Search Results for author: Karan Goel

Found 16 papers, 10 papers with code

It's Raw! Audio Generation with State-Space Models

2 code implementations20 Feb 2022 Karan Goel, Albert Gu, Chris Donahue, Christopher Ré

SaShiMi yields state-of-the-art performance for unconditional waveform generation in the autoregressive setting.

Audio Generation Density Estimation +1

Personalized Benchmarking with the Ludwig Benchmarking Toolkit

2 code implementations8 Nov 2021 Avanika Narayan, Piero Molino, Karan Goel, Willie Neiswanger, Christopher Ré

LBT provides a configurable interface for controlling training and customizing evaluation, a standardized training framework for eliminating confounding variables, and support for multi-objective evaluation.

Hyperparameter Optimization Text Classification

Efficiently Modeling Long Sequences with Structured State Spaces

3 code implementations ICLR 2022 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 dependencies.

Data Augmentation Long-range modeling +1

Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers

2 code implementations NeurIPS 2021 Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, Christopher Ré

Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency.

Sequential Image Classification Time Series

On the Opportunities and Risks of Foundation Models

no code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Kohd, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

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.

Transfer Learning

Managing ML Pipelines: Feature Stores and the Coming Wave of Embedding Ecosystems

no code implementations11 Aug 2021 Laurel Orr, Atindriyo Sanyal, Xiao Ling, Karan Goel, Megan Leszczynski

The industrial machine learning pipeline requires iterating on model features, training and deploying models, and monitoring deployed models at scale.

Mandoline: Model Evaluation under Distribution Shift

1 code implementation1 Jul 2021 Mayee Chen, Karan Goel, Nimit S. Sohoni, Fait Poms, Kayvon Fatahalian, Christopher Ré

If an unlabeled sample from the target distribution is available, along with a labeled sample from a possibly different source distribution, standard approaches such as importance weighting can be applied to estimate performance on the target.

Density Ratio Estimation Epidemiology

Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers

no code implementations NeurIPS 2021 Albert Gu, Isys Johnson, Karan Goel, Khaled Kamal Saab, Tri Dao, Atri Rudra, Christopher Re

Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency.

Sequential Image Classification Time Series

SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization

1 code implementation ACL 2021 Jesse Vig, Wojciech Kryściński, Karan Goel, Nazneen Fatema Rajani

Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization.

Abstractive Text Summarization

Robustness Gym: Unifying the NLP Evaluation Landscape

2 code implementations NAACL 2021 Karan Goel, Nazneen Rajani, Jesse Vig, Samson Tan, Jason Wu, Stephan Zheng, Caiming Xiong, Mohit Bansal, Christopher Ré

Despite impressive performance on standard benchmarks, deep neural networks are often brittle when deployed in real-world systems.

Entity Linking

Model Patching: Closing the Subgroup Performance Gap with Data Augmentation

1 code implementation ICLR 2021 Karan Goel, Albert Gu, Yixuan Li, Christopher Ré

Particularly concerning are models with inconsistent performance on specific subgroups of a class, e. g., exhibiting disparities in skin cancer classification in the presence or absence of a spurious bandage.

Data Augmentation Skin Cancer Classification

Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure

1 code implementation ICLR 2019 Karan Goel, Emma Brunskill

Given a dataset of time-series, the goal is to identify the latent sequence of steps common to them and label each time-series with the temporal extent of these procedural steps.

Time Series

PLOTS: Procedure Learning from Observations using Subtask Structure

no code implementations17 Apr 2019 Tong Mu, Karan Goel, Emma Brunskill

In many cases an intelligent agent may want to learn how to mimic a single observed demonstrated trajectory.

Sample Efficient Policy Search for Optimal Stopping Domains

no code implementations21 Feb 2017 Karan Goel, Christoph Dann, Emma Brunskill

Optimal stopping problems consider the question of deciding when to stop an observation-generating process in order to maximize a return.

Octopus: A Framework for Cost-Quality-Time Optimization in Crowdsourcing

1 code implementation12 Feb 2017 Karan Goel, Shreya Rajpal, Mausam

We present Octopus, an AI agent to jointly balance three conflicting task objectives on a micro-crowdsourcing marketplace - the quality of work, total cost incurred, and time to completion.

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