Cedric Renggli | DeepAI

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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 ...

0 Wenqi Jiang, et al. ∙

∙ 06/12/2022

Stochastic Gradient Descent without Full Data Shuffle

Stochastic gradient descent (SGD) is the cornerstone of modern machine l...

39 Lijie Xu, et al. ∙

∙ 04/04/2022

SHiFT: An Efficient, Flexible Search Engine for Transfer Learning

Transfer learning can be seen as a data- and compute-efficient alternati...

21 Cedric Renggli, et al. ∙

∙ 03/08/2022

DeepSE-WF: Unified Security Estimation for Website Fingerprinting Defenses

Website fingerprinting (WF) attacks, usually conducted with the help of ...

0 Alexander Veicht, et al. ∙

∙ 02/24/2022

Learning to Merge Tokens in Vision Transformers

Transformers are widely applied to solve natural language understanding ...

3 Cedric Renggli, et al. ∙

∙ 12/15/2021

Dynamic Human Evaluation for Relative Model Comparisons

Collecting human judgements is currently the most reliable evaluation me...

0 Þórhildur Þorleiksdóttir, et al. ∙

∙ 08/30/2021

Evaluating Bayes Error Estimators on Read-World Datasets with FeeBee

The Bayes error rate (BER) is a fundamental concept in machine learning ...

0 Cedric Renggli, et al. ∙

∙ 02/17/2021

Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

Until recently, human behavioral data from reading has mainly been of in...

0 Nora Hollenstein, et al. ∙

∙ 02/15/2021

A Data Quality-Driven View of MLOps

Developing machine learning models can be seen as a process similar to t...

0 Cedric Renggli, et al. ∙

∙ 10/16/2020

On Automatic Feasibility Study for Machine Learning Application Development with ease.ml/snoopy

In our experience working with domain experts who are using today's Auto...

0 Cedric Renggli, et al. ∙

∙ 10/15/2020

On Convergence of Nearest Neighbor Classifiers over Feature Transformations

The k-Nearest Neighbors (kNN) classifier is a fundamental non-parametric...

13 Luka Rimanic, et al. ∙

∙ 10/13/2020

Which Model to Transfer? Finding the Needle in the Growing Haystack

Transfer learning has been recently popularized as a data-efficient alte...

19 Cedric Renggli, et al. ∙

∙ 09/28/2020

Scalable Transfer Learning with Expert Models

Transfer of pre-trained representations can improve sample efficiency an...

11 Joan Puigcerver, et al. ∙

∙ 10/08/2019

Lossy Image Compression with Recurrent Neural Networks: from Human Perceived Visual Quality to Classification Accuracy

Deep neural networks have recently advanced the state-of-the-art in imag...

31 Maurice Weber, et al. ∙

∙ 03/01/2019

Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment

Continuous integration is an indispensable step of modern software engin...

0 Cedric Renggli, et al. ∙

∙ 10/17/2018

Distributed Learning over Unreliable Networks

Most of today's distributed machine learning systems assume reliable ne...

0 Hanlin Tang, et al. ∙

∙ 09/27/2018

The Convergence of Sparsified Gradient Methods

Distributed training of massive machine learning models, in particular d...

0 Dan Alistarh, et al. ∙

∙ 02/22/2018

SparCML: High-Performance Sparse Communication for Machine Learning

One of the main drivers behind the rapid recent advances in machine lear...

0 Cedric Renggli, et al. ∙

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