Search Results for author:
Found 17 papers, 2 papers with code
SocraSynth: Multi-LLM Reasoning with Conditional Statistics
In the knowledge generation phase, the moderator defines the debate topic and contentiousness level, prompting the agents to formulate supporting arguments for their respective stances.
Prompting Large Language Models With the Socratic Method
This paper presents a systematic approach to using the Socratic method in developing prompt templates that effectively interact with large language models, including GPT-3.
Knowledge-Guided Data-Centric AI in Healthcare: Progress, Shortcomings, and Future Directions
The success of deep learning is largely due to the availability of large amounts of training data that cover a wide range of examples of a particular concept or meaning.
RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes
Our method is capable of modifying images by changing particular attributes of interest in a continuous manner while preserving the other attributes.
Effective Medical Test Suggestions Using Deep Reinforcement Learning
Effective medical test suggestions benefit both patients and physicians to conserve time and improve diagnosis accuracy.
G2R Bound: A Generalization Bound for Supervised Learning from GAN-Synthetic Data
Performing supervised learning from the data synthesized by using Generative Adversarial Networks (GANs), dubbed GAN-synthetic data, has two important applications.
KG-GAN: Knowledge-Guided Generative Adversarial Networks
Can generative adversarial networks (GANs) generate roses of various colors given only roses of red petals as input?
MBS: Macroblock Scaling for CNN Model Reduction
In this paper we propose the macroblock scaling (MBS) algorithm, which can be applied to various CNN architectures to reduce their model size.
BRIEF: Backward Reduction of CNNs with Information Flow Analysis
This paper proposes BRIEF, a backward reduction algorithm that explores compact CNN-model designs from the information flow perspective.
EA-CG: An Approximate Second-Order Method for Training Fully-Connected Neural Networks
For training fully-connected neural networks (FCNNs), we propose a practical approximate second-order method including: 1) an approximation of the Hessian matrix and 2) a conjugate gradient (CG) based method.
Distributed Training Large-Scale Deep Architectures
Scale of data and scale of computation infrastructures together enable the current deep learning renaissance.
Representation Learning on Large and Small Data
Deep learning owes its success to three key factors: scale of data, enhanced models to learn representations from data, and scale of computation.
CLKN: Cascaded Lucas-Kanade Networks for Image Alignment
The main component of this architecture is a Lucas-Kanade layer that performs the inverse compositional algorithm on convolutional feature maps.
A New Retraction for Accelerating the Riemannian Three-Factor Low-Rank Matrix Completion Algorithm
In the line search step, R3MC approximates the minimum point on the searching curve by minimizing on the line tangent to the curve.
Errata: Distant Supervision for Relation Extraction with Matrix Completion
The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features.
Parallelizing Support Vector Machines on Distributed Computers
Support Vector Machines (SVMs) suffer from a widely recognized scalability problem in both memory use and computational time.
