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Found 53 papers, 13 papers with code
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
A recent regularization technique - Manifold Mixup focuses on learning a general-purpose representation, robust to small changes in the data distribution.
Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections
Projection of changes in extreme indices of climate variables such as temperature and precipitation are critical to assess the potential impacts of climate change on human-made and natural systems, including critical infrastructures and ecosystems.
LineEX: Data Extraction from Scientific Line Charts
In this paper, we introduce LINEEX that extracts data from scientific line charts.
Attributional Robustness Training using Input-Gradient Spatial Alignment
Safe deployment of machine learning system mandates that the prediction and its explanation be reliable and robust.
Ranked #1 on
Weakly-Supervised Object Localization
on CUB-200-2011
(Top-1 Error Rate metric)
BIG-bench Machine Learning
Weakly-Supervised Object Localization
Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models
We analyze the adversarially trained robust models to study their vulnerability against adversarial attacks at the level of the latent layers.
IIT Gandhinagar at SemEval-2019 Task 3: Contextual Emotion Detection Using Deep Learning
Recent advancements in Internet and Mobile infrastructure have resulted in the development of faster and efficient platforms of communication.
Bollyrics: Automatic Lyrics Generator for Romanised Hindi
Song lyrics convey a meaningful story in a creative manner with complex rhythmic patterns.
COMPARE: A Taxonomy and Dataset of Comparison Discussions in Peer Reviews
We present COMPARE, a taxonomy and a dataset of comparison discussions in peer reviews of research papers in the domain of experimental deep learning.
The Inefficiency of Language Models in Scholarly Retrieval: An Experimental Walk-through
Language models are increasingly becoming popular in AI-powered scientific IR systems.
Cross-lingual Editing in Multilingual Language Models
The training of large language models (LLMs) necessitates substantial data and computational resources, and updating outdated LLMs entails significant efforts and resources.
How Robust are the Tabular QA Models for Scientific Tables? A Study using Customized Dataset
Question-answering (QA) on hybrid scientific tabular and textual data deals with scientific information, and relies on complex numerical reasoning.
Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space
Recently, Neural networks have seen a huge surge in its adoption due to their ability to provide high accuracy on various tasks.
AppTechMiner: Mining Applications and Techniques from Scientific Articles
We also categorize individual research articles based on their application areas and the techniques proposed/improved in the article.
Which techniques does your application use?: An information extraction framework for scientific articles
Every field of research consists of multiple application areas with various techniques routinely used to solve problems in these wide range of application areas.
Automated Early Leaderboard Generation From Comparative Tables
We propose several approaches to rank papers from these noisy 'match' outcomes.
NLPExplorer: Exploring the Universe of NLP Papers
Also, it provides temporal statistics such as yearwise popularity of topics, datasets, and seminal papers.
Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification
The task of learning a sentiment classification model that adapts well to any target domain, different from the source domain, is a challenging problem.
A Method for Computing Class-wise Universal Adversarial Perturbations
We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks.
PHINC: A Parallel Hinglish Social Media Code-Mixed Corpus for Machine Translation
Code-mixing is the phenomenon of using more than one language in a sentence.
On the Benefits of Models with Perceptually-Aligned Gradients
In this paper, we leverage models with interpretable perceptually-aligned features and show that adversarial training with low max-perturbation bound can improve the performance of models for zero-shot and weakly supervised localization tasks.
Gandhipedia: A one-stop AI-enabled portal for browsing Gandhian literature, life-events and his social network
We introduce an AI-enabled portal that presents an excellent visualization of Mahatma Gandhi's life events by constructing temporal and spatial social networks from the Gandhian literature.
SEAL: Scientific Keyphrase Extraction and Classification
Automatic scientific keyphrase extraction is a challenging problem facilitating several downstream scholarly tasks like search, recommendation, and ranking.
IIT Gandhinagar at SemEval-2020 Task 9: Code-Mixed Sentiment Classification Using Candidate Sentence Generation and Selection
Code-mixing is the phenomenon of using multiple languages in the same utterance of a text or speech.
Identification, Tracking and Impact: Understanding the trade secret of catchphrases
Understanding the topical evolution in industrial innovation is a challenging problem.
LT-GAN: Self-Supervised GAN with Latent Transformation Detection
We propose a self-supervised approach (LT-GAN) to improve the generation quality and diversity of images by estimating the GAN-induced transformation (i. e. transformation induced in the generated images by perturbing the latent space of generator).
Ranked #4 on
Image Generation
on CelebA-HQ 128x128
CovidExplorer: A Multi-faceted AI-based Search and Visualization Engine for COVID-19 Information
The entire world is engulfed in the fight against the COVID-19 pandemic, leading to a significant surge in research experiments, government policies, and social media discussions.
