Search Results for author:
Found 6 papers, 2 papers with code
Deep Mapper Graph and its Application to Visualize Plausible Pathways on High-Dimensional Distribution with Small Time-Complexity
Mapper is a topology based data analysis method that extracts topological features from high-dimensional data.
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning
To this end, inspired by recent works on denoising and the success of the cosine-similarity-based objective functions in representation learning, we propose the denoising Cosine-Similarity (dCS) loss.
Towards Understanding the Mechanism of Contrastive Learning via Similarity Structure: A Theoretical Analysis
We first prove that the formulation characterizes the structure of representations learned with the kernel-based contrastive learning framework.
Fast and Multi-aspect Mining of Complex Time-stamped Event Streams
Thanks to its concise but effective summarization, CubeScope can also detect the sudden appearance of anomalies and identify the types of anomalies that occur in practice.
Deep Clustering with a Constraint for Topological Invariance based on Symmetric InfoNCE
To address the problem, we propose a constraint utilizing symmetric InfoNCE, which helps an objective of deep clustering method in the scenario train the model so as to be efficient for not only non-complex topology but also complex topology datasets.
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
However, common ML losses do not give strong guarantees on how consistently the ML model performs for different domains, in particular, whether the model performs well on a domain at the expense of its performance on another domain.
