79 citations · 194 across the 39 of their papers we have counts for
6 papers · 1 filter
A Heterogeneous Chiplet Architecture for Accelerating End-to-End Transformer Models
Harsh Sharma, Pratyush Dhingra, Janardhan Rao Doppa +2
Transformers have revolutionized deep learning and generative modeling, enabling advancements in natural language processing tasks. However, the size of transformer models is incre…
Block-Wise Mixed-Precision Quantization: Enabling High Efficiency for Practical ReRAM-based DNN Accelerators
Xueying Wu, Edward Hanson, Nansu Wang +9
Resistive random access memory (ReRAM)-based processing-in-memory (PIM) architectures have demonstrated great potential to accelerate Deep Neural Network (DNN) training/inference.…
Probabilistically robust conformal prediction
Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja +3
Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, C…
Preference-Aware Constrained Multi-Objective Bayesian Optimization
Alaleh Ahmadianshalchi, Syrine Belakaria, Janardhan Rao Doppa
This paper addresses the problem of constrained multi-objective optimization over black-box objective functions with practitioner-specified preferences over the objectives when a l…
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis
Subhankar Ghosh, Taha Belkhouja, Yan Yan +1
Safe deployment of deep neural networks in high-stake real-world applications requires theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled fra…
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
Aryan Deshwal, Sebastian Ament, Maximilian Balandat +3
We consider the problem of optimizing expensive black-box functions over high-dimensional combinatorial spaces which arises in many science, engineering, and ML applications. We us…