Search Results for author:
Found 6 papers, 1 papers with code
Natural Language Fine-Tuning
Compared to SFT, NLFT does not increase the algorithmic complexity, maintaining O(n).
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations
Traditional deep Gaussian processes model the data evolution using a discrete hierarchy, whereas differential Gaussian processes (DIFFGPs) represent the evolution as an infinitely deep Gaussian process.
Flexible Bayesian Last Layer Models Using Implicit Priors and Diffusion Posterior Sampling
Bayesian Last Layer (BLL) models focus solely on uncertainty in the output layer of neural networks, demonstrating comparable performance to more complex Bayesian models.
Tessel: Boosting Distributed Execution of Large DNN Models via Flexible Schedule Search
This paper presents Tessel, an automated system that searches for efficient schedules for distributed DNN training and inference for diverse operator placement strategies.
SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction
SuperScaler is a system that facilitates the design and generation of highly flexible parallelization plans.
PaGraph: Scaling GNN Training on Large Graphs via Computation-aware Caching and Partitioning
Emerging graph neural networks (GNNs) have extended the successes of deep learning techniques against datasets like images and texts to more complex graph-structured data.
