2.3k citations · 3.1k across the 15 of their papers we have counts for
8 papers · 1 filter
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…
SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization
Jesse Vig, Wojciech Kryściński, Karan Goel +1
Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization.…
Robustness Gym: Unifying the NLP Evaluation Landscape
Karan Goel, Nazneen Rajani, Jesse Vig +6
Despite impressive performance on standard benchmarks, deep neural networks are often brittle when deployed in real-world systems. Consequently, recent research has focused on test…