no code implementations • 25 Sep 2023 • Nidhi Hegde, Sujoy Paul, Gagan Madan, Gaurav Aggarwal
Recent document question answering models consist of two key components: the vision encoder, which captures layout and visual elements in images, and a Large Language Model (LLM) that helps contextualize questions to the image and supplements them with external world knowledge to generate accurate answers.
no code implementations • 29 Aug 2023 • Debapriya Tula, Sujoy Paul, Gagan Madan, Peter Garst, Reeve Ingle, Gaurav Aggarwal
While text line recognition models are generally trained on large corpora of real and synthetic data, such models can still make frequent mistakes if the handwriting is inscrutable or the image acquisition process adds corruptions, such as noise, blur, compression, etc.
no code implementations • 12 Jun 2023 • Sujoy Paul, Gagan Madan, Akankshya Mishra, Narayan Hegde, Pradeep Kumar, Gaurav Aggarwal
In this work, we focus on the complex problem of extracting medicine names from handwritten prescriptions using only weakly labeled data.
1 code implementation • 30 May 2023 • Returaj Burnwal, Anirban Santara, Nirav P. Bhatt, Balaraman Ravindran, Gaurav Aggarwal
We propose a novel approach that uses a generative adversarial network (GAN) to minimize the Jensen-Shannon divergence between the state-trajectory distributions of the demonstrator and the imitator.
no code implementations • 29 Nov 2022 • Sohan Rudra, Saksham Goel, Anirban Santara, Claudio Gentile, Laurent Perron, Fei Xia, Vikas Sindhwani, Carolina Parada, Gaurav Aggarwal
Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object.
no code implementations • 7 Oct 2022 • Soumyabrata Pal, Prateek Varshney, Prateek Jain, Abhradeep Guha Thakurta, Gagan Madan, Gaurav Aggarwal, Pradeep Shenoy, Gaurav Srivastava
We then study the framework in the linear setting, where the problem reduces to that of estimating the sum of a rank-$r$ and a $k$-column sparse matrix using a small number of linear measurements.
no code implementations • 21 Jul 2022 • K J Joseph, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, Vineeth N Balasubramanian
Inspired by this, we identify and formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery without Forgetting, which tasks a machine learning model to incrementally discover novel categories of instances from unlabeled data, while maintaining its performance on the previously seen categories.
no code implementations • 17 Jun 2022 • Ashwin Vaswani, Gaurav Aggarwal, Praneeth Netrapalli, Narayan G Hegde
Compared to standard multilabel baselines, CHAMP provides improved AUPRC in both robustness (8. 87% mean percentage improvement ) and less data regimes.
1 code implementation • 22 Apr 2022 • K J Joseph, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, Vineeth N Balasubramanian
Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes.
1 code implementation • 21 Mar 2022 • Mihir Prabhudesai, Anirudh Goyal, Sujoy Paul, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Gaurav Aggarwal, Thomas Kipf, Deepak Pathak, Katerina Fragkiadaki
In our work, we find evidence that these losses are insufficient for the task of scene decomposition, without also considering architectural inductive biases.
no code implementations • 4 Dec 2021 • Ansh Khurana, Sujoy Paul, Piyush Rai, Soma Biswas, Gaurav Aggarwal
In Test-time Adaptation (TTA), given a source model, the goal is to adapt it to make better predictions for test instances from a different distribution than the source.
no code implementations • 4 Dec 2021 • Sujoy Paul, Ansh Khurana, Gaurav Aggarwal
Unsupervised domain adaptation aims to adapt a model learned on the labeled source data, to a new unlabeled target dataset.
no code implementations • NeurIPS 2021 • Ping Zhang, Rishabh Iyer, Ashish Tendulkar, Gaurav Aggarwal, Abir De
Marked temporal point processes (MTPPs) have emerged as a powerful modelingtool for a wide variety of applications which are characterized using discreteevents localized in continuous time.
1 code implementation • 23 Nov 2021 • Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Guha Thakurta, Gaurav Aggarwal, Prateek Jain
Graph Neural Networks (GNNs) are a popular technique for modelling graph-structured data and computing node-level representations via aggregation of information from the neighborhood of each node.
no code implementations • 7 Jun 2021 • Anirban Santara, Claudio Gentile, Gaurav Aggarwal, Shuai Li
Motivated by problems of learning to rank long item sequences, we introduce a variant of the cascading bandit model that considers flexible length sequences with varying rewards and losses.
no code implementations • 17 May 2021 • Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham, Milind Tambe
In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks.
no code implementations • 28 Jan 2015 • Miriam Redi, Nikhil Rasiwasia, Gaurav Aggarwal, Alejandro Jaimes
Digital portrait photographs are everywhere, and while the number of face pictures keeps growing, not much work has been done to on automatic portrait beauty assessment.