Search Results for author: Sanket Vaibhav Mehta

Found 13 papers, 5 papers with code

Regularizing Self-training for Unsupervised Domain Adaptation via Structural Constraints

no code implementations29 Apr 2023 Rajshekhar Das, Jonathan Francis, Sanket Vaibhav Mehta, Jean Oh, Emma Strubell, Jose Moura

Self-training based on pseudo-labels has emerged as a dominant approach for addressing conditional distribution shifts in unsupervised domain adaptation (UDA) for semantic segmentation problems.

Object Semantic Segmentation +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

An Introduction to Lifelong Supervised Learning

no code implementations10 Jul 2022 Shagun Sodhani, Mojtaba Faramarzi, Sanket Vaibhav Mehta, Pranshu Malviya, Mohamed Abdelsalam, Janarthanan Janarthanan, Sarath Chandar

Following these different classes of learning algorithms, we discuss the commonly used evaluation benchmarks and metrics for lifelong learning (Chapter 6) and wrap up with a discussion of future challenges and important research directions in Chapter 7.

Train Flat, Then Compress: Sharpness-Aware Minimization Learns More Compressible Models

no code implementations25 May 2022 Clara Na, Sanket Vaibhav Mehta, Emma Strubell

Model compression by way of parameter pruning, quantization, or distillation has recently gained popularity as an approach for reducing the computational requirements of modern deep neural network models for NLP.

Model Compression Quantization +3

An Empirical Investigation of the Role of Pre-training in Lifelong Learning

1 code implementation NeurIPS 2023 Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, Emma Strubell

The lifelong learning paradigm in machine learning is an attractive alternative to the more prominent isolated learning scheme not only due to its resemblance to biological learning but also its potential to reduce energy waste by obviating excessive model re-training.

Continual Learning Image Classification

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

Unsupervised Domain Adaptation Via Pseudo-labels And Objectness Constraints

no code implementations29 Sep 2021 Rajshekhar Das, Jonathan Francis, Sanket Vaibhav Mehta, Jean Oh, Emma Strubell, Jose Moura

Crucially, the objectness constraint is agnostic to the ground-truth semantic segmentation labels and, therefore, remains appropriate for unsupervised adaptation settings.

Object Pseudo Label +4

Efficient Meta Lifelong-Learning with Limited Memory

no code implementations EMNLP 2020 ZiRui Wang, Sanket Vaibhav Mehta, Barnabás Póczos, Jaime Carbonell

State-of-the-art lifelong language learning methods store past examples in episodic memory and replay them at both training and inference time.

Multi-Task Learning Question Answering +2

Learning Rhyming Constraints using Structured Adversaries

1 code implementation IJCNLP 2019 Harsh Jhamtani, Sanket Vaibhav Mehta, Jaime Carbonell, Taylor Berg-Kirkpatrick

Existing recurrent neural language models often fail to capture higher-level structure present in text: for example, rhyming patterns present in poetry.

Spatial Co-location Pattern Mining - A new perspective using Graph Database

1 code implementation21 Oct 2018 Sanket Vaibhav Mehta, Shagun Sodhani, Dhaval Patel

Spatial co-location pattern mining refers to the task of discovering the group of objects or events that co-occur at many places.

Databases Distributed, Parallel, and Cluster Computing

Towards Semi-Supervised Learning for Deep Semantic Role Labeling

no code implementations EMNLP 2018 Sanket Vaibhav Mehta, Jay Yoon Lee, Jaime Carbonell

The paper proposes a semi-supervised semantic role labeling method that outperforms the state-of-the-art in limited SRL training corpora.

Semantic Role Labeling

Gradient-based Inference for Networks with Output Constraints

no code implementations26 Jul 2017 Jay Yoon Lee, Sanket Vaibhav Mehta, Michael Wick, Jean-Baptiste Tristan, Jaime Carbonell

Practitioners apply neural networks to increasingly complex problems in natural language processing, such as syntactic parsing and semantic role labeling that have rich output structures.

Constituency Parsing Semantic Role Labeling +2

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