Ramakrishna Vedantam

According to our database, Ramakrishna Vedantam authored at least 26 papers between 2014 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Embarassingly Simple Dataset Distillation.

Yunzhen Feng, Ramakrishna Vedantam, Julia Kempe

CoRR, 2023

Understanding the detrimental class-level effects of data augmentation.

Polina Kirichenko, Mark Ibrahim, Randall Balestriero, Diane Bouchacourt, Shanmukha Ramakrishna Vedantam, Hamed Firooz, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Hyperbolic Image-text Representations.

Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Shanmukha Ramakrishna Vedantam

Proceedings of the International Conference on Machine Learning, 2023

Don't forget the nullspace! Nullspace occupancy as a mechanism for out of distribution failure.

Daksh Idnani, Vivek Madan, Naman Goyal, David J. Schwab, Ramakrishna Vedantam

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Improving Selective Visual Question Answering by Learning from Your Peers.

Corentin Dancette, Spencer Whitehead, Rishabh Maheshwary, Ramakrishna Vedantam, Stefan Scherer, Xinlei Chen, Matthieu Cord, Marcus Rohrbach

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

COAT: Measuring Object Compositionality in Emergent Representations.

Sirui Xie, Ari S. Morcos, Song-Chun Zhu, Ramakrishna Vedantam

Proceedings of the International Conference on Machine Learning, 2022

2021

An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers.

Ramakrishna Vedantam, David Lopez-Paz, David J. Schwab

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

CURI: A Benchmark for Productive Concept Learning Under Uncertainty.

Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel, Ari Morcos, Brenden M. Lake

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization.

Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra

Int. J. Comput. Vis., 2020

Learning Optimal Representations with the Decodable Information Bottleneck.

Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL.

Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

2019

Unsupervised Discovery of Decision States for Transfer in Reinforcement Learning.

Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam

CoRR, 2019

Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering.

Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, Devi Parikh

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Generative Models of Visually Grounded Imagination.

Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin Murphy

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Sound-Word2Vec: Learning Word Representations Grounded in Sounds.

Ashwin K. Vijayakumar, Ramakrishna Vedantam, Devi Parikh

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017

Context-Aware Captions from Context-Agnostic Supervision.

Ramakrishna Vedantam, Samy Bengio, Kevin Murphy, Devi Parikh, Gal Chechik

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Counting Everyday Objects in Everyday Scenes.

Prithvijit Chattopadhyay, Ramakrishna Vedantam, Ramprasaath R. Selvaraju, Dhruv Batra, Devi Parikh

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

Adopting Abstract Images for Semantic Scene Understanding.

C. Lawrence Zitnick, Ramakrishna Vedantam, Devi Parikh

IEEE Trans. Pattern Anal. Mach. Intell., 2016

Grad-CAM: Why did you say that?

Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, Dhruv Batra

CoRR, 2016

Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization.

Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, Dhruv Batra

CoRR, 2016

VisualWord2Vec (Vis-W2V): Learning Visually Grounded Word Embeddings Using Abstract Scenes.

Satwik Kottur, Ramakrishna Vedantam, José M. F. Moura, Devi Parikh

Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016

2015

Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes.

Satwik Kottur, Ramakrishna Vedantam, José M. F. Moura, Devi Parikh

CoRR, 2015

Microsoft COCO Captions: Data Collection and Evaluation Server.

Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, C. Lawrence Zitnick

CoRR, 2015

Learning Common Sense through Visual Abstraction.

Ramakrishna Vedantam, Xiao Lin, Tanmay Batra, C. Lawrence Zitnick, Devi Parikh

Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015

CIDEr: Consensus-based image description evaluation.

Ramakrishna Vedantam, C. Lawrence Zitnick, Devi Parikh

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

2014

Collecting Image Description Datasets using Crowdsourcing.

Ramakrishna Vedantam, C. Lawrence Zitnick, Devi Parikh

CoRR, 2014