Search Results for author:
Found 15 papers, 8 papers with code
QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction
Employing Large Language Models (LLMs) for semantic parsing has achieved remarkable success.
Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments
We instantiate the path on structured environments and provide feedback to edit the path if anything goes wrong.
DyRoNet: Dynamic Routing and Low-Rank Adapters for Autonomous Driving Streaming Perception
The advancement of autonomous driving systems hinges on the ability to achieve low-latency and high-accuracy perception.
Animatable 3D Gaussian: Fast and High-Quality Reconstruction of Multiple Human Avatars
Neural radiance fields are capable of reconstructing high-quality drivable human avatars but are expensive to train and render.
MarkQA: A large scale KBQA dataset with numerical reasoning
We design a logic form in Python format called PyQL to represent the reasoning process of numerical reasoning questions.
Learning to simulate partially known spatio-temporal dynamics with trainable difference operators
Recently, using neural networks to simulate spatio-temporal dynamics has received a lot of attention.
Question Decomposition Tree for Answering Complex Questions over Knowledge Bases
To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA system called QDTQA.
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
Graph Neural Networks (GNNs) are de facto solutions to structural data learning.
A Universal PINNs Method for Solving Partial Differential Equations with a Point Source
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs)method emerges to be a promising method for solving both forward and inverse PDE problems.
Computing Real Numbers with Large-Population Protocols Having a Continuum of Equilibria
As a result, a number is computable by an LPP if and only if it is algebraic, namely, not a single transcendental number can be computed under this notion.
PhyIR: Physics-Based Inverse Rendering for Panoramic Indoor Images
In this paper, we present PhyIR, a neural inverse rendering method with a more completed SVBRDF representation and a physics-based in-network rendering layer, which can handle complex material and incorporate physical constraints by re-rendering realistic and detailed specular reflectance.
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i. e., PDEs with different physical parameters, boundary conditions, shapes of computation domains, etc.
Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE problems.
A Distributed Synchronous SGD Algorithm with Global Top-$k$ Sparsification for Low Bandwidth Networks
Current methods that use AllGather to accumulate the sparse gradients have a communication complexity of $O(kP)$, where $P$ is the number of workers, which is inefficient on low bandwidth networks with a large number of workers.
U-SLADS: Unsupervised Learning Approach for Dynamic Dendrite Sampling
Novel data acquisition schemes have been an emerging need for scanning microscopy based imaging techniques to reduce the time in data acquisition and to minimize probing radiation in sample exposure.
