Search Results for author:
Found 7 papers, 3 papers with code
Self-Supervised Contrastive Learning for Long-term Forecasting
To this end, our contrastive loss incorporates global autocorrelation held in the whole time series, which facilitates the construction of positive and negative pairs in a self-supervised manner.
Ranked #1 on
Time Series Forecasting
on ETTh1 (720) Univariate
Conditional Generation of Periodic Signals with Fourier-Based Decoder
Periodic signals play an important role in daily lives.
Natural Attribute-based Shift Detection
Despite the impressive performance of deep networks in vision, language, and healthcare, unpredictable behaviors on samples from the distribution different than the training distribution cause severe problems in deployment.
Decoupled Kernel Neural Processes: Neural Network-Parameterized Stochastic Processes using Explicit Data-driven Kernel
To address this, we introduce a new neural stochastic processes, Decoupled Kernel Neural Processes (DKNPs), which explicitly learn a separate mean and kernel function to directly model the covariance between output variables in a data-driven manner.
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation
However, the distribution of max logits of each predicted class is significantly different from each other, which degrades the performance of identifying unexpected objects in urban-scene segmentation.
Ranked #4 on
Anomaly Detection
on Lost and Found
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment
We evaluate the out-of-distribution (OOD) detection performance of self-supervised learning (SSL) techniques with a new evaluation framework.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Neural Ordinary Differential Equations for Intervention Modeling
By interpreting the forward dynamics of the latent representation of neural networks as an ordinary differential equation, Neural Ordinary Differential Equation (Neural ODE) emerged as an effective framework for modeling a system dynamics in the continuous time domain.
