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Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels
Shahin Shahrampour, Vahid Tarokh
Ourmethod, dubbed Multi Feature Greedy Approximation (MFGA), hasaccess features.
54024fca0cef9911be36319e622cde38-Paper-Conference.pdf
With a seed set of manually-identified tactics, we apply GPT -4 to expand the discovery automatically.
Learning to Teach with Dynamic Loss Functions
Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Lai Jian-Huang, Tie-Yan Liu
Theultimate goalofteacher model is cultivating the student to have better performance measured on development dataset.
Foundations of Comparison-Based Hierarchical Clustering
Debarghya Ghoshdastidar, Michaël Perrot, Ulrike von Luxburg
Neural Information Processing Systems http://nips.cc/
ManifoldDenoisingbyNonlinearRobustPrincipal ComponentAnalysis
Theoretical error bounds are provided when the tangent spaces of the manifold satisfy certain incoherence conditions.
With Friends Like These, Who Needs Adversaries?
Saumya Jetley, Nicholas Lord, Philip Torr
The vulnerability of deep image classification networks to adversarial attack is nowwellknown,butlesswellunderstood.
Generalizable Person Re-identification via Balancing Alignment and Uniformity
Domain generalizable person re-identification (DG re-ID) aims to learn discriminative representations that are robust to distributional shifts.
Deep Generative Models for Distribution-Preserving Lossy Compression
Michael Tschannen, Eirikur Agustsson, Mario Lucic
Neural Information Processing Systems http://nips.cc/
a7471fdc77b3435276507cc8f2dc2569-Paper.pdf
We propose simple robust variants of two notions that havebeen considered inrecent literature: (in)fidelity,and sensitivity.