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Found 10 papers, 2 papers with code
Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering
Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems.
Why the pseudo label based semi-supervised learning algorithm is effective?
The core idea of the pseudo label based semi-supervised learning algorithm is to use the model trained on the labeled data to generate pseudo labels on the unlabeled data, and then train a model to fit the previously generated pseudo labels.
Exploring and Enhancing the Transferability of Adversarial Examples
State-of-the-art deep neural networks are vulnerable to adversarial examples, formed by applying small but malicious perturbations to the original inputs.
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations
We develop the mathematical foundations of the stochastic modified equations (SME) framework for analyzing the dynamics of stochastic gradient algorithms, where the latter is approximated by a class of stochastic differential equations with small noise parameters.
Understanding and Enhancing the Transferability of Adversarial Examples
State-of-the-art deep neural networks are known to be vulnerable to adversarial examples, formed by applying small but malicious perturbations to the original inputs.
Enhancing the Transferability of Adversarial Examples with Noise Reduced Gradient
Deep neural networks provide state-of-the-art performance for many applications of interest.
Maximum Principle Based Algorithms for Deep Learning
The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms.
Stochastic modified equations and adaptive stochastic gradient algorithms
We develop the method of stochastic modified equations (SME), in which stochastic gradient algorithms are approximated in the weak sense by continuous-time stochastic differential equations.
Convolutional neural networks with low-rank regularization
Recently, tensor decompositions have been used for speeding up CNNs.
Multiscale Adaptive Representation of Signals: I. The Basic Framework
The new framework, called AdaFrame, improves over dictionary learning-based techniques in terms of computational efficiency at inference time.
