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Found 16 papers, 8 papers with code
Evaluating Machine Accuracy on ImageNet
We perform an in-depth evaluation of human accuracy on the ImageNet dataset.
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)
Contrastively trained image-text models such as CLIP, ALIGN, and BASIC have demonstrated unprecedented robustness to multiple challenging natural distribution shifts.
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments.
Predicting with Confidence on Unseen Distributions
Our work connects techniques from domain adaptation and predictive uncertainty literature, and allows us to predict model accuracy on challenging unseen distributions without access to labeled data.
A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery
Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use.
Measuring Robustness to Natural Distribution Shifts in Image Classification
We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets.
Neural Kernels Without Tangents
We investigate the connections between neural networks and simple building blocks in kernel space.
Serverless Straggler Mitigation using Local Error-Correcting Codes
Inexpensive cloud services, such as serverless computing, are often vulnerable to straggling nodes that increase end-to-end latency for distributed computation.
Distributed, Parallel, and Cluster Computing Information Theory Information Theory
A Meta-Analysis of Overfitting in Machine Learning
By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition.
When Robustness Doesn’t Promote Robustness: Synthetic vs. Natural Distribution Shifts on ImageNet
We conduct a large experimental comparison of various robustness metrics for image classification.
Do Image Classifiers Generalize Across Time?
Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points.
A Systematic Framework for Natural Perturbations from Videos
We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos.
Do ImageNet Classifiers Generalize to ImageNet?
We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet.
Cloud Programming Simplified: A Berkeley View on Serverless Computing
Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud.
Operating Systems
Do CIFAR-10 Classifiers Generalize to CIFAR-10?
Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models.
Flare Prediction Using Photospheric and Coronal Image Data
This is the first attempt to predict solar flares using photospheric vector magnetic field data as well as multiple wavelengths of image data from the chromosphere, transition region, and corona.
