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07/11/2023
EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy
In the post-Moore's Law era, relying solely on hardware advancements for...
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06/19/2023
Co-design Hardware and Algorithm for Vector Search
Vector search has emerged as the foundation for large-scale information ...
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05/28/2023
Repeated Random Sampling for Minimizing the Time-to-Accuracy of Learning
Methods for carefully selecting or generating a small set of training da...
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05/16/2023
Growing and Serving Large Open-domain Knowledge Graphs
Applications of large open-domain knowledge graphs (KGs) to real-world p...
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04/04/2023
High-Throughput Vector Similarity Search in Knowledge Graphs
There is an increasing adoption of machine learning for encoding data in...
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04/15/2022
Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale
We introduce Saga, a next-generation knowledge construction and serving ...
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02/04/2022
Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine
Graph Neural Networks (GNNs) have emerged as a powerful model for ML ove...
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06/02/2021
Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins
Structured data, or data that adheres to a pre-defined schema, can suffe...
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01/20/2021
Marius: Learning Massive Graph Embeddings on a Single Machine
We propose a new framework for computing the embeddings of large-scale g...
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10/14/2020
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion
We show that state-of-the-art self-supervised language models can be rea...
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06/23/2020
Principal Component Networks: Parameter Reduction Early in Training
Recent works show that overparameterized networks contain small subnetwo...
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06/18/2020
Record fusion: A learning approach
Record fusion is the task of aggregating multiple records that correspon...
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06/08/2020
Picket: Self-supervised Data Diagnostics for ML Pipelines
Data corruption is an impediment to modern machine learning deployments....
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06/06/2020
An Empirical Analysis of the Impact of Data Augmentation on Knowledge Distillation
Generalization Performance of Deep Learning models trained using the Emp...
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02/10/2020
Robust Mean Estimation under Coordinate-level Corruption
Data corruption, systematic or adversarial, may skew statistical estimat...
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10/28/2019
Fine-Grained Object Detection over Scientific Document Images with Region Embeddings
We study the problem of object detection over scanned images of scientif...
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06/29/2019
Approximate Inference in Structured Instances with Noisy Categorical Observations
We study the problem of recovering the latent ground truth labeling of a...
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05/30/2019
Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
Directed graphical models (DGMs) are a class of probabilistic models tha...
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05/04/2019
Learning Functional Dependencies with Sparse Regression
We study the problem of discovering functional dependencies (FD) from a ...
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04/04/2019
HoloDetect: Few-Shot Learning for Error Detection
We introduce a few-shot learning framework for error detection. We show ...
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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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01/21/2018