Search Results for author: Sumit Soman

Found 10 papers, 1 papers with code

Observations on LLMs for Telecom Domain: Capabilities and Limitations

no code implementations22 May 2023 Sumit Soman, Ranjani H G

The landscape for building conversational interfaces (chatbots) has witnessed a paradigm shift with recent developments in generative Artificial Intelligence (AI) based Large Language Models (LLMs), such as ChatGPT by OpenAI (GPT3. 5 and GPT4), Google's Bard, Large Language Model Meta AI (LLaMA), among others.

Domain Adaptation Language Modelling +1

Complexity Controlled Generative Adversarial Networks

no code implementations20 Nov 2020 Himanshu Pant, Jayadeva, Sumit Soman

One of the issues faced in training Generative Adversarial Nets (GANs) and their variants is the problem of mode collapse, wherein the training stability in terms of the generative loss increases as more training data is used.

An Online Learning Approach for Dengue Fever Classification

no code implementations17 Apr 2019 Siddharth Srivastava, Sumit Soman, Astha Rai

This paper introduces a novel approach for dengue fever classification based on online learning paradigms.

Classification General Classification

Effect of Various Regularizers on Model Complexities of Neural Networks in Presence of Input Noise

no code implementations31 Jan 2019 Mayank Sharma, Aayush Yadav, Sumit Soman, Jayadeva

We show that $L_2$ regularization leads to a simpler hypothesis class and better generalization followed by DARC1 regularizer, both for shallow as well as deeper architectures.

Radius-margin bounds for deep neural networks

no code implementations3 Nov 2018 Mayank Sharma, Jayadeva, Sumit Soman

Explaining the unreasonable effectiveness of deep learning has eluded researchers around the globe.

Learning Neural Network Classifiers with Low Model Complexity

no code implementations31 Jul 2017 Jayadeva, Himanshu Pant, Mayank Sharma, Abhimanyu Dubey, Sumit Soman, Suraj Tripathi, Sai Guruju, Nihal Goalla

Our proposed approach yields benefits across a wide range of architectures, in comparison to and in conjunction with methods such as Dropout and Batch Normalization, and our results strongly suggest that deep learning techniques can benefit from model complexity control methods such as the LCNN learning rule.

Scalable Twin Neural Networks for Classification of Unbalanced Data

1 code implementation30 Apr 2017 Jayadeva, Himanshu Pant, Sumit Soman, Mayank Sharma

In this paper, we discuss a Twin Neural Network (Twin NN) architecture for learning from large unbalanced datasets.

Classification General Classification

Benchmarking NLopt and state-of-art algorithms for Continuous Global Optimization via Hybrid IACO$_\mathbb{R}$

no code implementations11 Mar 2015 Udit Kumar, Sumit Soman, Jayadeva

This paper presents a comparative analysis of the performance of the Incremental Ant Colony algorithm for continuous optimization ($IACO_\mathbb{R}$), with different algorithms provided in the NLopt library.

Benchmarking

A Neurodynamical System for finding a Minimal VC Dimension Classifier

no code implementations11 Mar 2015 Jayadeva, Sumit Soman, Amit Bhaya

The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane classifier by minimizing an exact bound on the Vapnik-Chervonenkis (VC) dimension.

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