On Averaging ROC Curves

Receiver operating characteristic (ROC) curves are a popular method of summarising the performance of classifiers. The ROC curve describes the separability of the distributions of predictions from...

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How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Fu...

Jorge A Mendez, ERIC EATON

https://openreview.net/forum?id=VynY6Bk03b

#lifelong #knowledge #composition

How to Reuse and Compose Knowledge for a Lifetime of Tasks: A...

A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general understanding of the world. Such an agent would require the ability to continually accumulate and...

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Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training

Utku Ozbulak, Hyun Jung Lee, Beril Boga et al.

https://openreview.net/forum?id=Ma25S4ludQ

#supervised #discriminative #generative

Know Your Self-supervised Learning: A Survey on Image-based...

Although supervised learning has been highly successful in improving the state-of-the-art in the domain of image-based computer vision in the past, the margin of improvement has diminished...

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Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with M...

Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, Tommi S. Jaakkola

https://openreview.net/forum?id=A8pqQipwkt

#molecular #molecules #benchmarked

Forces are not Enough: Benchmark and Critical Evaluation for...

Molecular dynamics (MD) simulation techniques are widely used for various natural science applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab-initio...

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Partition-Based Active Learning for Graph Neural Networks

Jiaqi Ma, Ziqiao Ma, Joyce Chai, Qiaozhu Mei

https://openreview.net/forum?id=e0xaRylNuT

#graphpart #supervised #classification

Partition-Based Active Learning for Graph Neural Networks

We study the problem of semi-supervised learning with Graph Neural Networks (GNNs) in an active learning setup. We propose GraphPart, a novel partition-based active learning approach for GNNs....

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Better Theory for SGD in the Nonconvex World

Ahmed Khaled, Peter Richtárik

https://openreview.net/forum?id=AU4qHN2VkS

#sgd #optimal #stochastic

Better Theory for SGD in the Nonconvex World

Large-scale nonconvex optimization problems are ubiquitous in modern machine learning, and among practitioners interested in solving them, Stochastic Gradient Descent (SGD) reigns supreme. We...

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A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey

Elahe Arani, Shruthi Gowda, Ratnajit Mukherjee et al.

https://openreview.net/forum?id=ywr5sWqQt4

#detectors #benchmark #adversarial

A Comprehensive Study of Real-Time Object Detection Networks Across...

Deep neural network based object detectors are continuously evolving and are used in a multitude of applications, each having its own set of requirements. While safety-critical applications need...

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On the link between conscious function and general intelligence in humans and machines

Arthur Juliani, Kai Arulkumaran, Shuntaro Sasai, Ryota Kanai

https://openreview.net/forum?id=LTyqvLEv5b

#cognitive #intelligence #consciousness

On the link between conscious function and general intelligence in...

In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence. In...

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Structural Learning in Artificial Neural Networks: A Neural Operator Perspective

Kaitlin Maile, Luga Hervé, Dennis George Wilson

https://openreview.net/forum?id=gzhEGhcsnN

#synaptic #synaptogenesis #neurogenesis

Structural Learning in Artificial Neural Networks: A Neural...

Over the history of Artificial Neural Networks (ANNs), only a minority of algorithms integrate structural changes of the network architecture into the learning process. Modern neuroscience has...

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A Snapshot of the Frontiers of Client Selection in Federated Learning

Gergely Dániel Németh, Miguel Angel Lozano, Novi Quadrianto, Nuria M Oliver

https://openreview.net/forum?id=vwOKBldzFu

#federated #privacy #distributed

A Snapshot of the Frontiers of Client Selection in Federated Learning

Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a number of clients...

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