Search Results for author:
Found 16 papers, 3 papers with code
Consistent Image Layout Editing with Diffusion Models
Despite the great success of large-scale text-to-image diffusion models in image generation and image editing, existing methods still struggle to edit the layout of real images.
Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?
Our method generates pseudo-rewards through a self-supervised mechanism that leverages the inherent structure of both teacher and student responses, enabling reward learning without explicit external evaluation.
FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling
Speculative sampling has emerged as an important technique for accelerating the auto-regressive generation process of large language models (LLMs) by utilizing a draft-then-verify mechanism to produce multiple tokens per forward pass.
Collaborative Deterministic-Diffusion Model for Probabilistic Urban Spatiotemporal Prediction
Existing spatiotemporal prediction models are predominantly deterministic, focusing on primary spatiotemporal patterns.
Large Action Models: From Inception to Implementation
As AI continues to advance, there is a growing demand for systems that go beyond language-based assistance and move toward intelligent agents capable of performing real-world actions.
RuAG: Learned-rule-augmented Generation for Large Language Models
In-context learning (ICL) and Retrieval-Augmented Generation (RAG) have gained attention for their ability to enhance LLMs' reasoning by incorporating external knowledge but suffer from limited contextual window size, leading to insufficient information injection.
Enabling Real-Time Conversations with Minimal Training Costs
To address this limitation, researchers have proposed duplex models.
Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network
Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone.
DragTex: Generative Point-Based Texture Editing on 3D Mesh
Creating 3D textured meshes using generative artificial intelligence has garnered significant attention recently.
A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models
The significant breakthroughs of Medical Multi-Modal Large Language Models (Med-MLLMs) renovate modern healthcare with robust information synthesis and medical decision support.
Large Language Models Are Neurosymbolic Reasoners
A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning.
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
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MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System
Multi-modal sarcasm detection has attracted much recent attention.
Multimodal Molecular Pretraining via Modality Blending
Self-supervised learning has recently gained growing interest in molecular modeling for scientific tasks such as AI-assisted drug discovery.
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.
RSPT: Reconstruct Surroundings and Predict Trajectories for Generalizable Active Object Tracking
However, building a generalizable active tracker that works robustly across different scenarios remains a challenge, especially in unstructured environments with cluttered obstacles and diverse layouts.