Data InStance Prior (DISP) in Generative Adversarial Networks
Previous works have addressed training in low data setting by leveraging transfer learning and data augmentation techniques.
TabLeX: A Benchmark Dataset for Structure and Content Information Extraction from Scientific Tables
Information Extraction (IE) from the tables present in scientific articles is challenging due to complicated tabular representations and complex embedded text.
SentEmojiBot: Empathising Conversations Generation with Emojis
The increasing use of dialogue agents makes it extremely desirable for them to understand and acknowledge the implied emotions to respond like humans with empathy.
ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX
This paper discusses the dataset, tasks, participants' methods, and results of the ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX.
Challenges and Considerations with Code-Mixed NLP for Multilingual Societies
Multilingualism refers to the high degree of proficiency in two or more languages in the written and oral communication modes.
Challenges and Limitations with the Metrics Measuring the Complexity of Code-Mixed Text
Code-mixing is a frequent communication style among multilingual speakers where they mix words and phrases from two different languages in the same utterance of text or speech.
TweeNLP: A Twitter Exploration Portal for Natural Language Processing
It supports multiple features such as TweetExplorer to explore tweets by topics, visualize insights from Twitter activity throughout the organization cycle of conferences, discover popular research papers and researchers.
On Adversarial Robustness of Synthetic Code Generation
In this paper, we specifically experiment with \textsc{AlgoLisp} DSL-based generative models and showcase the existence of significant dataset bias through different classes of adversarial examples.
HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text
Text generation is a highly active area of research in the computational linguistic community.
MIPE: A Metric Independent Pipeline for Effective Code-Mixed NLG Evaluation
Code-mixing is a phenomenon of mixing words and phrases from two or more languages in a single utterance of speech and text.
Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text
In this shared task, we seek the participating teams to investigate the factors influencing the quality of the code-mixed text generation systems.
Data Instance Prior for Transfer Learning in GANs
Recent advances in generative adversarial networks (GANs) have shown remarkable progress in generating high-quality images.
AutoCoder: Leveraging Transformers for Automatic Code Synthesis
Program synthesis from natural language descriptions is a challenging task.
Adversarial Robustness of Program Synthesis Models
The resurgence of automatic program synthesis has been observed with the rise of deep learning.
The Bull and the Bear: Summarizing Stock Market Discussions
We plan to make the dataset publicly available.
Tables to LaTeX: structure and content extraction from scientific tables
In this paper, we adapt the transformer-based language modeling paradigm for scientific table structure and content extraction.
MUTANT: A Multi-sentential Code-mixed Hinglish Dataset
As a use case, we leverage multilingual articles from two different data sources and build a first-of-its-kind multi-sentential code-mixed Hinglish dataset i. e., MUTANT.
MMT: A Multilingual and Multi-Topic Indian Social Media Dataset
Social media plays a significant role in cross-cultural communication.
Analogy-Forming Transformers for Few-Shot 3D Parsing
We present Analogical Networks, a model that encodes domain knowledge explicitly, in a collection of structured labelled 3D scenes, in addition to implicitly, as model parameters, and segments 3D object scenes with analogical reasoning: instead of mapping a scene to part segments directly, our model first retrieves related scenes from memory and their corresponding part structures, and then predicts analogous part structures for the input scene, via an end-to-end learnable modulation mechanism.
Metric@CustomerN: Evaluating Metrics at a Customer Level in E-Commerce
Accuracy measures such as Recall, Precision, and Hit Rate have been a standard way of evaluating Recommendation Systems.
Modeling interdisciplinary interactions among Physics, Mathematics & Computer Science
Interdisciplinarity has over the recent years have gained tremendous importance and has become one of the key ways of doing cutting edge research.
Unlocking Model Insights: A Dataset for Automated Model Card Generation
To automate model card generation, we introduce a dataset of 500 question-answer pairs for 25 ML models that cover crucial aspects of the model, such as its training configurations, datasets, biases, architecture details, and training resources.
Unveiling the Multi-Annotation Process: Examining the Influence of Annotation Quantity and Instance Difficulty on Model Performance
The NLP community has long advocated for the construction of multi-annotator datasets to better capture the nuances of language interpretation, subjectivity, and ambiguity.
PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLM
Driven by the surge in code generation using large language models (LLMs), numerous benchmarks have emerged to evaluate these LLMs capabilities.
LEGOBench: Scientific Leaderboard Generation Benchmark
We present four graph-based and two language model-based leaderboard generation task configurations.
Remember This Event That Year? Assessing Temporal Information and Reasoning in Large Language Models
Large Language Models (LLMs) are increasingly becoming ubiquitous, yet their ability to reason about and retain temporal information remains limited.
