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Found 13 papers, 5 papers with code
dS^2LBI: Exploring Structural Sparsity on Deep Network via Differential Inclusion Paths
Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error.
Evaluating Membership Inference Attacks and Defenses in Federated Learning
Firstly, combining model information from multiple communication rounds (Multi-temporal) enhances the overall effectiveness of MIAs compared to utilizing model information from a single epoch.
P-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models
Lastly, P-Bench performs existing privacy attacks on LMs with pre-defined privacy objectives as the empirical evaluation results.
ChatCounselor: A Large Language Models for Mental Health Support
This paper presents ChatCounselor, a large language model (LLM) solution designed to provide mental health support.
Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints
This paper investigates conservative exploration in reinforcement learning where the performance of the learning agent is guaranteed to be above a certain threshold throughout the learning process.
Random Smoothing Regularization in Kernel Gradient Descent Learning
In this paper, we aim to bridge this gap by presenting a framework for random smoothing regularization that can adaptively and effectively learn a wide range of ground truth functions belonging to the classical Sobolev spaces.
NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release
To further enhance the utility and address the label collapse issue when the mixup degree is large, we propose a Hierarchical sampling method to stratify the mixup samples on a small number of classes.
DessiLBI: Exploring Structural Sparsity of Deep Networks via Differential Inclusion Paths
Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error.
Split LBI for Deep Learning: Structural Sparsity via Differential Inclusion Paths
Over-parameterization is ubiquitous nowadays in training neural networks to benefit both optimization in seeking global optima and generalization in reducing prediction error.
Exploring Structural Sparsity of Deep Networks via Inverse Scale Spaces
To fill in this gap, this paper proposes a new approach based on differential inclusions of inverse scale spaces, which generate a family of models from simple to complex ones along the dynamics via coupling a pair of parameters, such that over-parameterized deep models and their structural sparsity can be explored simultaneously.
PRUNING IN TRAINING: LEARNING AND RANKING SPARSE CONNECTIONS IN DEEP CONVOLUTIONAL NETWORKS
This paper proposes a Pruning in Training (PiT) framework of learning to reduce the parameter size of networks.
$S^{2}$-LBI: Stochastic Split Linearized Bregman Iterations for Parsimonious Deep Learning
This paper proposes a novel Stochastic Split Linearized Bregman Iteration ($S^{2}$-LBI) algorithm to efficiently train the deep network.
Particle filter re-detection for visual tracking via correlation filters
Most of the correlation filter based tracking algorithms can achieve good performance and maintain fast computational speed.
