydrologists that favor the methodology may be lower than expected. That’s as a result of the neural network’s data-driven nature for the applied challenges looking to be solved. Also, neural systems make provision for a modeling footprint that comes in handy when there is a sufficiency of raw information to connect X with Y, particularly in the cases where there is a need for real-time results. With this paper, we are introducing neural ecosystem challenges in generality, setting them in more extensive modeling in a relational context with hydrology. This study explores the internals of the learned networks for different basin sets from the sundrily obtainable dataset known as CAMELS. Also, it utilizes local sensing data in the symbolism of patterns in alignment with our comprehension of the system of hydrology. Demonstratively, we showcase that internally, LSTMs learned to interpret patterns that align with our comprehension of the hydrological system. For snow-carried catchments, for instance, the network grows specific memory cells with the capacity to imitate conceptual snow memories with yearly dynamics. This is known and accepted for the catchment models based on processes.

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A Sample-based Criterion for Unsupervised Learning of Complex Models beyond Maximum Likelihood and Density Estimation

Cited by 8 publications

References 20 publications

Molecular Generators and Optimizers Failure Modes

Molecular Generators and Optimizers Failure Modes

The Impact of COVID – 19 on Employee’s Job Security and Job Satisfaction of Bangladesh

Do Internals of Neural Networks Make Sense in the Context of Hydrology?

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