Search Results for author:
Found 38 papers, 14 papers with code
Building Program Vector Representations for Deep Learning
In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis.
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning
People can learn a wide range of tasks from their own experience, but can also learn from observing other creatures.
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation
Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator.
Meta-Reinforcement Learning of Structured Exploration Strategies
Exploration is a fundamental challenge in reinforcement learning (RL).
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
We show that we can learn continuous latent representations of trajectories, which are effective in solving temporally extended and multi-stage problems.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Learning to Drive from Simulation without Real World Labels
Simulation can be a powerful tool for understanding machine learning systems and designing methods to solve real-world problems.
Difference Attention Based Error Correction LSTM Model for Time Series Prediction
In this paper, we propose a novel model for time series prediction in which difference-attention LSTM model and error-correction LSTM model are respectively employed and combined in a cascade way.
ATG-PVD: Ticketing Parking Violations on A Drone
In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD).
Learning geometry-image representation for 3D point cloud generation
We study the problem of generating point clouds of 3D objects.
Ground-aware Monocular 3D Object Detection for Autonomous Driving
We further verify the power of the proposed module with a neural network designed for monocular depth prediction.
Ranked #5 on
Monocular 3D Object Detection
on KITTI Cars Hard
YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection
Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs.
3D Object Detection From Stereo Images
Disparity Estimation
+3
Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning
In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs.
Unified Group Fairness on Federated Learning
We validate the advantages of the FMDA-M algorithm with various kinds of distribution shift settings in experiments, and the results show that FMDA-M algorithm outperforms the existing fair FL algorithms on unified group fairness.
csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps
To provide a solution to the aforementioned problems, in this letter, we propose a novel system termed csBoundary to automatically detect road boundaries at the city scale for HD map annotation.
RNGDet: Road Network Graph Detection by Transformer in Aerial Images
To provide a solution to these problems, we propose a novel approach based on transformer and imitation learning in this paper.
Integrating Dependency Tree Into Self-attention for Sentence Representation
Recent progress on parse tree encoder for sentence representation learning is notable.
Tackling Long-Tailed Category Distribution Under Domain Shifts
Two new datasets were proposed for this problem, named AWA2-LTS and ImageNet-LTS.
Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation
The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each source with an independent treatment assignment mechanism).
CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation
Due to the use of the DETR-like transformer network, CenterLineDet can handle complicated graph topology, such as lane intersections.
RNGDet++: Road Network Graph Detection by Transformer with Instance Segmentation and Multi-scale Features Enhancement
To annotate road network graphs effectively and efficiently, automatic algorithms for road network graph detection are demanded.
Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction
3D bounding boxes are a widespread intermediate representation in many computer vision applications.
Random Feature Models for Learning Interacting Dynamical Systems
Particle dynamics and multi-agent systems provide accurate dynamical models for studying and forecasting the behavior of complex interacting systems.
Nonlinear Intensity, Scale and Rotation Invariant Matching for Multimodal Images
We present an effective method for the matching of multimodal images.
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving
Predicting accurate depth with monocular images is important for low-cost robotic applications and autonomous driving.
Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN
Existing state-of-the-art methods are often unable to capture meaningful uncertainty in challenging or ambiguous scenes, and as such can cause critical errors in high-performance applications.
Self-Supervised Instance Segmentation by Grasping
Leveraging this insight, we learn a grasp segmentation model to segment the grasped object from before and after grasp images.
Calibrating LLM-Based Evaluator
Recent advancements in large language models (LLMs) on language modeling and emergent capabilities make them a promising reference-free evaluator of natural language generation quality, and a competent alternative to human evaluation.
PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers
Approximating nonlinear differential equations using a neural network provides a robust and efficient tool for various scientific computing tasks, including real-time predictions, inverse problems, optimal controls, and surrogate modeling.
Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training
Monocular 3D object detection plays a crucial role in autonomous driving.
Democratizing Reasoning Ability: Tailored Learning from Large Language Model
In this paper, we propose a tailored learning approach to distill such reasoning ability to smaller LMs to facilitate the democratization of the exclusive reasoning ability.
Disentangle Estimation of Causal Effects from Cross-Silo Data
Estimating causal effects among different events is of great importance to critical fields such as drug development.
Text Diffusion with Reinforced Conditioning
Diffusion models have demonstrated exceptional capability in generating high-quality images, videos, and audio.
HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition
Large language models (LLMs) have emerged as a promising alternative to expensive human evaluations.
Scalable Vision-Based 3D Object Detection and Monocular Depth Estimation for Autonomous Driving
Collectively, these contributions lay a robust foundation for the widespread adoption of vision-based 3D perception technologies in autonomous driving applications.
Harnessing Multi-Role Capabilities of Large Language Models for Open-Domain Question Answering
Open-domain question answering (ODQA) has emerged as a pivotal research spotlight in information systems.
Multiple Latent Space Mapping for Compressed Dark Image Enhancement
Since texture details intertwine with compression artifacts in compressed dark images, detail enhancement and blocking artifacts suppression contradict each other in image space.
CurbNet: Curb Detection Framework Based on LiDAR Point Cloud Segmentation
Moreover, we propose an adaptive weighted loss function group, specifically formulated to counteract the imbalance in the distribution of curb point clouds relative to other categories.
Plug-and-Play Grounding of Reasoning in Multimodal Large Language Models
The surge of Multimodal Large Language Models (MLLMs), given their prominent emergent capabilities in instruction following and reasoning, has greatly advanced the field of visual reasoning.
