Understanding Deep Neural Networks with Rectified Linear Units
1 version
AM
Anirbit Mukherjee
cs.LGstat.MLmath.OCcs.NAmath.NAmath.APcs.AIcs.CCcs.ITmath.DS
On Valency
published · living versionsPreprints & journals
23 papers in the corpus · 2013–2026Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise and Hölder Smoothness
0 citations
Towards a Foundation Model for the Martian Atmosphere
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Convergent Stochastic Training of Multi-Headed Attention and Understanding LoRA
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Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
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Noisy PDE Training Requires Bigger PINNs
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Improving PINNs By Algebraic Inclusion of Boundary and Initial Conditions
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Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
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Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp
2 citations
LIPEx-Locally Interpretable Probabilistic Explanations-To Look Beyond The True Class
0 citations
Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets
0 citations
Size Lowerbounds for Deep Operator Networks
1 citation
Global Convergence of SGD On Two Layer Neural Nets
2 citations
Towards Size-Independent Generalization Bounds for Deep Operator Nets
2 citations
An Empirical Study of the Occurrence of Heavy-Tails in Training a ReLU Gate
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Provable training of a ReLU gate with an iterative non-gradient algorithm.
5 citations
Dynamics of Local Elasticity During Training of Neural Nets
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A Study of the Mathematics of Deep Learning
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Depth-2 Neural Networks Under a Data-Poisoning Attack
2 citations
Convergence guarantees for RMSProp and ADAM in non-convex optimization and an empirical comparison to Nesterov acceleration
84 citations
Sparse Coding and Autoencoders
13 citations
Understanding Deep Neural Networks with Rectified Linear Units
372 citations
Renyi entropy of the critical O(N) model
0 citations
N-point correlations of dark matter tracers : Renormalization with univariate biasing and its O(f_NL) terms with bivariate biasing
0 citations
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