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08/27/2022
Apiary: A DBMS-Backed Transactional Function-as-a-Service Framework
Developers are increasingly using function-as-a-service (FaaS) platforms...
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01/15/2022
Finding Label and Model Errors in Perception Data With Learned Observation Assertions
ML is being deployed in complex, real-world scenarios where errors have ...
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02/17/2021
Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training
Self-training is a standard approach to semi-supervised learning where t...
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09/09/2020
Task-agnostic Indexes for Deep Learning-based Queries over Unstructured Data
Unstructured data is now commonly queried by using target deep neural ne...
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08/24/2020
Approximate Partition Selection for Big-Data Workloads using Summary Statistics
Many big-data clusters store data in large partitions that support acces...
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08/23/2020
Leveraging Organizational Resources to Adapt Models to New Data Modalities
As applications in large organizations evolve, the machine learning (ML)...
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07/25/2020
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics
While deep neural networks (DNNs) are an increasingly popular way to que...
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06/30/2020
Similarity Search for Efficient Active Learning and Search of Rare Concepts
Many active learning and search approaches are intractable for industria...
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06/06/2020
Chromatic Learning for Sparse Datasets
Learning over sparse, high-dimensional data frequently necessitates the ...
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04/02/2020
Approximate Selection with Guarantees using Proxies
Due to the falling costs of data acquisition and storage, researchers an...
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03/03/2020
Model Assertions for Monitoring and Improving ML Model
ML models are increasingly deployed in settings with real world interact...
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03/03/2020
Model Assertions for Monitoring and Improving ML Models
ML models are increasingly deployed in settings with real world interact...
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02/08/2020
Storyboard: Optimizing Precomputed Summaries for Aggregation
An emerging class of data systems partition their data and precompute ap...
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10/02/2019
MLPerf Training Benchmark
Machine learning is experiencing an explosion of software and hardware s...
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06/26/2019
Selection Via Proxy: Efficient Data Selection For Deep Learning
Data selection methods such as active learning and core-set selection ar...
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06/03/2019
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
Machine learning (ML) has become increasingly important and performance-...
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05/07/2019
CrossTrainer: Practical Domain Adaptation with Loss Reweighting
Domain adaptation provides a powerful set of model training techniques g...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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01/25/2019
Equivariant Transformer Networks
How can prior knowledge on the transformation invariances of a domain be...
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10/02/2018
LIT: Block-wise Intermediate Representation Training for Model Compression
Knowledge distillation (KD) is a popular method for reducing the computa...
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06/04/2018
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
The deep learning community has proposed optimizations spanning hardware...
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05/02/2018
BlazeIt: Fast Exploratory Video Queries using Neural Networks
As video volumes grow, analysts have increasingly turned to deep learnin...
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03/26/2018
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...
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03/26/2018
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...
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03/06/2018
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries
Interactive analytics increasingly involves querying for quantiles over ...
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11/07/2017
Finding Heavily-Weighted Features in Data Streams
We introduce a new sub-linear space data structure---the Weight-Median S...
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08/01/2017
DROP: Dimensionality Reduction Optimization for Time Series
Dimensionality reduction is critical in analyzing increasingly high-volu...
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06/25/2017
There and Back Again: A General Approach to Learning Sparse Models
We propose a simple and efficient approach to learning sparse models. Ou...
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06/05/2017
To Index or Not to Index: Optimizing Maximum Inner Product Search
Making top-K predictions for state-of-the-art Matrix Factorization model...
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06/05/2017
SimDex: Exploiting Model Similarity in Exact Matrix Factorization Recommendations
We present SimDex, a new technique for serving exact top-K recommendatio...
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05/22/2017
Infrastructure for Usable Machine Learning: The Stanford DAWN Project
Despite incredible recent advances in machine learning, building machine...
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03/07/2017
