Search Results for author: Jai Gupta

Found 12 papers, 5 papers with code

How Does Generative Retrieval Scale to Millions of Passages?

no code implementations19 May 2023 Ronak Pradeep, Kai Hui, Jai Gupta, Adam D. Lelkes, Honglei Zhuang, Jimmy Lin, Donald Metzler, Vinh Q. Tran

Popularized by the Differentiable Search Index, the emerging paradigm of generative retrieval re-frames the classic information retrieval problem into a sequence-to-sequence modeling task, forgoing external indices and encoding an entire document corpus within a single Transformer.

Information Retrieval Passage Ranking +1

DSI++: Updating Transformer Memory with New Documents

no code implementations19 Dec 2022 Sanket Vaibhav Mehta, Jai Gupta, Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Jinfeng Rao, Marc Najork, Emma Strubell, Donald Metzler

In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents.

Continual Learning Natural Questions +1

Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification

no code implementations16 Dec 2022 Jai Gupta, Yi Tay, Chaitanya Kamath, Vinh Q. Tran, Donald Metzler, Shailesh Bavadekar, Mimi Sun, Evgeniy Gabrilovich

With the devastating outbreak of COVID-19, vaccines are one of the crucial lines of defense against mass infection in this global pandemic.

Natural Language Understanding

Confident Adaptive Language Modeling

no code implementations14 Jul 2022 Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Q. Tran, Yi Tay, Donald Metzler

Recent advances in Transformer-based large language models (LLMs) have led to significant performance improvements across many tasks.

Language Modelling Text Generation

Transformer Memory as a Differentiable Search Index

1 code implementation14 Feb 2022 Yi Tay, Vinh Q. Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, Tal Schuster, William W. Cohen, Donald Metzler

In this paper, we demonstrate that information retrieval can be accomplished with a single Transformer, in which all information about the corpus is encoded in the parameters of the model.

Information Retrieval Retrieval

ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning

3 code implementations ICLR 2022 Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Steven Zheng, Sanket Vaibhav Mehta, Honglei Zhuang, Vinh Q. Tran, Dara Bahri, Jianmo Ni, Jai Gupta, Kai Hui, Sebastian Ruder, Donald Metzler

Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the number of tasks during pre-training.

Denoising Multi-Task Learning

Are Pre-trained Convolutions Better than Pre-trained Transformers?

1 code implementation7 May 2021 Yi Tay, Mostafa Dehghani, Jai Gupta, Dara Bahri, Vamsi Aribandi, Zhen Qin, Donald Metzler

In the context of language models, are convolutional models competitive to Transformers when pre-trained?

OmniNet: Omnidirectional Representations from Transformers

1 code implementation1 Mar 2021 Yi Tay, Mostafa Dehghani, Vamsi Aribandi, Jai Gupta, Philip Pham, Zhen Qin, Dara Bahri, Da-Cheng Juan, Donald Metzler

In OmniNet, instead of maintaining a strictly horizontal receptive field, each token is allowed to attend to all tokens in the entire network.

Few-Shot Learning Language Modelling +2

Analog Signal Processing Approach for Coarse and Fine Depth Estimation

no code implementations6 Jan 2015 Nihar Athreyas, Zhiguo Lai, Jai Gupta, Dev Gupta

We propose novel modifications to the algorithms and new imaging architectures which, significantly reduces the computation time.

Depth Estimation

Cannot find the paper you are looking for? You can Submit a new open access paper.