Suraj Srinivas | DeepAI

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∙ 09/06/2023

Certifying LLM Safety against Adversarial Prompting

Large language models (LLMs) released for public use incorporate guardra...

0 Aounon Kumar, et al. ∙

∙ 07/27/2023

Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability

With the increased deployment of machine learning models in various real...

0 Usha Bhalla, et al. ∙

∙ 07/26/2023

Efficient Estimation of the Local Robustness of Machine Learning Models

Machine learning models often need to be robust to noisy input data. The...

0 Tessa Han, et al. ∙

∙ 06/11/2023

On Minimizing the Impact of Dataset Shifts on Actionable Explanations

The Right to Explanation is an important regulatory principle that allow...

0 Anna P. Meyer, et al. ∙

∙ 06/09/2023

Consistent Explanations in the Face of Model Indeterminacy via Ensembling

This work addresses the challenge of providing consistent explanations f...

0 Dan Ley, et al. ∙

∙ 05/30/2023

Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness

One of the remarkable properties of robust computer vision models is tha...

0 Suraj Srinivas, et al. ∙

∙ 06/14/2022

Flatten the Curve: Efficiently Training Low-Curvature Neural Networks

The highly non-linear nature of deep neural networks causes them to be s...

0 Suraj Srinivas, et al. ∙

∙ 06/02/2022

Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations

Despite the plethora of post hoc model explanation methods, the basic pr...

0 Tessa Han, et al. ∙

∙ 03/09/2022

Data-Efficient Structured Pruning via Submodular Optimization

Structured pruning is an effective approach for compressing large pre-tr...

8 Marwa El Halabi, et al. ∙

∙ 02/02/2022

Cyclical Pruning for Sparse Neural Networks

Current methods for pruning neural network weights iteratively apply mag...

12 Suraj Srinivas, et al. ∙

∙ 06/16/2020

Gradient Alignment in Deep Neural Networks

One cornerstone of interpretable deep learning is the high degree of vis...

17 Suraj Srinivas, et al. ∙

∙ 05/02/2019

Full-Jacobian Representation of Neural Networks

Non-linear functions such as neural networks can be locally approximated...

70 Suraj Srinivas, et al. ∙

∙ 03/01/2018

Knowledge Transfer with Jacobian Matching

Classical distillation methods transfer representations from a "teacher"...

0 Suraj Srinivas, et al. ∙

∙ 07/21/2017

Confidence estimation in Deep Neural networks via density modelling

State-of-the-art Deep Neural Networks can be easily fooled into providin...

0 Akshayvarun Subramanya, et al. ∙

∙ 11/21/2016

Generalized Dropout

Deep Neural Networks often require good regularizers to generalize well....

0 Suraj Srinivas, et al. ∙

∙ 11/21/2016

Training Sparse Neural Networks

Deep neural networks with lots of parameters are typically used for larg...

0 Suraj Srinivas, et al. ∙

∙ 11/17/2016

Compensating for Large In-Plane Rotations in Natural Images

Rotation invariance has been studied in the computer vision community pr...

0 Lokesh Boominathan, et al. ∙

∙ 01/25/2016

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the d...

0 Suraj Srinivas, et al. ∙

∙ 11/17/2015

Learning Neural Network Architectures using Backpropagation

Deep neural networks with millions of parameters are at the heart of man...

0 Suraj Srinivas, et al. ∙

∙ 07/22/2015

Data-free parameter pruning for Deep Neural Networks

Deep Neural nets (NNs) with millions of parameters are at the heart of m...

0 Suraj Srinivas, et al. ∙

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