Search Results for author:
Found 18 papers, 4 papers with code
Adaptive Regularized Newton Method for Riemannian Optimization
Optimization on Riemannian manifolds widely arises in eigenvalue computation, density functional theory, Bose-Einstein condensates, low rank nearest correlation, image registration, and signal processing, etc.
Optimization and Control
Structured Quasi-Newton Methods for Optimization with Orthogonality Constraints
In particular, we are interested in applications that the Euclidean Hessian itself consists of a computational cheap part and a significantly expensive part.
Optimization and Control
Automatic Microprocessor Performance Bug Detection
Design bugs that affect processor performance rather than its functionality are especially difficult to catch, particularly in new microarchitectures.
PowerNet: Transferable Dynamic IR Drop Estimation via Maximum Convolutional Neural Network
Moreover, the proposed CNN model is general and transferable to different designs.
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning
Experimental results on benchmark circuits show that our approach achieves 25% improvement in design quality or 37% reduction in sampling cost compared to random forest method, which is the kernel of a highly cited previous work.
Fast IR Drop Estimation with Machine Learning
IR drop constraint is a fundamental requirement enforced in almost all chip designs.
Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation
Net length is a key proxy metric for optimizing timing and power across various stages of a standard digital design flow.
Automatic Routability Predictor Development Using Neural Architecture Search
The rise of machine learning technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs.
Toward Taming the Overhead Monster for Data-Flow Integrity
Data-Flow Integrity (DFI) is a well-known approach to effectively detecting a wide range of software attacks.
Hardware Architecture
Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data
To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario.
Riemannian Natural Gradient Methods
Moreover, we show that if the loss function satisfies certain convexity and smoothness conditions and the input-output map satisfies a Riemannian Jacobian stability condition, then our proposed method enjoys a local linear -- or, under the Lipschitz continuity of the Riemannian Jacobian of the input-output map, even quadratic -- rate of convergence.
Decentralized Riemannian natural gradient methods with Kronecker-product approximations
With a computationally efficient approximation of the second-order information, natural gradient methods have been successful in solving large-scale structured optimization problems.
Decentralized Weakly Convex Optimization Over the Stiefel Manifold
We focus on a class of non-smooth optimization problems over the Stiefel manifold in the decentralized setting, where a connected network of $n$ agents cooperatively minimize a finite-sum objective function with each component being weakly convex in the ambient Euclidean space.
Radiology-Llama2: Best-in-Class Large Language Model for Radiology
This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning.
Composite federated learning with heterogeneous data
We propose a novel algorithm for solving the composite Federated Learning (FL) problem.
MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation
The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks.
MediViSTA-SAM: Zero-shot Medical Video Analysis with Spatio-temporal SAM Adaptation for Echocardiography
The Segmentation Anything Model (SAM) has gained significant attention for its robust generalization capabilities across diverse downstream tasks.
AdaFish: Fast low-rank parameter-efficient fine-tuning by using second-order information
Our key observation is that the associated generalized Fisher information matrix is either low-rank or extremely small-scaled.
