Search Results for author:
Found 40 papers, 12 papers with code
LLM-Driven Dual-Level Multi-Interest Modeling for Recommendation
To alleviate the limited insights derived from individual users' behaviors, at the user-crowd level, we propose aggregating user cliques into synthesized users with rich behaviors for more comprehensive LLM-driven multi-interest analysis.
Learning Instruction-Following Policies through Open-Ended Instruction Relabeling with Large Language Models
Developing effective instruction-following policies in reinforcement learning remains challenging due to the reliance on extensive human-labeled instruction datasets and the difficulty of learning from sparse rewards.
FRESA:Feedforward Reconstruction of Personalized Skinned Avatars from Few Images
We present a novel method for reconstructing personalized 3D human avatars with realistic animation from only a few images.
M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality
We introduce Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality ($\text{M}^3\text{HF}$), a novel framework that integrates multi-phase human feedback of mixed quality into the MARL training process.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing
In the probing stage, PP selects a small yet crucial set of hidden states, based on residual importance, to run a few model layers ahead.
PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation
Diffusion models have made significant advancements in recent years.
FRESA: Feedforward Reconstruction of Personalized Skinned Avatars from Few Images
We present a novel method for reconstructing personalized 3D human avatars with realistic animation from only a few images.
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation
Knowledge distillation (KD)-based methods can alleviate these issues by transferring the knowledge to a small student, which trains a student based on the predictions of a cumbersome teacher.
Boolean Product Graph Neural Networks
To mitigate fluctuations in latent graph structure learning, this paper proposes a novel Boolean product-based graph residual connection in GNNs to link the latent graph and the original graph.
Probability Passing for Graph Neural Networks: Graph Structure and Representations Joint Learning
To solve this problem, Latent Graph Inference (LGI) is proposed to infer a task-specific latent structure by computing similarity or edge probability of node features and then apply a GNN to produce predictions.
ZeroDDI: A Zero-Shot Drug-Drug Interaction Event Prediction Method with Semantic Enhanced Learning and Dual-Modal Uniform Alignment
Drug-drug interactions (DDIs) can result in various pharmacological changes, which can be categorized into different classes known as DDI events (DDIEs).
CEST-KAN: Kolmogorov-Arnold Networks for CEST MRI Data Analysis
Results: The water and CEST maps generated by both MLP and KAN were visually comparable to the MPLF results.
Towards Domain Adaptive Neural Contextual Bandits
Our approach learns a bandit model for the target domain by collecting feedback from the source domain.
Safe Multi-agent Reinforcement Learning with Natural Language Constraints
These embeddings are then integrated into the multi-agent policy learning process, enabling agents to learn policies that minimize constraint violations while optimizing rewards.
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf
Communication is a fundamental aspect of human society, facilitating the exchange of information and beliefs among people.
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles.
Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation
However, the former methods struggle with optimal prompts to elicit the correct reasoning of LLMs due to the lack of task-specific feedback, leading to unsatisfactory recommendations.
ANIM: Accurate Neural Implicit Model for Human Reconstruction from a single RGB-D image
In this paper, we explore the benefits of incorporating depth observations in the reconstruction process by introducing ANIM, a novel method that reconstructs arbitrary 3D human shapes from single-view RGB-D images with an unprecedented level of accuracy.
Large Language Model with Graph Convolution for Recommendation
To adapt text-based LLMs with structured graphs, We use the LLM as an aggregator in graph processing, allowing it to understand graph-based information step by step.
Natural Language Reinforcement Learning
Reinforcement Learning (RL) has shown remarkable abilities in learning policies for decision-making tasks.
Safe Reinforcement Learning with Free-form Natural Language Constraints and Pre-Trained Language Models
Through the use of pre-trained LMs and the elimination of the need for a ground-truth cost, our method enhances safe policy learning under a diverse set of human-derived free-form natural language constraints.
A Local Appearance Model for Volumetric Capture of Diverse Hairstyle
In this paper, we present a novel method for creating high-fidelity avatars with diverse hairstyles.
MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment
Offline Multi-agent Reinforcement Learning (MARL) is valuable in scenarios where online interaction is impractical or risky.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Comprehensive Survey for Evaluation Methodologies of AI-Generated Music
In recent years, AI-generated music has made significant progress, with several models performing well in multimodal and complex musical genres and scenes.
ChessGPT: Bridging Policy Learning and Language Modeling
Thus, we propose ChessGPT, a GPT model bridging policy learning and language modeling by integrating data from these two sources in Chess games.
Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing
Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalanced.
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
While the majority of current approaches construct the reward redistribution in an uninterpretable manner, we propose to explicitly model the contributions of state and action from a causal perspective, resulting in an interpretable reward redistribution and preserving policy invariance.
NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture and Animation
The capture and animation of human hair are two of the major challenges in the creation of realistic avatars for the virtual reality.
Neural Strands: Learning Hair Geometry and Appearance from Multi-View Images
Furthermore, we introduce a novel neural rendering framework based on rasterization of the learned hair strands.
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications.
HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture
Capturing and rendering life-like hair is particularly challenging due to its fine geometric structure, the complex physical interaction and its non-trivial visual appearance. Yet, hair is a critical component for believable avatars.
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention
In this paper, we introduce a new generation of RL solvers that learn to minimise safety violations while maximising the task reward to the extent that can be tolerated by the safe policy.
Multi-Agent Constrained Policy Optimisation
To fill these gaps, in this work, we formulate the safe MARL problem as a constrained Markov game and solve it with policy optimisation methods.
Learning Compositional Radiance Fields of Dynamic Human Heads
In addition, we show that the learned dynamic radiance field can be used to synthesize novel unseen expressions based on a global animation code.
A Parametric Top-View Representation of Complex Road Scenes
In this paper, we address the problem of inferring the layout of complex road scenes given a single camera as input.
Geometry-Aware Recurrent Neural Networks for Active Visual Recognition
We present recurrent geometry-aware neural networks that integrate visual information across multiple views of a scene into 3D latent feature tensors, while maintaining an one-to-one mapping between 3D physical locations in the world scene and latent feature locations.
Semantic Photometric Bundle Adjustment on Natural Sequences
More recently, excellent results have been attained through the application of photometric bundle adjustment (PBA) methods -- which directly minimize the photometric error across frames.
Object-Centric Photometric Bundle Adjustment with Deep Shape Prior
Reconstructing 3D shapes from a sequence of images has long been a problem of interest in computer vision.
Virtual to Real Reinforcement Learning for Autonomous Driving
To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.
Correlated and Individual Multi-Modal Deep Learning for RGB-D Object Recognition
Specifically, we construct a pair of deep convolutional neural networks (CNNs) for the RGB and depth data, and concatenate them at the top layer of the network with a loss function which learns a new feature space where both correlated part and the individual part of the RGB-D information are well modelled.
