text: 106,34,1408286165858410503,1059281382025977856,Polina Kirichenko,"Excited to share that (1) It is my birthday 🎂 (2) We have a new paper ""Task-agnostic Continual Learning with Hybrid Probabilistic Models"" on arxiv today! We design a hybrid generative-discriminative model based on normalizing flows for continual learning <LINK> <LINK> In task-agnostic continual learning, we need to train the model continually on a sequence of tasks without knowing the task boundaries. Turns out, we can use a single hybrid model to (1) detect task changes, (2) avoid forgetting and (3) make predictions! We propose HCL, which uses a single normalizing flow to map the data into a latent space where we model the distribution of each class in each task as a Gaussian. When a new task appears we (1) identify it using the flow and (2) initialize the latent Gaussians for the new task. To make predictions, we use the Bayes rule: we predict the class (and task) for which the input has the highest density. To avoid forgetting, we propose two techniques: - HCL-GR uses generative replay, where we sample data from a snapshot of the model and re-train the model on this data - HCL-FR uses functional regularization, where we force the model to map replay data to the same latent position <LINK> HCL-FR provides stronger regularization and prevents forgetting better! In the figure: (b) data distribution with squares showing our replay data; grey is the first and orange is the second task; (c) HCL-GR model and (d) HCL-FR model after training on the second task. <LINK> We compare HCL variants with other generative continual learning models, with promising results. HCL performs well both in task-aware and task-agnostic settings! <LINK> Finally, HCL enables us to automatically detect new tasks as well as recurring tasks! To do so we can use the normalizing flow likelihood, or an advanced anomaly detection technique such as DoSE (<LINK>). With a great team of coauthors at @DeepMind @GoogleAI and @NYUDataScience: @MFarajtabar, @drao64, @balajiln, Nir Levine, @__angli, Huiyi Hu, @andrewgwils and @rpascanu! @KevinKaichuang Happy birthday! 🙂 @balajiln Thank you! :) @MFarajtabar Haha thank you Mehrdad! 🙂",https://arxiv.org/abs/2106.12772,"Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learning. In this work we propose HCL, a Hybrid generative-discriminative approach to Continual Learning for classification. We model the distribution of each task and each class with a normalizing flow. The flow is used to learn the data distribution, perform classification, identify task changes, and avoid forgetting, all leveraging the invertibility and exact likelihood which are uniquely enabled by the normalizing flow model. We use the generative capabilities of the flow to avoid catastrophic forgetting through generative replay and a novel functional regularization technique. For task identification, we use state-of-the-art anomaly detection techniques based on measuring the typicality of the model's statistics. We demonstrate the strong performance of HCL on a range of continual learning benchmarks such as split-MNIST, split-CIFAR, and SVHN-MNIST. ",Task-agnostic Continual Learning with Hybrid Probabilistic Models,12,"['Excited to share that\n(1) It is my birthday 🎂\n(2) We have a new paper ""Task-agnostic Continual Learning with Hybrid Probabilistic Models"" on arxiv today!  We design a hybrid generative-discriminative model based on normalizing flows for continual learning <LINK> <LINK>', 'In task-agnostic continual learning, we need to train the model continually on a sequence of tasks without knowing the task boundaries. Turns out, we can use a single hybrid model to (1) detect task changes, (2) avoid forgetting and (3) make predictions!', 'We propose HCL, which uses a single normalizing flow to map the data into a latent space where we model the distribution of each class in each task as a Gaussian. When a new task appears we (1) identify it using the flow and (2) initialize the latent Gaussians for the new task.', 'To make predictions, we use the Bayes rule: we predict the class (and task) for which the input has the highest density.', 'To avoid forgetting, we propose two techniques: \n- HCL-GR uses generative replay, where we sample data from a snapshot of the model and re-train the model on this data\n- HCL-FR uses functional regularization, where we force the model to map replay data to the same latent position https://t.co/1kIUJYq1NK', 'HCL-FR provides stronger regularization and prevents forgetting better! In the figure: (b) data distribution with squares showing our replay data; grey is the first and orange is the second task; (c) HCL-GR model and (d) HCL-FR model after training on the second task. https://t.co/0veMNcEbpB', 'We compare HCL variants with other generative continual learning models, with promising results. HCL performs well both in task-aware and task-agnostic settings! https://t.co/r77wd7gZHe', 'Finally, HCL enables us to automatically detect new tasks as well as recurring tasks! To do so we can use the normalizing flow likelihood, or an advanced anomaly detection technique such as DoSE (https://t.co/woliqw12Ma).', 'With a great team of coauthors at @DeepMind @GoogleAI  and @NYUDataScience:\n\n@MFarajtabar, @drao64, @balajiln, Nir Levine, @__angli, Huiyi Hu, @andrewgwils and @rpascanu!', '@KevinKaichuang Happy birthday! 🙂', '@balajiln Thank you! :)', '@MFarajtabar Haha thank you Mehrdad! 🙂']",21,06,2126