Search Results for author:
Found 14 papers, 1 papers with code
Proximal Oracles for Optimization and Sampling
Finally, we combine this proximal sampling oracle and ASF to obtain a Markov chain Monte Carlo method with non-asymptotic complexity bounds for sampling in semi-smooth and composite settings.
Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions
In this paper, we extensively analyze the robustness of seven leading TAD methods and obtain some interesting findings: 1) Existing methods are particularly vulnerable to temporal corruptions, and end-to-end methods are often more susceptible than those with a pre-trained feature extractor; 2) Vulnerability mainly comes from localization error rather than classification error; 3) When corruptions occur in the middle of an action instance, TAD models tend to yield the largest performance drop.
On Independent Samples Along the Langevin Diffusion and the Unadjusted Langevin Algorithm
We study the rate at which the initial and current random variables become independent along a Markov chain, focusing on the Langevin diffusion in continuous time and the Unadjusted Langevin Algorithm (ULA) in discrete time.
Variance Reduction and Low Sample Complexity in Stochastic Optimization via Proximal Point Method
This paper proposes a stochastic proximal point method to solve a stochastic convex composite optimization problem.
GreatSplicing: A Semantically Rich Splicing Dataset
In existing splicing forgery datasets, the insufficient semantic varieties of spliced regions cause a problem that trained detection models overfit semantic features rather than splicing traces.
RITA: Group Attention is All You Need for Timeseries Analytics
Timeseries analytics is of great importance in many real-world applications.
Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data
Monitoring drivers' mental workload facilitates initiating and maintaining safe interactions with in-vehicle information systems, and thus delivers adaptive human machine interaction with reduced impact on the primary task of driving.
SigVIC: Spatial Importance Guided Variable-Rate Image Compression
Variable-rate mechanism has improved the flexibility and efficiency of learning-based image compression that trains multiple models for different rate-distortion tradeoffs.
GotFlow3D: Recurrent Graph Optimal Transport for Learning 3D Flow Motion in Particle Tracking
By introducing a novel deep neural network based on recurrent Graph Optimal Transport, called GotFlow3D, we present an end-to-end solution to learn the 3D fluid flow motion from double-frame particle sets.
A Proximal Algorithm for Sampling from Non-convex Potentials
This work extends the recent algorithm in \cite{LiaChe21, LiaChe22} for non-smooth/semi-smooth log-concave distribution to the setting with non-convex potentials.
STDC-MA Network for Semantic Segmentation
Through this relationship, regions receiving much attention are integrated into the segmentation results, thereby reducing the unfocused regions of the input image and improving the effective utilization of multiscale features.
A Proximal Algorithm for Sampling
Departing from the standard smooth setting, the potentials are only assumed to be weakly smooth or non-smooth, or the summation of multiple such functions.
A Proximal Algorithm for Sampling from Non-smooth Potentials
One key contribution of this work is a fast algorithm that realizes the restricted Gaussian oracle for any convex non-smooth potential with bounded Lipschitz constant.
A Semantic-based Method for Unsupervised Commonsense Question Answering
In this paper, we present a novel SEmantic-based Question Answering method (SEQA) for unsupervised commonsense question answering.
