42 citations · 55 across the 16 of their papers we have counts for
19 papers
GaborFlow: Hardware-Software Co-Optimization of Gabor Filter Accelerator Design
Priyanka Agarwal, Pruthvi Parate, Raghav Khurana +1
Brain tumor classification from MRI scans represents a critical challenge in medical imaging, where accuracy directly impacts patient outcomes. This paper introduces GaborFlow, a n…
Rethinking Literature Search Evaluation: Deep Research Helps, and Human Citation Lists Are Not a Ground Truth
Gaurav Sahu, Laurent Charlin, Christopher Pal
We study large-scale literature search from two complementary angles: improving the retrieval pipeline, and stress-testing the human reference list as an evaluation target. First,…
Computational Modeling of Artistic Inspiration: A Framework for Predicting Aesthetic Preferences in Lyrical Lines Using Linguistic and Stylistic Features
Gaurav Sahu, Olga Vechtomova
Artistic inspiration remains one of the least understood aspects of the creative process. It plays a crucial role in producing works that resonate deeply with audiences, but the co…
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches
Gaurav Sahu, Olga Vechtomova, Issam H. Laradji
Existing approaches for low-resource text summarization primarily employ large language models (LLMs) like GPT-3 or GPT-4 at inference time to generate summaries directly; however,…
InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation
Gaurav Sahu, Abhay Puri, Juan Rodriguez +11
Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We introduce InsightBench, a benchmark dataset w…
Prompt-based Pseudo-labeling Strategy for Sample-Efficient Semi-Supervised Extractive Summarization
Gaurav Sahu, Olga Vechtomova, Issam H. Laradji
Semi-supervised learning (SSL) is a widely used technique in scenarios where labeled data is scarce and unlabeled data is abundant. While SSL is popular for image and text classifi…
PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation
Gaurav Sahu, Olga Vechtomova, Dzmitry Bahdanau +1
Data augmentation is a widely used technique to address the problem of text classification when there is a limited amount of training data. Recent work often tackles this problem u…
Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media
Liam Hebert, Gaurav Sahu, Yuxuan Guo +3
We present the Multi-Modal Discussion Transformer (mDT), a novel methodfor detecting hate speech in online social networks such as Reddit discussions. In contrast to traditional co…
Future Sight: Dynamic Story Generation with Large Pretrained Language Models
Brian D. Zimmerman, Gaurav Sahu, Olga Vechtomova
Recent advances in deep learning research, such as transformers, have bolstered the ability for automated agents to generate creative texts similar to those that a human would writ…
LyricJam Sonic: A Generative System for Real-Time Composition and Musical Improvisation
Olga Vechtomova, Gaurav Sahu
Electronic music artists and sound designers have unique workflow practices that necessitate specialized approaches for developing music information retrieval and creativity suppor…
Data Augmentation for Intent Classification with Off-the-shelf Large Language Models
Gaurav Sahu, Pau Rodriguez, Issam H. Laradji +3
Data augmentation is a widely employed technique to alleviate the problem of data scarcity. In this work, we propose a prompting-based approach to generate labelled training data f…
Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management
Alexandre Parmentier, Robin Cohen, Xueguang Ma +2
In this paper, we present an approach for predicting trust links between peers in social media, one that is grounded in the artificial intelligence area of multiagent trust modelin…
LyricJam: A system for generating lyrics for live instrumental music
Olga Vechtomova, Gaurav Sahu, Dhruv Kumar
We describe a real-time system that receives a live audio stream from a jam session and generates lyric lines that are congruent with the live music being played. Two novel approac…
Towards A Multi-agent System for Online Hate Speech Detection
Gaurav Sahu, Robin Cohen, Olga Vechtomova
This paper envisions a multi-agent system for detecting the presence of hate speech in online social media platforms such as Twitter and Facebook. We introduce a novel framework em…
Generation of lyrics lines conditioned on music audio clips
Olga Vechtomova, Gaurav Sahu, Dhruv Kumar
We present a system for generating novel lyrics lines conditioned on music audio. A bimodal neural network model learns to generate lines conditioned on any given short audio clip.…
Adaptive Fusion Techniques for Multimodal Data
Gaurav Sahu, Olga Vechtomova
Effective fusion of data from multiple modalities, such as video, speech, and text, is challenging due to the heterogeneous nature of multimodal data. In this paper, we propose ada…
Adversarial Learning on the Latent Space for Diverse Dialog Generation
Kashif Khan, Gaurav Sahu, Vikash Balasubramanian +2
Generating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation but also being able to generate fluent sentences duri…
Multimodal Speech Emotion Recognition and Ambiguity Resolution
Gaurav Sahu
Identifying emotion from speech is a non-trivial task pertaining to the ambiguous definition of emotion itself. In this work, we adopt a feature-engineering based approach to tackl…
Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit
Amrith Krishna, Bishal Santra, Sasi Prasanth Bandaru +4
The configurational information in sentences of a free word order language such as Sanskrit is of limited use. Thus, the context of the entire sentence will be desirable even for b…