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07/28/2023
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
We study how vision-language models trained on Internet-scale data can b...
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02/02/2023
Mnemosyne: Learning to Train Transformers with Transformers
Training complex machine learning (ML) architectures requires a compute ...
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02/02/2023
Efficient Graph Field Integrators Meet Point Clouds
We present two new classes of algorithms for efficient field integration...
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02/01/2023
FAVOR#: Sharp Attention Kernel Approximations via New Classes of Positive Random Features
The problem of efficient approximation of a linear operator induced by t...
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10/05/2022
A Fourier Approach to Mixture Learning
We revisit the problem of learning mixtures of spherical Gaussians. Give...
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09/15/2022
Unsupervised Opinion Summarization Using Approximate Geodesics
Opinion summarization is the task of creating summaries capturing popula...
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05/30/2022
Chefs' Random Tables: Non-Trigonometric Random Features
We introduce chefs' random tables (CRTs), a new class of non-trigonometr...
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10/15/2021
On Learning the Transformer Kernel
In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, d...
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04/14/2021
Exact and Approximate Hierarchical Clustering Using A*
Hierarchical clustering is a critical task in numerous domains. Many app...
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10/22/2020
Scalable Bottom-Up Hierarchical Clustering
Bottom-up algorithms such as the classic hierarchical agglomerative clus...
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07/28/2020
Big Bird: Transformers for Longer Sequences
Transformers-based models, such as BERT, have been one of the most succe...
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01/15/2020
Distributed, partially collapsed MCMC for Bayesian Nonparametrics
Bayesian nonparametric (BNP) models provide elegant methods for discover...
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01/30/2018
Transformation Autoregressive Networks
The fundamental task of general density estimation has been of keen inte...
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01/30/2018
Personalized Survival Prediction with Contextual Explanation Networks
Accurate and transparent prediction of cancer survival times on the leve...
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01/30/2018
The Intriguing Properties of Model Explanations
Linear approximations to the decision boundary of a complex model have b...
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05/29/2017
Contextual Explanation Networks
We introduce contextual explanation networks (CENs)---a class of models ...
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03/09/2017
Parallel Markov Chain Monte Carlo for the Indian Buffet Process
Indian Buffet Process based models are an elegant way for discovering un...
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02/13/2016
Science Question Answering using Instructional Materials
We provide a solution for elementary science test using instructional ma...
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06/29/2015
Bayesian Nonparametric Kernel-Learning
Kernel methods are ubiquitous tools in machine learning. They have prove...
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11/29/2012
Exact and Efficient Parallel Inference for Nonparametric Mixture Models
Nonparametric mixture models based on the Dirichlet process are an elega...
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08/22/2012