REVIEW 9 cited by
Embedding Projector: Interactive Visualization and Interpretation of Embeddings
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Researchers and developers often need to explore the properties of a specific embedding, and one way to analyze embeddings is to visualize them. We present the Embedding Projector, a tool for interactive visualization and interpretation of embeddings.
Forward citations
Cited by 9 Pith papers
-
LatentFlow: Visual Analytics for Latent Space Analysis in Molecular Graph Neural Networks
LatentFlow is a visual analytics tool that tracks molecular GNN embedding clusters across layers and training states with a modified Sankey diagram, linking them to chemical substructures.
-
Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections
High-dimensional graph embeddings are projected to 2D views optimized for aesthetic metrics using a differentiable edge-crossing surrogate, outperforming standard layouts in tests.
-
Context-Aware Explanations for Spatialized Document Layouts
CAPE produces spatially grounded natural-language explanations for document layouts using pattern detection and multi-level context, rated more helpful than content-only baselines in a user study.
-
Evolving and Detecting Multi-Turn Deception using Geometric Signatures
Multi-objective genetic prompt optimization creates multi-turn deceptive datasets validated by humans, then detected with 0.89 recall using angular coverage, distance ratio, and linearity features in embeddings.
-
Visualising Information Flow in Word Embeddings with Diffusion Tensor Imaging
Applying diffusion tensor imaging to LLM hidden states produces a new visualisation of token-to-token and layer-to-layer 'information flow'.
-
Enhancing Transferability and Consistency in Cross-Domain Recommendations via Supervised Disentanglement
DGCDR applies post-hoc disentanglement to GNN-extracted user embeddings and uses a hierarchical contrastive decoder to supervise the separation, achieving state-of-the-art results in six cross-domain recommendation tasks.
-
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference
Proposes the CSI framework for co-designing visual interactions and deep learning models to expose and allow semantic control over intermediate reasoning processes, shown in a summarization case study.
-
Uncovering Latent Connections in Indigenous Heritage: Semantic Pipelines for Cultural Preservation in Brazil
The paper introduces two embedding pipelines and a visualization tool that reveal latent clusters and label errors in the Museu Nacional dos Povos Indígenas digital collection.
-
ClusterChat: Multi-Feature Search for Corpus Exploration
An open source system combining topic clustering, temporal filtering, lexical and semantic search, and retrieval augmented question answering provides a new way to explore four million PubMed abstracts.
Discussion (0). Continue with ORCID to comment.