2.3k citations · 3.1k across the 15 of their papers we have counts for
10 papers · 1 filter
Effectively Modeling Time Series with Simple Discrete State Spaces
Michael Zhang, Khaled K. Saab, Michael Poli +3
Time series modeling is a well-established problem, which often requires that methods (1) expressively represent complicated dependencies, (2) forecast long horizons, and (3) effic…
On the Parameterization and Initialization of Diagonal State Space Models
Albert Gu, Ankit Gupta, Karan Goel +1
State space models (SSM) have recently been shown to be very effective as a deep learning layer as a promising alternative to sequence models such as RNNs, CNNs, or Transformers. T…
Personalized Benchmarking with the Ludwig Benchmarking Toolkit
Avanika Narayan, Piero Molino, Karan Goel +2
The rapid proliferation of machine learning models across domains and deployment settings has given rise to various communities (e.g. industry practitioners) which seek to benchmar…
Efficiently Modeling Long Sequences with Structured State Spaces
Albert Gu, Karan Goel, Christopher Ré
A central goal of sequence modeling is designing a single principled model that can address sequence data across a range of modalities and tasks, particularly on long-range depende…
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers
Albert Gu, Isys Johnson, Karan Goel +4
Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with uniqu…
Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems
Laurel Orr, Atindriyo Sanyal, Xiao Ling +2
The industrial machine learning pipeline requires iterating on model features, training and deploying models, and monitoring deployed models at scale. Feature stores were developed…
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli +111
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.…
Mandoline: Model Evaluation under Distribution Shift
Mayee Chen, Karan Goel, Nimit S. Sohoni +3
Machine learning models are often deployed in different settings than they were trained and validated on, posing a challenge to practitioners who wish to predict how well the deplo…
Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
Karan Goel, Albert Gu, Yixuan Li +1
Classifiers in machine learning are often brittle when deployed. Particularly concerning are models with inconsistent performance on specific subgroups of a class, e.g., exhibiting…
PLOTS: Procedure Learning from Observations using Subtask Structure
Tong Mu, Karan Goel, Emma Brunskill
In many cases an intelligent agent may want to learn how to mimic a single observed demonstrated trajectory. In this work we consider how to perform such procedural learning from o…