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Found 21 papers, 12 papers with code
$f$-PO: Generalizing Preference Optimization with $f$-divergence Minimization
Preference optimization has made significant progress recently, with numerous methods developed to align language models with human preferences.
Geometric Trajectory Diffusion Models
In this work, we propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories.
TFG: Unified Training-Free Guidance for Diffusion Models
Given an unconditional diffusion model and a predictor for a target property of interest (e. g., a classifier), the goal of training-free guidance is to generate samples with desirable target properties without additional training.
CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion
Diffusion models have a tendency to exactly replicate their training data, especially when trained on small datasets.
RelBench: A Benchmark for Deep Learning on Relational Databases
We use RelBench to conduct the first comprehensive study of Relational Deep Learning (RDL) (Fey et al., 2024), which combines graph neural network predictive models with (deep) tabular models that extract initial entity-level representations from raw tables.
Img2CAD: Reverse Engineering 3D CAD Models from Images through VLM-Assisted Conditional Factorization
Reverse engineering 3D computer-aided design (CAD) models from images is an important task for many downstream applications including interactive editing, manufacturing, architecture, robotics, etc.
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Geometric graph is a special kind of graph with geometric features, which is vital to model many scientific problems.
An Empirical Study of Data Ability Boundary in LLMs' Math Reasoning
Large language models (LLMs) are displaying emergent abilities for math reasoning tasks, and there is a growing attention on enhancing the ability of open-source LLMs through supervised fine-tuning (SFT). In this paper, we aim to explore a general data strategy for supervised data to help optimize and expand math reasoning ability. Firstly, we determine the ability boundary of reasoning paths augmentation by identifying these paths' minimal optimal set. Secondly, we validate that different abilities of the model can be cumulatively enhanced by Mix of Minimal Optimal Sets of corresponding types of data, while our models MMOS achieve SOTA performance on series base models under much lower construction costs. Besides, we point out GSM-HARD is not really hard and today's LLMs no longer lack numerical robustness. Also, we provide an Auto Problem Generator for robustness testing and educational applications. Our code and data are publicly available at https://github. com/cyzhh/MMOS.
Ranked #2 on
Math Word Problem Solving
on ASDiv-A
(using extra training data)
Equivariant Graph Neural Operator for Modeling 3D Dynamics
Comprehensive experiments in multiple domains, including particle simulations, human motion capture, and molecular dynamics, demonstrate the significantly superior performance of EGNO against existing methods, thanks to the equivariant temporal modeling.
Crystal Structure Prediction by Joint Equivariant Diffusion
To be specific, DiffCSP jointly generates the lattice and atom coordinates for each crystal by employing a periodic-E(3)-equivariant denoising model, to better model the crystal geometry.
Structure-Aware DropEdge Towards Deep Graph Convolutional Networks
Regarding the layer-dependent sampler, we interestingly find that increasingly sampling edges from the bottom layer yields superior performance than the decreasing counterpart as well as DropEdge.
Subequivariant Graph Reinforcement Learning in 3D Environments
Learning a shared policy that guides the locomotion of different agents is of core interest in Reinforcement Learning (RL), which leads to the study of morphology-agnostic RL.
DialogVCS: Robust Natural Language Understanding in Dialogue System Upgrade
In the constant updates of the product dialogue systems, we need to retrain the natural language understanding (NLU) model as new data from the real users would be merged into the existent data accumulated in the last updates.
Learning Physical Dynamics with Subequivariant Graph Neural Networks
Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics.
Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs
Pretraining molecular representation models without labels is fundamental to various applications.
Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation
After the great success of Vision Transformer variants (ViTs) in computer vision, it has also demonstrated great potential in domain adaptive semantic segmentation.
Ranked #7 on
Semantic Segmentation
on SYNTHIA-to-Cityscapes
Equivariant Graph Mechanics Networks with Constraints
The core of GMN is that it represents, by generalized coordinates, the forward kinematics information (positions and velocities) of a structural object.
Equivariant Graph Hierarchy-Based Neural Networks
Equivariant Graph neural Networks (EGNs) are powerful in characterizing the dynamics of multi-body physical systems.
Geometrically Equivariant Graph Neural Networks: A Survey
Many scientific problems require to process data in the form of geometric graphs.
Constrained Graph Mechanics Networks
In this manner, the geometrical constraints are implicitly and naturally encoded in the forward kinematics.
On Locality in Graph Learning via Graph Neural Network
In this paper, we study how the training set in the input graph effects the performance of GNN.
