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https://arxiv.org/pdf/2407.07726(July 2024)PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong performance on a wide variety of open-world tasks. We evaluate PaliGemma on almost 40 diverse tasks incl..

https://arxiv.org/pdf/2106.13884(Jun 2021 NeurIPS 2021)Abstract When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language). Using aligned imag..

(Mar 2023)https://arxiv.org/pdf/2303.06053https://github.com/google-research/google-research/tree/master/tsmixerAbstractReal-world time-series datasets are often multivariate with complex dynamics. To capture this complexity, high capacity architectures like recurrent- or attention-based sequential deep learning models have become popular. However, recent work demonstrates that simple univariate..

https://doi.org/10.1609/aaai.v38i21.30383(March, 2024)AbstractTime series forecasting is an essential area of machine learning with a wide range of real-world applications. Most of the previous forecasting models aim to capture dynamic characteristics from uni-modal numerical historical data. Although extra knowledge can boost the time series forecasting performance, it is hard to collect such i..

https://arxiv.org/pdf/2409.16040https://github.com/Time-MoE/Time-MoE(Sep 2024)AbstractDeep learning for time series forecasting has seen significant advancements over the past decades. However, despite the success of large-scale pre-training in language and vision domains, pre-trained time series models remain limited in scale and operate at a high cost, hindering the development of larger capab..

https://arxiv.org/pdf/2403.07815https://github.com/amazon-science/chronos-forecastingAbstractWe introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cro..

(Feb 2024 ICML 2024)https://arxiv.org/pdf/2402.03885https://github.com/moment-timeseries-foundation-model/momentAbstractWe introduce MOMENT, a family of open-source foundation models for general-purpose time series analysis. Pre-training large models on time series data is challenging due to (1) the absence of a large and cohesive public time series repository, and (2) diverse time series charac..

https://arxiv.org/pdf/2310.10688https://github.com/google-research/timesfm(Oct 2023 Google Research)AbstractMotivated by recent advances in large language models for Natural Language Processing (NLP), we design a time-series foundation model for forecasting whose out-of-the-box zero-shot performance on a variety of public datasets comes close to the accuracy of state-of-the-art supervised foreca..