Deep Learning
Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need
Recently, 3D point cloud deep learning has been making remarkable strides in various domains, e.g.,
SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning Y exiao He
The pre-trained Large Language Models (LLMs) can be adapted for many downstream tasks and tailored to align with human preferences through fine-tuning.
PaCE: Parsimonious Concept Engineering for Large Language Models
Large Language Models (LLMs) are being used for a wide variety of tasks.
PersonalSum: A User-Subjective Guided Personalized Summarization Dataset for Large Language Models
Models (LLMs) can sometimes surpass those annotated by experts, such as journalists, according to human evaluations. However, there is limited research on whether these generic summaries meet the individual needs of ordinary people.
MVInpainter: Learning Multi-View Consistent Inpainting to Bridge 2D and 3D Editing
Novel View Synthesis (NVS) and 3D generation have recently achieved prominent improvements.
ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence
Neural ODEs (NODEs) are continuous-time neural networks (NNs) that can process data without the limitation of time intervals.
Elucidating the Design Space of Dataset Condensation
D ataset C ondensation ( EDC), establishes a benchmark for both small and large-scale dataset condensation.