Srinadh Bhojanapalli

Orcid: 0000-0002-4147-2106

According to our database1, Srinadh Bhojanapalli authored at least 45 papers between 2014 and 2024.

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Bibliography

2024
Efficient Language Model Architectures for Differentially Private Federated Learning.
CoRR, 2024

HiRE: High Recall Approximate Top-k Estimation for Efficient LLM Inference.
CoRR, 2024

2023
Efficacy of Dual-Encoders for Extreme Multi-Label Classification.
CoRR, 2023

Functional Interpolation for Relative Positions Improves Long Context Transformers.
CoRR, 2023

Depth Dependence of μP Learning Rates in ReLU MLPs.
CoRR, 2023

On student-teacher deviations in distillation: does it pay to disobey?
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Treeformer: Dense Gradient Trees for Efficient Attention Computation.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Teacher's pet: understanding and mitigating biases in distillation.
Trans. Mach. Learn. Res., 2022

Large Models are Parsimonious Learners: Activation Sparsity in Trained Transformers.
CoRR, 2022

On the Adversarial Robustness of Mixture of Experts.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Robust Training of Neural Networks Using Scale Invariant Architectures.
Proceedings of the International Conference on Machine Learning, 2022

2021
Leveraging redundancy in attention with Reuse Transformers.
CoRR, 2021

Eigen Analysis of Self-Attention and its Reconstruction from Partial Computation.
CoRR, 2021

Demystifying the Better Performance of Position Encoding Variants for Transformer.
CoRR, 2021

On the Reproducibility of Neural Network Predictions.
CoRR, 2021

Coping with Label Shift via Distributionally Robust Optimisation.
Proceedings of the 9th International Conference on Learning Representations, 2021

Understanding Robustness of Transformers for Image Classification.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

A Simple and Effective Positional Encoding for Transformers.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

2020
Modifying Memories in Transformer Models.
CoRR, 2020

O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

An efficient nonconvex reformulation of stagewise convex optimization problems.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Does label smoothing mitigate label noise?
Proceedings of the 37th International Conference on Machine Learning, 2020

Low-Rank Bottleneck in Multi-head Attention Models.
Proceedings of the 37th International Conference on Machine Learning, 2020

Are Transformers universal approximators of sequence-to-sequence functions?
Proceedings of the 8th International Conference on Learning Representations, 2020

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes.
Proceedings of the 8th International Conference on Learning Representations, 2020

Semantic Label Smoothing for Sequence to Sequence Problems.
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

2019
The role of over-parametrization in generalization of neural networks.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks.
CoRR, 2018

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

Smoothed analysis for low-rank solutions to semidefinite programs in quadratic penalty form.
Proceedings of the Conference On Learning Theory, 2018

2017
Provable quantum state tomography via non-convex methods.
CoRR, 2017

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks.
CoRR, 2017

Stabilizing GAN Training with Multiple Random Projections.
CoRR, 2017

Exploring Generalization in Deep Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Implicit Regularization in Matrix Factorization.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
Provable non-convex projected gradient descent for a class of constrained matrix optimization problems.
CoRR, 2016

Single Pass PCA of Matrix Products.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Global Optimality of Local Search for Low Rank Matrix Recovery.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Dropping Convexity for Faster Semi-definite Optimization.
Proceedings of the 29th Conference on Learning Theory, 2016

2015
Completing any low-rank matrix, provably.
J. Mach. Learn. Res., 2015

A New Sampling Technique for Tensors.
CoRR, 2015

Tighter Low-rank Approximation via Sampling the Leveraged Element.
Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, 2015

2014
Coherent Matrix Completion.
Proceedings of the 31th International Conference on Machine Learning, 2014

Universal Matrix Completion.
Proceedings of the 31th International Conference on Machine Learning, 2014


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