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Found 19 papers, 9 papers with code
Learn to Think: Bootstrapping LLM Reasoning Capability Through Graph Learning
Large Language Models (LLMs) have achieved remarkable success across various domains.
GraphTEN: Graph Enhanced Texture Encoding Network
Texture recognition is a fundamental problem in computer vision and pattern recognition.
CrossView-GS: Cross-view Gaussian Splatting For Large-scale Scene Reconstruction
3D Gaussian Splatting (3DGS) has emerged as a prominent method for scene representation and reconstruction, leveraging densely distributed Gaussian primitives to enable real-time rendering of high-resolution images.
Toward Efficient Data-Free Unlearning
We propose a novel method, Inhibited Synthetic PostFilter (ISPF), to tackle this challenge from two perspectives: First, the Inhibited Synthetic, by reducing the synthesized forgetting information; Second, the PostFilter, by fully utilizing the retaining-related information in synthesized samples.
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach
To address these limitations, we propose a novel method that leverages the strengths of both LLM and GNN, allowing for the processing of graph data with any format and type of nodes and edges without the need for type information or special preprocessing.
Can MLLMs Understand the Deep Implication Behind Chinese Images?
To fill the gap, we introduce the **C**hinese **I**mage **I**mplication understanding **Bench**mark, **CII-Bench**, which aims to assess the higher-order perception and understanding capabilities of MLLMs for Chinese images.
Chemistry-Inspired Diffusion with Non-Differentiable Guidance
Recent advances in diffusion models have shown remarkable potential in the conditional generation of novel molecules.
Standard compliant video coding using low complexity, switchable neural wrappers
We employ a set of jointly optimized neural pre- and post-processors, wrapping a standard video codec, to encode videos at different resolutions.
GENIU: A Restricted Data Access Unlearning for Imbalanced Data
However, the imbalanced original data can cause trouble for these proxies and unlearning, particularly when the forgetting data consists predominantly of the majority class.
II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks.
Improving Gaussian Splatting with Localized Points Management
We apply point densification in the identified zones and then reset the opacity of the points in front of these regions, creating a new opportunity to correct poorly conditioned points.
CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling
To bridge the gap, we propose CPsyCoun, a report-based multi-turn dialogue reconstruction and evaluation framework for Chinese psychological counseling.
CPsyExam: A Chinese Benchmark for Evaluating Psychology using Examinations
In this paper, we introduce a novel psychological benchmark, CPsyExam, constructed from questions sourced from Chinese language examinations.
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.
Voxel-Mesh Hybrid Representation for Real-Time View Synthesis
The neural radiance fields (NeRF) have 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.
