Search Results
SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer
However, previous methods usually suffered from discrete nature of point cloud and unstructured prediction of points in local regions, which makes it hard to reveal fine local geometric details on the complete shape.
Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer
Our insight into the detailed geometry is to introduce a skip-transformer in the SPD to learn the point splitting patterns that can best fit the local regions.
Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing
Recent research has shown that graph neural networks (GNNs) can learn policies for locomotion control that are as effective as a typical multi-layer perceptron (MLP), with superior transfer and multi-task performance (Wang et al., 2018; Huang et al., 2020).
Score-Based Point Cloud Denoising
Since $p * n$ is unknown at test-time, and we only need the score (i. e., the gradient of the log-probability function) to perform gradient ascent, we propose a neural network architecture to estimate the score of $p * n$ given only noisy point clouds as input.
The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs
Based on observations, we innovatively introduce the Heterophily Snowflake Hypothesis and provide an effective solution to guide and facilitate research on heterophilic graphs and beyond.
Ranked #3 on
Node Classification
on Texas
Unchecked and Overlooked: Addressing the Checkbox Blind Spot in Large Language Models with CheckboxQA
Checkboxes are critical in real-world document processing where the presence or absence of ticks directly informs data extraction and decision-making processes.
RedPajama: an Open Dataset for Training Large Language Models
In addition, we release RedPajama-V2, a massive web-only dataset consisting of raw, unfiltered text data together with quality signals and metadata.
Development of a free boundary Tokamak Equilibrium Solver (TES) for Advanced Study of Tokamak Equilibria
A free-boundary Tokamak Equilibrium Solver (TES), developed for advanced study of tokamak equilibra, is described with two distinctive features.
Plasma Physics
SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation
In this paper, we present SwiftKV, a novel model transformation and distillation procedure specifically designed to reduce the time and cost of processing prompt tokens while preserving high quality of generated tokens.
ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-Tree Complex Wavelet Representation and Contradict Channel Loss
Moreover, due to the limitation of existing snow datasets, to simulate the snow scenarios comprehensively, we propose a large-scale dataset called Comprehensive Snow Dataset (CSD).
Ranked #6 on
Single Image Desnowing
on CSD
