Search Results for author:
Found 12 papers, 4 papers with code
From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization
This paper considers the problem of offline optimization, where the objective function is unknown except for a collection of ``offline" data examples.
Exactly Tight Information-Theoretic Generalization Error Bound for the Quadratic Gaussian Problem
We provide a new information-theoretic generalization error bound that is exactly tight (i. e., matching even the constant) for the canonical quadratic Gaussian (location) problem.
Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints
In this task, the correct camera pose is the key to generating accurate lanes, which can transform an image from perspective-view to the top-view.
Ranked #6 on
3D Lane Detection
on Apollo Synthetic 3D Lane
Individually Conditional Individual Mutual Information Bound on Generalization Error
We propose a new information-theoretic bound on generalization error based on a combination of the error decomposition technique of Bu et al. and the conditional mutual information (CMI) construction of Steinke and Zakynthinou.
End-to-end Lane Shape Prediction with Transformers
To tackle these issues, we propose an end-to-end method that directly outputs parameters of a lane shape model, using a network built with a transformer to learn richer structures and context.
Ranked #20 on
Lane Detection
on TuSimple
Multiple Sample Clustering
The clustering algorithms that view each object data as a single sample drawn from a certain distribution, Gaussian distribution, for example, has been a hot topic for decades.
Removing Rain in Videos: A Large-scale Database and A Two-stream ConvLSTM Approach
In this paper, we establish a large-scale video database for rain removal (LasVR), which consists of 316 rain videos.
MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video
Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video.
Ranked #5 on
Video Enhancement
on MFQE v2
DeepVS: A Deep Learning Based Video Saliency Prediction Approach
Hence, an object-to-motion convolutional neural network (OM-CNN) is developed to predict the intra-frame saliency for DeepVS, which is composed of the objectness and motion subnets.
Enhancing Quality for HEVC Compressed Videos
Our experimental results validate that our QE-CNN method is effective in enhancing quality for both I and P frames of HEVC videos.
Multimedia
Duality between Feature Selection and Data Clustering
The feature-selection problem is formulated from an information-theoretic perspective.
Detection of Cooperative Interactions in Logistic Regression Models
For the case when the underlying interaction graph is known to be acyclic, it is shown that a simple algorithm that is based on a maximum-weight spanning tree with respect to the plug-in estimates of the influences not only has strong theoretical performance guarantees, but can also outperform generic feature selection algorithms for recovering the interaction graph from i. i. d.
