Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

1 code implementation • 12 Oct 2023 • Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Hena Ghonia, Rishika Bhagwatkar, Arian Khorasani, Mohammad Javad Darvishi Bayazi, George Adamopoulos, Roland Riachi, Nadhir Hassen, Marin Biloš, Sahil Garg, Anderson Schneider, Nicolas Chapados, Alexandre Drouin, Valentina Zantedeschi, Yuriy Nevmyvaka, Irina Rish

Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization.

Probabilistic Time Series Forecasting Time Series +1

TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series

1 code implementation • 2 Oct 2023 • Arjun Ashok, Étienne Marcotte, Valentina Zantedeschi, Nicolas Chapados, Alexandre Drouin

We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including forecasting, interpolation, and their combinations.

Time Series Time Series Prediction

Extremely Simple Activation Shaping for Out-of-Distribution Detection

1 code implementation • 20 Sep 2022 • Andrija Djurisic, Nebojsa Bozanic, Arjun Ashok, Rosanne Liu

The separation between training and deployment of machine learning models implies that not all scenarios encountered in deployment can be anticipated during training, and therefore relying solely on advancements in training has its limits.

Out-of-Distribution Detection

Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer

1 code implementation • 7 Aug 2022 • Arjun Ashok, K J Joseph, Vineeth Balasubramanian

This allows the model to learn classes in such a way that it maximizes positive forward transfer from similar prior classes, thus increasing plasticity, and minimizes negative backward transfer on dissimilar prior classes, whereby strengthening stability.

Class Incremental Learning Clustering +1

Learning Modular Structures That Generalize Out-of-Distribution

no code implementations • 7 Aug 2022 • Arjun Ashok, Chaitanya Devaguptapu, Vineeth Balasubramanian

generalization remains to be a key challenge for real-world machine learning systems.

Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks

7 code implementations • 16 Apr 2022 • Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, Siddhartha Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi, Daniel Khashabi

This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.

Benchmarking Instruction Following