Dibyakanti Kumar
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View article: Langevin Monte-Carlo Provably Learns Depth Two Neural Nets at Any Size and Data
Langevin Monte-Carlo Provably Learns Depth Two Neural Nets at Any Size and Data Open
In this work, we will establish that the Langevin Monte-Carlo algorithm can learn depth-2 neural nets of any size and for any data and we give non-asymptotic convergence rates for it. We achieve this via showing that under Total Variation …
View article: Investigating the ability of PINNs to solve Burgers’ PDE near finite-time blowup
Investigating the ability of PINNs to solve Burgers’ PDE near finite-time blowup Open
Physics Informed Neural Networks (PINNs) have been achieving ever newer feats of solving complicated Partial Differential Equations (PDEs) numerically while offering an attractive trade-off between accuracy and speed of inference. A partic…
View article: Towards Size-Independent Generalization Bounds for Deep Operator Nets
Towards Size-Independent Generalization Bounds for Deep Operator Nets Open
View article: Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp
Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp Open
Physics Informed Neural Networks (PINNs) have been achieving ever newer feats of solving complicated PDEs numerically while offering an attractive trade-off between accuracy and speed of inference. A particularly challenging aspect of PDEs…
View article: Realistic Data Augmentation Framework for Enhancing Tabular Reasoning
Realistic Data Augmentation Framework for Enhancing Tabular Reasoning Open
Existing approaches to constructing training data for Natural Language Inference (NLI) tasks, such as for semi-structured table reasoning, are either via crowdsourcing or fully automatic methods. However, the former is expensive and time-c…
View article: Realistic Data Augmentation Framework for Enhancing Tabular Reasoning
Realistic Data Augmentation Framework for Enhancing Tabular Reasoning Open
Existing approaches to constructing training data for Natural Language Inference (NLI) tasks, such as for semi-structured table reasoning, are either via crowdsourcing or fully automatic methods. However, the former is expensive and time c…