1 code implementation • 13 Mar 2024 • Soumen Basu, Mayuna Gupta, Chetan Madan, Pankaj Gupta, Chetan Arora
We validate the proposed methods on the curated dataset, and report a new state-of-the-art (SOTA) accuracy of 96. 4% for the GBC detection problem, against an accuracy of 84% by current Image-based SOTA - GBCNet, and RadFormer, and 94. 7% by Video-based SOTA - AdaMAE.
no code implementations • 1 Dec 2023 • Anku Rani, Dwip Dalal, Shreya Gautam, Pankaj Gupta, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
This research explores the problem of deception through the lens of psychology, employing a framework that categorizes deception into three forms: lies of omission, lies of commission, and lies of influence.
no code implementations • 11 Sep 2023 • Soumen Basu, Ashish Papanai, Mayank Gupta, Pankaj Gupta, Chetan Arora
We posit that even when we have only the image level label, still formulating the problem as object detection (with bounding box output) helps a deep neural network (DNN) model focus on the relevant region of interest.
1 code implementation • 12 Jan 2023 • Arjun Choudhry, Inder Khatri, Pankaj Gupta, Aaryan Gupta, Maxime Nicol, Marie-Jean Meurs, Dinesh Kumar Vishwakarma
We propose a Transformer-based NER approach for French, using adversarial adaptation to similar domain or general corpora to improve feature extraction and enable better generalization.
1 code implementation • 5 Dec 2022 • Arjun Choudhry, Pankaj Gupta, Inder Khatri, Aaryan Gupta, Maxime Nicol, Marie-Jean Meurs, Dinesh Kumar Vishwakarma
Named Entity Recognition (NER) involves the identification and classification of named entities in unstructured text into predefined classes.
no code implementations • 26 Nov 2022 • Arkajyoti Chakraborty, Inder Khatri, Arjun Choudhry, Pankaj Gupta, Dinesh Kumar Vishwakarma, Mukesh Prasad
Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance.
1 code implementation • 9 Nov 2022 • Soumen Basu, Mayank Gupta, Pratyaksha Rana, Pankaj Gupta, Chetan Arora
We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis.
1 code implementation • 12 Oct 2022 • Yatin Chaudhary, Pranav Rai, Matthias Schubert, Hinrich Schütze, Pankaj Gupta
The objective of Federated Continual Learning (FCL) is to improve deep learning models over life time at each client by (relevant and efficient) knowledge transfer without sharing data.
1 code implementation • 26 Jul 2022 • Soumen Basu, Somanshu Singla, Mayank Gupta, Pratyaksha Rana, Pankaj Gupta, Chetan Arora
We further validate the generalizability of our method on a publicly available lung US image dataset of COVID-19 pathologies and show an improvement of 1. 5% compared to SOTA.
1 code implementation • CVPR 2022 • Soumen Basu, Mayank Gupta, Pratyaksha Rana, Pankaj Gupta, Chetan Arora
However, USG images are challenging to analyze due to low image quality, noise, and varying viewpoints due to the handheld nature of the sensor.
Ranked #1 on Gallbladder Cancer Detection on GBCU
1 code implementation • NAACL 2021 • Pankaj Gupta, Yatin Chaudhary, Hinrich Schütze
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection of documents.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena, Vivek Kulkarni, Thomas Runkler, Hinrich Schütze
Our work thus focuses on optimizing the computational cost of fine-tuning for document classification.
1 code implementation • ICML 2020 • Pankaj Gupta, Yatin Chaudhary, Thomas Runkler, Hinrich Schütze
To address the problem, we propose a lifelong learning framework for neural topic modeling that can continuously process streams of document collections, accumulate topics and guide future topic modeling tasks by knowledge transfer from several sources to better deal with the sparse data.
1 code implementation • ICML 2020 • Yatin Chaudhary, Hinrich Schütze, Pankaj Gupta
Marrying topic models and language models exposes language understanding to a broader source of document-level context beyond sentences via topics.
1 code implementation • WS 2019 • Usama Yaseen, Pankaj Gupta, Hinrich Schütze
Our RE system ranked first in the SeeDev-binary Relation Extraction Task with F1-score of 0. 3738.
1 code implementation • WS 2019 • Yatin Chaudhary, Pankaj Gupta, Hinrich Schütze
This paper presents our system details and results of participation in the RDoC Tasks of BioNLP-OST 2019.
no code implementations • 29 Sep 2019 • Yatin Chaudhary, Pankaj Gupta, Thomas Runkler
in topic modeling, (2) A novel lifelong learning mechanism into neural topic modeling framework to demonstrate continuous learning in sequential document collections and minimizing catastrophic forgetting.
no code implementations • 25 Sep 2019 • Pankaj Gupta, Yatin Chaudhary, Hinrich Schütze
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents.
no code implementations • 14 Sep 2019 • Pankaj Gupta, Yatin Chaudhary, Hinrich Schütze
Though word embeddings and topics are complementary representations, several past works have only used pre-trained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collection of documents.
no code implementations • WS 2019 • Pankaj Gupta, Khushbu Saxena, Usama Yaseen, Thomas Runkler, Hinrich Schütze
To address the tasks of sentence (SLC) and fragment level (FLC) propaganda detection, we explore different neural architectures (e. g., CNN, LSTM-CRF and BERT) and extract linguistic (e. g., part-of-speech, named entity, readability, sentiment, emotion, etc.
1 code implementation • 11 Oct 2018 • Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, Bernt Andrassy, Thomas Runkler
iDepNN models the shortest and augmented dependency paths via recurrent and recursive neural networks to extract relationships within (intra-) and across (inter-) sentence boundaries.
Ranked #1 on Relation Extraction on MUC6
1 code implementation • ICLR 2019 • Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze
We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i. e., P(word|context): (1) No Language Structure in Context: Probabilistic topic models ignore word order by summarizing a given context as a "bag-of-word" and consequently the semantics of words in the context is lost.
1 code implementation • 15 Sep 2018 • Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze
Here, we extend a neural autoregressive topic model to exploit the full context information around words in a document in a language modeling fashion.
1 code implementation • 11 Aug 2018 • Pankaj Gupta, Florian Buettner, Hinrich Schütze
Context information around words helps in determining their actual meaning, for example "networks" used in contexts of artificial neural networks or biological neuron networks.
no code implementations • WS 2018 • Pankaj Gupta, Hinrich Schütze
Recurrent neural networks (RNNs) are temporal networks and cumulative in nature that have shown promising results in various natural language processing tasks.
no code implementations • COLING 2018 • Pankaj Gupta, Bernt Andrassy, Hinrich Schütze
The task is challenging due to significant term mismatch in the query and ticket pairs of asymmetric lengths, where subject is a short text but description and solution are multi-sentence texts.
1 code implementation • NAACL 2018 • Pankaj Gupta, Benjamin Roth, Hinrich Schütze
Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances.
no code implementations • NAACL 2018 • Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, Bernt Andrassy
We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.
no code implementations • 10 Jan 2017 • Drew Linsley, Sven Eberhardt, Tarun Sharma, Pankaj Gupta, Thomas Serre
Our study demonstrates that the narrowing gap between the object recognition accuracy of human observers and DCNs obscures distinct visual strategies used by each to achieve this performance.
1 code implementation • COLING 2016 • Pankaj Gupta, Hinrich Sch{\"u}tze, Bernt Andrassy
This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies.
no code implementations • NAACL 2016 • Ngoc Thang Vu, Heike Adel, Pankaj Gupta, Hinrich Schütze
This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks.