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Found 6 papers, 4 papers with code
Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models
Leveraging this approximation, we adapt the original model to eliminate information from the forgotten data at the representation level.
Vosh: Voxel-Mesh Hybrid Representation for Real-Time View Synthesis
The neural radiance field (NeRF) has emerged as a prominent methodology for synthesizing realistic images of novel views.
GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps.
CaMU: Disentangling Causal Effects in Deep Model Unlearning
To address this shortcoming, the present study undertakes a causal analysis of the unlearning and introduces a novel framework termed Causal Machine Unlearning (CaMU).
AdaNIC: Towards Practical Neural Image Compression via Dynamic Transform Routing
Compressive autoencoders (CAEs) play an important role in deep learning-based image compression, but large-scale CAEs are computationally expensive.
A Boosting Algorithm for Positive-Unlabeled Learning
This is because AdaPU learns a weak classifier and its weight using a weighted positive-negative (PN) dataset with some negative data weights $-$ the dataset is derived from the original PU data, and the data weights are determined by the current weighted classifier combination, but some data weights are negative.
