Search Results for author: Anahita Bhiwandiwalla

Found 16 papers, 5 papers with code

Llama-Nemotron: Efficient Reasoning Models

no code implementations2 May 2025 Akhiad Bercovich, Itay Levy, Izik Golan, Mohammad Dabbah, Ran El-Yaniv, Omri Puny, Ido Galil, Zach Moshe, Tomer Ronen, Najeeb Nabwani, Ido Shahaf, Oren Tropp, Ehud Karpas, Ran Zilberstein, Jiaqi Zeng, Soumye Singhal, Alexander Bukharin, Yian Zhang, Tugrul Konuk, Gerald Shen, Ameya Sunil Mahabaleshwarkar, Bilal Kartal, Yoshi Suhara, Olivier Delalleau, Zijia Chen, Zhilin Wang, David Mosallanezhad, Adi Renduchintala, Haifeng Qian, Dima Rekesh, Fei Jia, Somshubra Majumdar, Vahid Noroozi, Wasi Uddin Ahmad, Sean Narenthiran, Aleksander Ficek, Mehrzad Samadi, Jocelyn Huang, Siddhartha Jain, Igor Gitman, Ivan Moshkov, Wei Du, Shubham Toshniwal, George Armstrong, Branislav Kisacanin, Matvei Novikov, Daria Gitman, Evelina Bakhturina, Jane Polak Scowcroft, John Kamalu, Dan Su, Kezhi Kong, Markus Kliegl, Rabeeh Karimi, Ying Lin, Sanjeev Satheesh, Jupinder Parmar, Pritam Gundecha, Brandon Norick, Joseph Jennings, Shrimai Prabhumoye, Syeda Nahida Akter, Mostofa Patwary, Abhinav Khattar, Deepak Narayanan, Roger Waleffe, Jimmy Zhang, Bor-Yiing Su, Guyue Huang, Terry Kong, Parth Chadha, Sahil Jain, Christine Harvey, Elad Segal, Jining Huang, Sergey Kashirsky, Robert McQueen, Izzy Putterman, George Lam, Arun Venkatesan, Sherry Wu, Vinh Nguyen, Manoj Kilaru, Andrew Wang, Anna Warno, Abhilash Somasamudramath, Sandip Bhaskar, Maka Dong, Nave Assaf, Shahar Mor, Omer Ullman Argov, Scot Junkin, Oleksandr Romanenko, Pedro Larroy, Marco Rovinelli, Viji Balas, Nicholas Edelman, Anahita Bhiwandiwalla, Muthu Subramaniam, Smita Ithape, Karthik Ramamoorthy, Yuting Wu, Suguna Varshini Velury, Omri Almog, Joyjit Daw, Denys Fridman, Erick Galinkin, Michael Evans, Shaona Ghosh, Katherine Luna, Leon Derczynski, Nikki Pope, Eileen Long, Seth Schneider, Guillermo Siman, Tomasz Grzegorzek, Pablo Ribalta, Monika Katariya, Chris Alexiuk, Joey Conway, Trisha Saar, Ann Guan, Krzysztof Pawelec, Shyamala Prayaga, Oleksii Kuchaiev, Boris Ginsburg, Oluwatobi Olabiyi, Kari Briski, Jonathan Cohen, Bryan Catanzaro, Jonah Alben, Yonatan Geifman, Eric Chung

We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an open license for enterprise use.

Knowledge Distillation Neural Architecture Search

LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression

no code implementations6 Mar 2025 Souvik Kundu, Anahita Bhiwandiwalla, Sungduk Yu, Phillip Howard, Tiep Le, Sharath Nittur Sridhar, David Cobbley, Hao Kang, Vasudev Lal

Despite recent efforts in understanding the compression impact on large language models (LLMs) in terms of their downstream task performance and trustworthiness on relatively simpler uni-modal benchmarks (for example, question answering, common sense reasoning), their detailed study on multi-modal Large Vision-Language Models (LVLMs) is yet to be unveiled.

Benchmarking Common Sense Reasoning +8

ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution

no code implementations28 Aug 2024 Sungduk Yu, Brian L. White, Anahita Bhiwandiwalla, Musashi Hinck, Matthew Lyle Olson, Yaniv Gurwicz, Raanan Y. Rohekar, Tung Nguyen, Vasudev Lal

The dataset is curated from both CMIP6 climate model simulations and real-world observation-assimilated reanalysis datasets (ERA5, JRA-3Q, and MERRA-2), and is designed to enhance model accuracy in detecting climate change signals.

