Europe
Understanding the Role of Momentum in Stochastic Gradient Methods
Igor Gitman, Hunter Lang, Pengchuan Zhang, Lin Xiao
Different variants ofmomentum, including heavyball momentum, Nesterov's accelerated gradient (NAG), and quasi-hyperbolic momentum (QHM), havedemonstrated success onvarious tasks. Our results are most closely related to the work of Mandt et al.[19]who use stationaryanalysis of SGD with momentum to perform approximateBayesianinference.
How Many Samples are Needed to Estimate a Convolutional Neural Network?
Simon S. Du, Yining Wang, Xiyu Zhai, Sivaraman Balakrishnan, Russ R. Salakhutdinov, Aarti Singh
Neural Information Processing Systems http://nips.cc/
PROSPECT: LabeledTandemMassSpectrometry DatasetforMachineLearninginProteomics
PROSPECT provides value to proteomics and machine learning researchers by including several high-quality annotations and by being accessible in terms of format and structure for applying machinelearning.
Learning SMaLL Predictors
Vikas Garg, Ofer Dekel, Lin Xiao
Neural Information Processing Systems http://nips.cc/
Multivariate Stochastic Dominance via Optimal Transport and Applications to Models Benchmarking
Large Language Models that are evaluated on multiple metrics.
AnoShift: ADistributionShiftBenchmarkfor UnsupervisedAnomalyDetection
Theexisting benchmarks arefocused onsupervised learning, andtothe best of our knowledge, there is none for unsupervised learning.