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Researcher profile

Gerald Woo

Gerald Woo contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningArtificial IntelligenceCryptography and Security

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Published work

4 published item(s)

preprint2026arXiv

Toto 2.0: Time Series Forecasting Enters the Scaling Era

We show that time series foundation models scale: a single training recipe produces reliable forecast-quality improvements from 4M to 2.5B parameters. We release Toto 2.0, a family of five open-weights forecasting models trained under this recipe. The Toto 2.0 family sets a new state of the art on three forecasting benchmarks: BOOM, our observability benchmark; GIFT-Eval, the standard general-purpose benchmark; and the recent contamination-resistant TIME benchmark. This report describes our experimental results and details the design decisions behind Toto 2.0: its architecture and training recipe, training data, and the u-muP hyperparameter transfer pipeline. All five base checkpoints are released under Apache 2.0.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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

Deep learning has been actively studied for time series forecasting, and the mainstream paradigm is based on the end-to-end training of neural network architectures, ranging from classical LSTM/RNNs to more recent TCNs and Transformers. 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. Following this principle, we propose a new time series representation learning framework for time series forecasting named CoST, which applies contrastive learning methods to learn disentangled seasonal-trend representations. CoST comprises both time domain and frequency domain contrastive losses to learn discriminative trend and seasonal representations, respectively. Extensive experiments on real-world datasets show that CoST consistently outperforms the state-of-the-art methods by a considerable margin, achieving a 21.3% improvement in MSE on multivariate benchmarks. It is also robust to various choices of backbone encoders, as well as downstream regressors. Code is available at https://github.com/salesforce/CoST.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

Transformers have been actively studied for time-series forecasting in recent years. While often showing promising results in various scenarios, traditional Transformers are not designed to fully exploit the characteristics of time-series data and thus suffer some fundamental limitations, e.g., they generally lack of decomposition capability and interpretability, and are neither effective nor efficient for long-term forecasting. In this paper, we propose ETSFormer, a novel time-series Transformer architecture, which exploits the principle of exponential smoothing in improving Transformers for time-series forecasting. In particular, inspired by the classical exponential smoothing methods in time-series forecasting, we propose the novel exponential smoothing attention (ESA) and frequency attention (FA) to replace the self-attention mechanism in vanilla Transformers, thus improving both accuracy and efficiency. Based on these, we redesign the Transformer architecture with modular decomposition blocks such that it can learn to decompose the time-series data into interpretable time-series components such as level, growth and seasonality. Extensive experiments on various time-series benchmarks validate the efficacy and advantages of the proposed method. Code is available at https://github.com/salesforce/ETSformer.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Model Agnostic Defence against Backdoor Attacks in Machine Learning

Machine Learning (ML) has automated a multitude of our day-to-day decision making domains such as education, employment and driving automation. The continued success of ML largely depends on our ability to trust the model we are using. Recently, a new class of attacks called Backdoor Attacks have been developed. These attacks undermine the user's trust in ML models. In this work, we present NEO, a model agnostic framework to detect and mitigate such backdoor attacks in image classification ML models. For a given image classification model, our approach analyses the inputs it receives and determines if the model is backdoored. In addition to this feature, we also mitigate these attacks by determining the correct predictions of the poisoned images. An appealing feature of NEO is that it can, for the first time, isolate and reconstruct the backdoor trigger. NEO is also the first defence methodology, to the best of our knowledge that is completely blackbox. We have implemented NEO and evaluated it against three state of the art poisoned models. These models include highly critical applications such as traffic sign detection (USTS) and facial detection. In our evaluation, we show that NEO can detect $\approx$88% of the poisoned inputs on average and it is as fast as 4.4 ms per input image. We also reconstruct the poisoned input for the user to effectively test their systems.

0 citations0 reviewsNo code linkedOpen access