b'Peter Bailis'

Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training

Self-training is a standard approach to semi-supervised learning where t...
02/17/2021 ∙ by Kai Sheng Tai, et al. ∙ 0 ∙ share

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Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data

Unstructured data is now commonly queried by using target deep neural ne...
09/09/2020 ∙ by Daniel Kang, et al. ∙ 0 ∙ share

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Approximate Partition Selection for Big-Data Workloads using Summary Statistics

Many big-data clusters store data in large partitions that support acces...
08/24/2020 ∙ by Kexin Rong, et al. ∙ 0 ∙ share

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Leveraging Organizational Resources to Adapt Models to New Data Modalities

As applications in large organizations evolve, the machine learning (ML)...
08/23/2020 ∙ by Sahaana Suri, et al. ∙ 12 ∙ share

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Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics

While deep neural networks (DNNs) are an increasingly popular way to que...
07/25/2020 ∙ by Daniel Kang, et al. ∙ 1 ∙ share

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Similarity Search for Efficient Active Learning and Search of Rare Concepts

Many active learning and search approaches are intractable for industria...
06/30/2020 ∙ by Cody Coleman, et al. ∙ 7 ∙ share

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Chromatic Learning for Sparse Datasets

Learning over sparse, high-dimensional data frequently necessitates the ...
06/06/2020 ∙ by Vladimir Feinberg, et al. ∙ 0 ∙ share

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Approximate Selection with Guarantees using Proxies

Due to the falling costs of data acquisition and storage, researchers an...
04/02/2020 ∙ by Daniel Kang, et al. ∙ 0 ∙ share

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Model Assertions for Monitoring and Improving ML Model

ML models are increasingly deployed in settings with real world interact...
03/03/2020 ∙ by Daniel Kang, et al. ∙ 16 ∙ share

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Model Assertions for Monitoring and Improving ML Models

ML models are increasingly deployed in settings with real world interact...
03/03/2020 ∙ by Daniel Kang, et al. ∙ 9 ∙ share

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Storyboard: Optimizing Precomputed Summaries for Aggregation

An emerging class of data systems partition their data and precompute ap...
02/08/2020 ∙ by Edward Gan, et al. ∙ 0 ∙ share

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MLPerf Training Benchmark

Machine learning is experiencing an explosion of software and hardware s...
10/02/2019 ∙ by Peter Mattson, et al. ∙ 6 ∙ share

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Selection Via Proxy: Efficient Data Selection For Deep Learning

Data selection methods such as active learning and core-set selection ar...
06/26/2019 ∙ by Cody Coleman, et al. ∙ 12 ∙ share

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Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference

Machine learning (ML) has become increasingly important and performance-...
06/03/2019 ∙ by Peter Kraft, et al. ∙ 0 ∙ share

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CrossTrainer: Practical Domain Adaptation with Loss Reweighting

Domain adaptation provides a powerful set of model training techniques g...
05/07/2019 ∙ by Justin Chen, et al. ∙ 0 ∙ share

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SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
03/29/2019 ∙ by Alexander Ratner, et al. ∙ 0 ∙ share

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Equivariant Transformer Networks

How can prior knowledge on the transformation invariances of a domain be...
01/25/2019 ∙ by Kai Sheng Tai, et al. ∙ 0 ∙ share

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LIT: Block-wise Intermediate Representation Training for Model Compression

Knowledge distillation (KD) is a popular method for reducing the computa...
10/02/2018 ∙ by Animesh Koratana, et al. ∙ 4 ∙ share

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Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark

The deep learning community has proposed optimizations spanning hardware...
06/04/2018 ∙ by Cody Coleman, et al. ∙ 0 ∙ share

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BlazeIt: Fast Exploratory Video Queries using Neural Networks

As video volumes grow, analysts have increasingly turned to deep learnin...
05/02/2018 ∙ by Daniel Kang, et al. ∙ 0 ∙ share

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Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science

In this work, we report on a novel application of Locality Sensitive Has...
03/26/2018 ∙ by Kexin Rong, et al. ∙ 0 ∙ share

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Locality-Sensitive Hashing for Earthquake Detection: A Case Study Scaling Data-Driven Science

In this work, we report on a novel application of Locality Sensitive Has...
03/26/2018 ∙ by Kexin Rong, et al. ∙ 0 ∙ share

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Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries

Interactive analytics increasingly involves querying for quantiles over ...
03/06/2018 ∙ by Edward Gan, et al. ∙ 0 ∙ share

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Finding Heavily-Weighted Features in Data Streams

We introduce a new sub-linear space data structure---the Weight-Median S...
11/07/2017 ∙ by Kai Sheng Tai, et al. ∙ 0 ∙ share

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DROP: Dimensionality Reduction Optimization for Time Series

Dimensionality reduction is critical in analyzing increasingly high-volu...
08/01/2017 ∙ by Sahaana Suri, et al. ∙ 0 ∙ share

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There and Back Again: A General Approach to Learning Sparse Models

We propose a simple and efficient approach to learning sparse models. Ou...
06/25/2017 ∙ by Vatsal Sharan, et al. ∙ 0 ∙ share

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To Index or Not to Index: Optimizing Maximum Inner Product Search

Making top-K predictions for state-of-the-art Matrix Factorization model...
06/05/2017 ∙ by Firas Abuzaid, et al. ∙ 0 ∙ share

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SimDex: Exploiting Model Similarity in Exact Matrix Factorization Recommendations

We present SimDex, a new technique for serving exact top-K recommendatio...
06/05/2017 ∙ by Firas Abuzaid, et al. ∙ 0 ∙ share

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Infrastructure for Usable Machine Learning: The Stanford DAWN Project

Despite incredible recent advances in machine learning, building machine...
05/22/2017 ∙ by Peter Bailis, et al. ∙ 0 ∙ share

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NoScope: Optimizing Neural Network Queries over Video at Scale

Recent advances in computer vision-in the form of deep neural networks-h...
03/07/2017 ∙ by Daniel Kang, et al. ∙ 0 ∙ share

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Peter Bailis

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