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Found 34 papers, 17 papers with code
StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion
Existing methods encounter a fundamental dilemma in learning humanoid locomotion: reinforcement learning with handcrafted rewards can achieve agile locomotion but produces unnatural gaits, while Generative Adversarial Imitation Learning (GAIL) with motion capture data yields natural movements but suffers from unstable training processes and restricted agility.
Towards Fast, Memory-based and Data-Efficient Vision-Language Policy
In this work, we propose LiteVLP, a lightweight, memory-based, and general-purpose vision-language policy generation model.
G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
Moreover, existing methods often struggle with the integration of extracted CF information with LLMs due to its implicit representation and the modality gap between graph structures and natural language explanations.
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation
Standard clothing asset generation involves creating forward-facing flat-lay garment images displayed on a clear background by extracting clothing information from diverse real-world contexts, which presents significant challenges due to highly standardized sampling distributions and precise structural requirements in the generated images.
GCoder: Improving Large Language Model for Generalized Graph Problem Solving
Furthermore, GCoder efficiently manages large-scale graphs with millions of nodes and diverse input formats, overcoming the limitations of previous models focused on the reasoning steps paradigm.
GLBench: A Comprehensive Benchmark for Graph with Large Language Models
To bridge this gap, we introduce GLBench, the first comprehensive benchmark for evaluating GraphLLM methods in both supervised and zero-shot scenarios.
GraphArena: Benchmarking Large Language Models on Graph Computational Problems
The "arms race" of Large Language Models (LLMs) demands novel, challenging, and diverse benchmarks to faithfully examine their progresses.
EDGE-LLM: Enabling Efficient Large Language Model Adaptation on Edge Devices via Layerwise Unified Compression and Adaptive Layer Tuning and Voting
Efficient adaption of large language models (LLMs) on edge devices is essential for applications requiring continuous and privacy-preserving adaptation and inference.
Effect of antibody levels on the spread of disease in multiple infections
We find that the antibody decay rate strongly affects the propagation process, and also that different network structures have different sensitivities to the antibody decay rate, and that changes in the antibody decay rate cause stronger changes in the propagation process in Barabasi Albert networks.
AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenario
While image-based virtual try-on has made significant strides, emerging approaches still fall short of delivering high-fidelity and robust fitting images across various scenarios, as their models suffer from issues of ill-fitted garment styles and quality degrading during the training process, not to mention the lack of support for various combinations of attire.
Approximating Multi-Dimensional and Multiband Signals
We study the problem of representing a discrete tensor that comes from finite uniform samplings of a multi-dimensional and multiband analog signal.
Image to Pseudo-Episode: Boosting Few-Shot Segmentation by Unlabeled Data
Few-shot segmentation (FSS) aims to train a model which can segment the object from novel classes with a few labeled samples.
GraphWiz: An Instruction-Following Language Model for Graph Problems
Large language models (LLMs) have achieved impressive success across several fields, but their proficiency in understanding and resolving complex graph problems is less explored.
ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs
The results underscore the effectiveness of our model in achieving significant cross-dataset zero-shot transferability, opening pathways for the development of graph foundation models.
VS: Reconstructing Clothed 3D Human from Single Image via Vertex Shift
However current implicit function-based methods inevitably produce artifacts while existing deformation methods are difficult to reconstruct high-fidelity humans wearing loose clothing.
Differentiable Micro-Mesh Construction
Micro-mesh (u-mesh) is a new graphics primitive for compact representation of extreme geometry consisting of a low-polygon base mesh enriched by per micro-vertex displacement.
AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunities
In the pharmaceutical industry, the use of artificial intelligence (AI) has seen consistent growth over the past decade.
A Survey of Graph Meets Large Language Model: Progress and Future Directions
First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i. e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks.
All Data on the Table: Novel Dataset and Benchmark for Cross-Modality Scientific Information Extraction
To close this gap in data availability and enable cross-modality IE, while alleviating labeling costs, we propose a semi-supervised pipeline for annotating entities in text, as well as entities and relations in tables, in an iterative procedure.
Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework
This task faces two primary challenges: providing a comprehensive and rigorous error quantification in CI estimation, and addressing the distributional shift that results from discrepancies between the distribution induced by the target policy and the offline data-generating process.
FocalDreamer: Text-driven 3D Editing via Focal-fusion Assembly
While text-3D editing has made significant strides in leveraging score distillation sampling, emerging approaches still fall short in delivering separable, precise and consistent outcomes that are vital to content creation.
Gentopia: A Collaborative Platform for Tool-Augmented LLMs
We present gentopia, an ALM framework enabling flexible customization of agents through simple configurations, seamlessly integrating various language models, task formats, prompting modules, and plugins into a unified paradigm.
Koopman-type inverse operator for linear non-minimum phase systems with disturbances
In this paper, a novel Koopman-type inverse operator for linear time-invariant non-minimum phase systems with stochastic disturbances is proposed.
3DQD: Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process
We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc.
Efficient 3D Object Reconstruction using Visual Transformers
Reconstructing a 3D object from a 2D image is a well-researched vision problem, with many kinds of deep learning techniques having been tried.
Quasi-optimal Reinforcement Learning with Continuous Actions
Many real-world applications of reinforcement learning (RL) require making decisions in continuous action environments.
Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process
We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc.
Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters
In this paper, we introduce the Harry Potter Dialogue (HPD) dataset, designed to advance the study of dialogue agents and character alignment.
TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases
Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios.
Learning Entity Linking Features for Emerging Entities
Entity linking (EL) is the process of linking entity mentions appearing in text with their corresponding entities in a knowledge base.
Community Question Answering Entity Linking via Leveraging Auxiliary Data
Community Question Answering (CQA) platforms contain plenty of CQA texts (i. e., questions and answers corresponding to the question) where named entities appear ubiquitously.
Entity Linking Meets Deep Learning: Techniques and Solutions
Entity linking (EL) is the process of linking entity mentions appearing in web text with their corresponding entities in a knowledge base.
Phase Transitions in Frequency Agile Radar Using Compressed Sensing
FAR has improved anti-jamming performance over traditional pulse-Doppler radars under complex electromagnetic circumstances.
Image Translation via Fine-grained Knowledge Transfer
Extensive experiments well demonstrate the effectiveness and feasibility of our framework in different image-translation tasks.
