29 citations · 101 across the 19 of their papers we have counts for
8 papers · 1 filter
RL4ReAl: Reinforcement Learning for Register Allocation
S. VenkataKeerthy, Siddharth Jain, Anilava Kundu +3
We aim to automate decades of research and experience in register allocation, leveraging machine learning. We tackle this problem by embedding a multi-agent reinforcement learning…
Expert Graphs: Synthesizing New Expertise via Collaboration
Bijan Mazaheri, Siddharth Jain, Jehoshua Bruck
Consider multiple experts with overlapping expertise working on a classification problem under uncertain input. What constitutes a consistent set of opinions? How can we predict th…
CodNN -- Robust Neural Networks From Coded Classification
Netanel Raviv, Siddharth Jain, Pulakesh Upadhyaya +2
Deep Neural Networks (DNNs) are a revolutionary force in the ongoing information revolution, and yet their intrinsic properties remain a mystery. In particular, it is widely known…
Overinterpretation reveals image classification model pathologies
Brandon Carter, Siddhartha Jain, Jonas Mueller +1
Image classifiers are typically scored on their test set accuracy, but high accuracy can mask a subtle type of model failure. We find that high scoring convolutional neural network…
Information Condensing Active Learning
Siddhartha Jain, Ge Liu, David Gifford
We introduce Information Condensing Active Learning (ICAL), a batch mode model agnostic Active Learning (AL) method targeted at Deep Bayesian Active Learning that focuses on acquir…
What is the Value of Data? On Mathematical Methods for Data Quality Estimation
Netanel Raviv, Siddharth Jain, Jehoshua Bruck
Data is one of the most important assets of the information age, and its societal impact is undisputed. Yet, rigorous methods of assessing the quality of data are lacking. In this…
Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles
Siddhartha Jain, Ge Liu, Jonas Mueller +1
The inaccuracy of neural network models on inputs that do not stem from the training data distribution is both problematic and at times unrecognized. Model uncertainty estimation c…
What made you do this? Understanding black-box decisions with sufficient input subsets
Brandon Carter, Jonas Mueller, Siddhartha Jain +1
Local explanation frameworks aim to rationalize particular decisions made by a black-box prediction model. Existing techniques are often restricted to a specific type of predictor…