Search Results for author: Gerald Woo

Found 8 papers, 6 papers with code

Unified Training of Universal Time Series Forecasting Transformers

no code implementations4 Feb 2024 Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo

Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models.

Time Series Time Series Forecasting

AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

no code implementations10 Apr 2023 Qian Cheng, Doyen Sahoo, Amrita Saha, Wenzhuo Yang, Chenghao Liu, Gerald Woo, Manpreet Singh, Silvio Saverese, Steven C. H. Hoi

There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency.

Learning Deep Time-index Models for Time Series Forecasting

1 code implementation13 Jul 2022 Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models.

Inductive Bias Meta-Learning +2

CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

1 code implementation ICLR 2022 Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step -- we justify such a paradigm from a causal perspective.

Contrastive Learning Representation Learning +2

Model Agnostic Defence against Backdoor Attacks in Machine Learning

2 code implementations6 Aug 2019 Sakshi Udeshi, Shanshan Peng, Gerald Woo, Lionell Loh, Louth Rawshan, Sudipta Chattopadhyay

In this work, we present NEO, a model agnostic framework to detect and mitigate such backdoor attacks in image classification ML models.

BIG-bench Machine Learning Decision Making +4

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