270 citations · 646 across the 26 of their papers we have counts for
4 papers · 1 filter
Stochastic Gradient Descent with Biased but Consistent Gradient Estimators
Jie Chen, Ronny Luss
Stochastic gradient descent (SGD), which dates back to the 1950s, is one of the most popular and effective approaches for performing stochastic optimization. Research on SGD resurg…
Improving Simple Models with Confidence Profiles
Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss +1
In this paper, we propose a new method called ProfWeight for transferring information from a pre-trained deep neural network that has a high test accuracy to a simpler interpretabl…
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel +8
Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective function. State-of-the-art metho…
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss +4
In this paper we propose a novel method that provides contrastive explanations justifying the classification of an input by a black box classifier such as a deep neural network. Gi…