Search Results for author:
Found 9 papers, 3 papers with code
Can Separators Improve Chain-of-Thought Prompting?
The basic idea of CoT is to let LLMs break down their thought processes step-by-step by putting exemplars in the input prompt.
PartSTAD: 2D-to-3D Part Segmentation Task Adaptation
Our proposed task adaptation method finetunes a 2D bounding box prediction model with an objective function for 3D segmentation.
PEMA: An Offsite-Tunable Plug-in External Memory Adaptation for Language Models
PEMA integrates with context representations from test data during inference to perform downstream tasks.
Toward Reinforcement Learning-based Rectilinear Macro Placement Under Human Constraints
Macro placement is a critical phase in chip design, which becomes more intricate when involving general rectilinear macros and layout areas.
ExMobileViT: Lightweight Classifier Extension for Mobile Vision Transformer
This paper is motivated by the idea that the data itself from early attention stages can have important meaning for the final classification.
CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps
In each quantization step, the trained weights of a model are used to initialize the weights of the next model with the quantization bit depth reduced by one.
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian
We propose a framework that can deform an object in a 2D image as it exists in 3D space.
PLAM: a Posit Logarithm-Approximate Multiplier
The Posit Number System was introduced in 2017 as a replacement for floating-point numbers.
The Effects of Approximate Multiplication on Convolutional Neural Networks
The approximate multiplication can reduce the cost of the underlying circuits so that CNN inferences can be performed more efficiently in hardware accelerators.
