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Found 9 papers, 8 papers with code
MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models
We evaluate the multilingual knowledge editing generalization capabilities of existing methods on MLaKE.
MicroDiffusion: Implicit Representation-Guided Diffusion for 3D Reconstruction from Limited 2D Microscopy Projections
This strategy enriches the diffusion process with structured 3D information, enhancing detail and reducing noise in localized 2D images.
Stable Knowledge Editing in Large Language Models
The premise of localization results in an incomplete knowledge editing, whereas an isolated assumption may impair both other knowledge and general abilities.
Retrieve Only When It Needs: Adaptive Retrieval Augmentation for Hallucination Mitigation in Large Language Models
A careful and balanced integration of the parametric knowledge within LLMs with external information is crucial to alleviate hallucinations.
SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation
To address this limitation, we present Masked Multi-view with Swin Transformers (SwinMM), a novel multi-view pipeline for enabling accurate and data-efficient self-supervised medical image analysis.
MacLaSa: Multi-Aspect Controllable Text Generation via Efficient Sampling from Compact Latent Space
Multi-aspect controllable text generation aims to generate fluent sentences that possess multiple desired attributes simultaneously.
TRBoost: A Generic Gradient Boosting Machine based on Trust-region Method
Gradient Boosting Machines (GBMs) have demonstrated remarkable success in solving diverse problems by utilizing Taylor expansions in functional space.
Discovering Intrinsic Reward with Contrastive Random Walk
The aim of this paper is to demonstrate the efficacy of using Contrastive Random Walk as a curiosity method to achieve faster convergence to the optimal policy. Contrastive Random Walk defines the transition matrix of a random walk with the help of neural networks.
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net Discriminators
In this paper, we present A-ESRGAN, a GAN model for blind SR tasks featuring an attention U-Net based, multi-scale discriminator that can be seamlessly integrated with other generators.
