1 citations · 1 across the 2 of their papers we have counts for
5 papers
Accelerating Markov Random Field Inference with Uncertainty Quantification
Ramin Bashizade, Xiangyu Zhang, Sayan Mukherjee +1
Statistical machine learning has widespread application in various domains. These methods include probabilistic algorithms, such as Markov Chain Monte-Carlo (MCMC), which rely on g…
Lightweight Inter-transaction Caching with Precise Clocks and Dynamic Self-invalidation
Pulkit A. Misra, Srihari Radhakrishnan, Jeffrey S. Chase +2
Distributed, transactional storage systems scale by sharding data across servers. However, workload-induced hotspots result in contention, leading to higher abort rates and perform…
Beyond Application End-Point Results: Quantifying Statistical Robustness of MCMC Accelerators
Xiangyu Zhang, Ramin Bashizade, Yicheng Wang +3
Statistical machine learning often uses probabilistic algorithms, such as Markov Chain Monte Carlo (MCMC), to solve a wide range of problems. Probabilistic computations, often cons…
Multi-version Indexing in Flash-based Key-Value Stores
Pulkit A. Misra, Jeffrey S. Chase, Johannes Gehrke +1
Maintaining multiple versions of data is popular in key-value stores since it increases concurrency and improves performance. However, designing a multi-version key-value store ent…
A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators
Xiangyu Zhang, Sayan Mukherjee, Alvin R. Lebeck
Statistical machine learning often uses probabilistic algorithms, such as Markov Chain Monte Carlo (MCMC), to solve a wide range of problems. Many accelerators are proposed using s…