Change Detection

Uncovering Bias in Large Vision-Language Models at Scale with Counterfactuals

no code implementations30 May 2024 Phillip Howard, Kathleen C. Fraser, Anahita Bhiwandiwalla, Svetlana Kiritchenko

To address this challenging problem, we conduct a large-scale study of text generated by different LVLMs under counterfactual changes to input images.

counterfactual Question Answering +1

Uncovering Bias in Large Vision-Language Models with Counterfactuals

no code implementations29 Mar 2024 Phillip Howard, Anahita Bhiwandiwalla, Kathleen C. Fraser, Svetlana Kiritchenko

We comprehensively evaluate the text produced by different LVLMs under this counterfactual generation setting and find that social attributes such as race, gender, and physical characteristics depicted in input images can significantly influence toxicity and the generation of competency-associated words.

counterfactual Question Answering +1

SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examples

1 code implementation CVPR 2024 Phillip Howard, Avinash Madasu, Tiep Le, Gustavo Lujan Moreno, Anahita Bhiwandiwalla, Vasudev Lal

Our approach utilizes Stable Diffusion with cross attention control to produce sets of counterfactual image-text pairs that are highly similar in their depiction of a subject (e. g., a given occupation) while differing only in their depiction of intersectional social attributes (e. g., race & gender).

counterfactual

Analyzing Zero-Shot Abilities of Vision-Language Models on Video Understanding Tasks

1 code implementation7 Oct 2023 Avinash Madasu, Anahita Bhiwandiwalla, Vasudev Lal

We investigate 9 foundational image-text models on a diverse set of video tasks that include video action recognition (video AR), video retrieval (video RT), video question answering (video QA), video multiple choice (video MC) and video captioning (video CP).

Action Recognition Multiple-choice +6

ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning

1 code implementation31 May 2023 Xiao Xu, Bei Li, Chenfei Wu, Shao-Yen Tseng, Anahita Bhiwandiwalla, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan

With only 4M VLP data, ManagerTower achieves superior performances on various downstream VL tasks, especially 79. 15% accuracy on VQAv2 Test-Std, 86. 56% IR@1 and 95. 64% TR@1 on Flickr30K.

cross-modal alignment Representation Learning

Correlation of Auroral Dynamics and GNSS Scintillation with an Autoencoder

no code implementations4 Oct 2019 Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atılım Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt

High energy particles originating from solar activity travel along the the Earth's magnetic field and interact with the atmosphere around the higher latitudes.

Prediction of GNSS Phase Scintillations: A Machine Learning Approach

no code implementations3 Oct 2019 Kara Lamb, Garima Malhotra, Athanasios Vlontzos, Edward Wagstaff, Atılım Günes Baydin, Anahita Bhiwandiwalla, Yarin Gal, Alfredo Kalaitzis, Anthony Reina, Asti Bhatt

We propose a novel architecture and loss function to predict 1 hour in advance the magnitude of phase scintillations within a time window of plus-minus 5 minutes with state-of-the-art performance.

BIG-bench Machine Learning Prediction

Using Scene Graph Context to Improve Image Generation

no code implementations11 Jan 2019 Subarna Tripathi, Anahita Bhiwandiwalla, Alexei Bastidas, Hanlin Tang

Generating realistic images from scene graphs asks neural networks to be able to reason about object relationships and compositionality.

Image Generation from Scene Graphs Open-Ended Question Answering +1

Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning

1 code implementation24 Jan 2018 Scott Cyphers, Arjun K. Bansal, Anahita Bhiwandiwalla, Jayaram Bobba, Matthew Brookhart, Avijit Chakraborty, Will Constable, Christian Convey, Leona Cook, Omar Kanawi, Robert Kimball, Jason Knight, Nikolay Korovaiko, Varun Kumar, Yixing Lao, Christopher R. Lishka, Jaikrishnan Menon, Jennifer Myers, Sandeep Aswath Narayana, Adam Procter, Tristan J. Webb

The current approach, which we call "direct optimization", requires deep changes within each framework to improve the training performance for each hardware backend (CPUs, GPUs, FPGAs, ASICs) and requires $\mathcal{O}(fp)$ effort; where $f$ is the number of frameworks and $p$ is the number of platforms.

Deep Learning graph partitioning +2

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