Search Results for author: Ankur Taly

Found 16 papers, 6 papers with code

Which Pretrain Samples to Rehearse when Finetuning Pretrained Models?

no code implementations12 Feb 2024 Andrew Bai, Chih-Kuan Yeh, Cho-Jui Hsieh, Ankur Taly

We propose a novel sampling scheme, mix-cd, that identifies and prioritizes samples that actually face forgetting, which we call collateral damage.

Interpretable Mixture of Experts

no code implementations5 Jun 2022 Aya Abdelsalam Ismail, Sercan Ö. Arik, Jinsung Yoon, Ankur Taly, Soheil Feizi, Tomas Pfister

In addition to constituting a standalone inherently-interpretable architecture, IME has the premise of being integrated with existing DNNs to offer interpretability to a subset of samples while maintaining the accuracy of the DNNs.

Decision Making Time Series

First is Better Than Last for Language Data Influence

1 code implementation24 Feb 2022 Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan, Frederick Liu, Pradeep Ravikumar

However, we observe that since the activation connected to the last layer of weights contains "shared logic", the data influenced calculated via the last layer weights prone to a ``cancellation effect'', where the data influence of different examples have large magnitude that contradicts each other.

The Explanation Game: Explaining Machine Learning Models Using Shapley Values

1 code implementation17 Sep 2019 Luke Merrick, Ankur Taly

While existing papers focus on the axiomatic motivation of Shapley values, and efficient techniques for computing them, they offer little justification for the game formulations used, and do not address the uncertainty implicit in their methods' outputs.

BIG-bench Machine Learning

Property Inference for Deep Neural Networks

1 code implementation29 Apr 2019 Divya Gopinath, Hayes Converse, Corina S. Pasareanu, Ankur Taly

We present techniques for automatically inferring formal properties of feed-forward neural networks.

Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry

no code implementations27 Nov 2018 Kevin McCloskey, Ankur Taly, Federico Monti, Michael P. Brenner, Lucy Colwell

The dataset bias makes these models unreliable for accurately revealing information about the mechanisms of protein-ligand binding.

Counterfactual Fairness in Text Classification through Robustness

no code implementations27 Sep 2018 Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, Alex Beutel

In this paper, we study counterfactual fairness in text classification, which asks the question: How would the prediction change if the sensitive attribute referenced in the example were different?

Attribute counterfactual +4

A Note about: Local Explanation Methods for Deep Neural Networks lack Sensitivity to Parameter Values

no code implementations11 Jun 2018 Mukund Sundararajan, Ankur Taly

Local explanation methods, also known as attribution methods, attribute a deep network's prediction to its input (cf.


Did the Model Understand the Question?

4 code implementations ACL 2018 Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, Kedar Dhamdhere

Our strongest attacks drop the accuracy of a visual question answering model from $61. 1\%$ to $19\%$, and that of a tabular question answering model from $33. 5\%$ to $3. 3\%$.

Question Answering Visual Question Answering

It was the training data pruning too!

no code implementations12 Mar 2018 Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, Kedar Dhamdhere

The large impact on the performance of the KDG model suggests that the pruning may be a useful pre-processing step in training other semantic parsers as well.

Question Answering

Axiomatic Attribution for Deep Networks

33 code implementations ICML 2017 Mukund Sundararajan, Ankur Taly, Qiqi Yan

We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works.

Explainable artificial intelligence Interpretable Machine Learning

Gradients of Counterfactuals

no code implementations8 Nov 2016 Mukund Sundararajan, Ankur Taly, Qiqi Yan

Unfortunately, in nonlinear deep networks, not only individual neurons but also the whole network can saturate, and as a result an important input feature can have a tiny gradient.

counterfactual Feature Importance +2

Cannot find the paper you are looking for? You can Submit a new open access paper.