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In this paper, we extend original Neural Collapse Phenomenon by proving Generalized Neural Collapse hypothesis.
Xxiv. on the structure of the atom: an investigation of the stability and periods of oscillation of a number of corpuscles arranged at equal intervals around the circumference of a circle; with application of the results to the theory of atomic structure
J.J. Thomson F.R.S · 1904
Earlier work this paper cites.
Probability of error for optimal codes in a gaussian channel
Claude E Shannon · 1959
Earlier work this paper cites.
Lower bounds on the maximum cross correlation of signals (corresp.)
Lloyd Welch · 1974
Earlier work this paper cites.
Rates of convergence of nearest neighbor estimation under arbitrary sampling
Sanjeev R Kulkarni and Steven E Posner · 1995
Earlier work this paper cites.
Packing lines, planes, etc.: Packings in grassmannian spaces
John H Conway, Ronald H Hardin, and Neil JA Sloane · 1996
Earlier work this paper cites.
Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2002
Earlier work this paper cites.
Equal-norm tight frames with erasures
Peter G Casazza and Jelena Kovačević · 2003
Earlier work this paper cites.
Grassmannian frames with applications to coding and communication
Thomas Strohmer and Robert W Heath Jr · 2003
Earlier work this paper cites.
Optimal frames for erasures
Roderick B Holmes and Vern I Paulsen · 2004
Earlier work this paper cites.
Frames, graphs and erasures
Bernhard G Bodmann and Vern I Paulsen · 2005
Earlier work this paper cites.
Geometric properties of grassmannian frames for r2 and r3
John Benedetto and Joseph Kolesar · 2006
Earlier work this paper cites.
On the complexity of linear prediction: Risk bounds, margin bounds, and regularization
Sham M Kakade, Karthik Sridharan, and Ambuj Tewari · 2008
Earlier work this paper cites.
Optimal dual frames for erasures
Jerry Lopez and Deguang Han · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
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