no code implementations • 22 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.
no code implementations • 16 Feb 2021 • Kartikeya Badola, Sameer Ambekar, Himanshu Pant, Sumit Soman, Anuradha Sural, Rajiv Narang, Suresh Chandra, Jayadeva
We show that popular choices of dataset selection suffer from data homogeneity, leading to misleading results.
no code implementations • 20 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.
no code implementations • 17 Apr 2019 • Siddharth Srivastava, Sumit Soman, Astha Rai
This paper introduces a novel approach for dengue fever classification based on online learning paradigms.
no code implementations • 31 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.
no code implementations • 3 Nov 2018 • Mayank Sharma, Jayadeva, Sumit Soman
Explaining the unreasonable effectiveness of deep learning has eluded researchers around the globe.
no code implementations • 31 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.
1 code implementation • 30 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.
no code implementations • 11 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.
no code implementations • 11 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.