Search Results for author:
Found 21 papers, 8 papers with code
Token-level Direct Preference Optimization
Fine-tuning pre-trained Large Language Models (LLMs) is essential to align them with human values and intentions.
Thermal-NeRF: Neural Radiance Fields from an Infrared Camera
In recent years, Neural Radiance Fields (NeRFs) have demonstrated significant potential in encoding highly-detailed 3D geometry and environmental appearance, positioning themselves as a promising alternative to traditional explicit representation for 3D scene reconstruction.
Reinforcement Learning from Bagged Reward: A Transformer-based Approach for Instance-Level Reward Redistribution
Instead, the learner only obtains rewards at the ends of bags, where a bag is defined as a partial sequence of a complete trajectory.
Empirical Evidence for the Fragment level Understanding on Drug Molecular Structure of LLMs
AI for drug discovery has been a research hotspot in recent years, and SMILES-based language models has been increasingly applied in drug molecular design.
De novo Drug Design using Reinforcement Learning with Multiple GPT Agents
A central challenge in this field is to generate molecules with specific properties while also producing a wide range of diverse candidates.
StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences
To address this issue, multi-frame optical flow methods leverage adjacent frames to mitigate the local ambiguity.
A Region of Interest Focused Triple UNet Architecture for Skin Lesion Segmentation
Skin lesion segmentation is of great significance for skin lesion analysis and subsequent treatment.
Re-evaluating Retrosynthesis Algorithms with Syntheseus
The planning of how to synthesize molecules, also known as retrosynthesis, has been a growing focus of the machine learning and chemistry communities in recent years.
Elastic Interaction Energy-Informed Real-Time Traffic Scene Perception
Urban segmentation and lane detection are two important tasks for traffic scene perception.
Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers
Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort.
NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping
To bridge this gap, in this paper, we propose a novel NeRF-based LiDAR odometry and mapping approach, NeRF-LOAM, consisting of three modules neural odometry, neural mapping, and mesh reconstruction.
Retrosynthetic Planning with Dual Value Networks
Retrosynthesis, which aims to find a route to synthesize a target molecule from commercially available starting materials, is a critical task in drug discovery and materials design.
Ranked #1 on
Multi-step retrosynthesis
on USPTO-190
Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation
In this work, we propose using only screenshots/pixels as input for automated game testing and build a general game testing agent, Inspector, that can be easily applied to different games without deep integration with games.
You May Not Need Ratio Clipping in PPO
Furthermore, we show that ESPO can be easily scaled up to distributed training with many workers, delivering strong performance as well.
Return-Based Contrastive Representation Learning for Reinforcement Learning
Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL).
Suphx: Mastering Mahjong with Deep Reinforcement Learning
Artificial Intelligence (AI) has achieved great success in many domains, and game AI is widely regarded as its beachhead since the dawn of AI.
Demonstration Actor Critic
One approach leverages demonstration data in a supervised manner, which is simple and direct, but can only provide supervision signal over those states seen in the demonstrations.
Independence-aware Advantage Estimation
Most of existing advantage function estimation methods in reinforcement learning suffer from the problem of high variance, which scales unfavorably with the time horizon.
Breaking Inter-Layer Co-Adaptation by Classifier Anonymization
This study addresses an issue of co-adaptation between a feature extractor and a classifier in a neural network.
Expressway visibility estimation based on image entropy and piecewise stationary time series analysis
We assume fog and haze cause blurred images and that fog and haze can be considered as a piecewise stationary signal.
Visual Recognition Using Directional Distribution Distance
In computer vision, an entity such as an image or video is often represented as a set of instance vectors, which can be SIFT, motion, or deep learning feature vectors extracted from different parts of that entity.
