40 citations · 104 across the 13 of their papers we have counts for
15 papers
Evaluation of Active Feature Acquisition Policies with Tabular Foundation Models
Yuta Kobayashi, Divyam Madaan, Shalmali Joshi
Active feature acquisition learns policies that sequentially acquire features to maximize information about a target variable. We study how to learn and evaluate such policies from…
Characterizing the Predictive Impact of Modalities with Supervised Latent-Variable Modeling
Divyam Madaan, Sumit Chopra, Kyunghyun Cho
Despite the recent success of Multimodal Large Language Models (MLLMs), existing approaches predominantly assume the availability of multiple modalities during training and inferen…
Temporal Generalization: A Reality Check
Divyam Madaan, Sumit Chopra, Kyunghyun Cho
Machine learning (ML) models often struggle to maintain performance under distribution shifts, leading to inaccurate predictions on unseen future data. In this work, we investigate…
Multi-modal Data Spectrum: Multi-modal Datasets are Multi-dimensional
Divyam Madaan, Varshan Muhunthan, Kyunghyun Cho +1
Understanding the interplay between intra-modality dependencies (the contribution of an individual modality to a target task) and inter-modality dependencies (the relationships bet…
HIST-AID: Leveraging Historical Patient Reports for Enhanced Multi-Modal Automatic Diagnosis
Haoxu Huang, Cem M. Deniz, Kyunghyun Cho +2
Chest X-ray imaging is a widely accessible and non-invasive diagnostic tool for detecting thoracic abnormalities. While numerous AI models assist radiologists in interpreting these…
Jointly Modeling Inter- & Intra-Modality Dependencies for Multi-modal Learning
Divyam Madaan, Taro Makino, Sumit Chopra +1
Supervised multi-modal learning involves mapping multiple modalities to a target label. Previous studies in this field have concentrated on capturing in isolation either the inter-…
On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis
Divyam Madaan, Daniel Sodickson, Kyunghyun Cho +1
Magnetic Resonance Imaging (MRI) is considered the gold standard of medical imaging because of the excellent soft-tissue contrast exhibited in the images reconstructed by the MRI p…
Heterogeneous Continual Learning
Divyam Madaan, Hongxu Yin, Wonmin Byeon +2
We propose a novel framework and a solution to tackle the continual learning (CL) problem with changing network architectures. Most CL methods focus on adapting a single architectu…
Improving Representational Continuity via Continued Pretraining
Michael Sun, Ananya Kumar, Divyam Madaan +1
We consider the continual representation learning setting: sequentially pretrain a model on tasks , and then adapt on a small amount of data from each t…
What Do NLP Researchers Believe? Results of the NLP Community Metasurvey
Julian Michael, Ari Holtzman, Alicia Parrish +8
We present the results of the NLP Community Metasurvey. Run from May to June 2022, the survey elicited opinions on controversial issues, including industry influence in the field,…
Representational Continuity for Unsupervised Continual Learning
Divyam Madaan, Jaehong Yoon, Yuanchun Li +2
Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge. However, recent CL advances are restricted to supervised continual l…
Online Coreset Selection for Rehearsal-based Continual Learning
Jaehong Yoon, Divyam Madaan, Eunho Yang +1
A dataset is a shred of crucial evidence to describe a task. However, each data point in the dataset does not have the same potential, as some of the data points can be more repres…
Learning to Generate Noise for Multi-Attack Robustness
Divyam Madaan, Jinwoo Shin, Sung Ju Hwang
Adversarial learning has emerged as one of the successful techniques to circumvent the susceptibility of existing methods against adversarial perturbations. However, the majority o…
Adversarial Neural Pruning with Latent Vulnerability Suppression
Divyam Madaan, Jinwoo Shin, Sung Ju Hwang
Despite the remarkable performance of deep neural networks on various computer vision tasks, they are known to be susceptible to adversarial perturbations, which makes it challengi…
VayuAnukulani: Adaptive Memory Networks for Air Pollution Forecasting
Divyam Madaan, Radhika Dua, Prerana Mukherjee +1
Air pollution is the leading environmental health hazard globally due to various sources which include factory emissions, car exhaust and cooking stoves. As a precautionary measure…