Search Results for author:
Found 23 papers, 5 papers with code
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.
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.
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.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
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.
