On the public citation graph

Visualizing Deep Neural Networks for Text Analytics

https://doi.org/10.1109/pacificvis.2018.00031

0 signed · 47 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-10590-1_53openalex-snapshot

Visualizing and Understanding Convolutional Networks

inferredcites →https://doi.org/10.1007/978-3-319-27857-5_77openalex-snapshot

An Interactive Node-Link Visualization of Convolutional Neural Networks

inferredcites →https://doi.org/10.1016/0169-7439(87)80084-9openalex-snapshot

Principal component analysis

inferredcites →https://doi.org/10.1016/j.visinf.2017.01.006openalex-snapshot

Towards better analysis of machine learning models: A visual analytics perspective

inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot

Deep learning

inferredcites →https://doi.org/10.1109/2945.841119openalex-snapshot

Graph visualization and navigation in information visualization: A survey

inferredcites →https://doi.org/10.1109/cvpr.2012.6248110openalex-snapshot

Multi-column deep neural networks for image classification

inferredcites →https://doi.org/10.1109/cvpr.2015.7299155openalex-snapshot

Understanding deep image representations by inverting them

inferredcites →https://doi.org/10.1109/infovis.2005.37openalex-snapshot

Visualization of Graphs with Associated Timeseries Data

inferredcites →https://doi.org/10.1109/infvis.2004.1openalex-snapshot

A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations

inferredcites →https://doi.org/10.1109/msp.2012.2205597openalex-snapshot

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

inferredcites →https://doi.org/10.1109/tsmc.1981.4308636openalex-snapshot

Methods for Visual Understanding of Hierarchical System Structures

inferredcites →https://doi.org/10.1109/tvcg.2011.187openalex-snapshot

Developing and Evaluating Quilts for the Depiction of Large Layered Graphs

inferredcites →https://doi.org/10.1109/tvcg.2012.213openalex-snapshot

Design Study Methodology: Reflections from the Trenches and the Stacks

inferredcites →https://doi.org/10.1109/tvcg.2016.2598831openalex-snapshot

Towards Better Analysis of Deep Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/tvcg.2016.2598838openalex-snapshot

Visualizing the Hidden Activity of Artificial Neural Networks

inferredcites →https://doi.org/10.1109/tvcg.2017.2744158openalex-snapshot

LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks

inferredcites →https://doi.org/10.1109/tvcg.2017.2744358openalex-snapshot

DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks

inferredcites →https://doi.org/10.1109/tvcg.2017.2744718openalex-snapshot

ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models

inferredcites →https://doi.org/10.1109/vis.2005.73openalex-snapshot

Opening the Black Box - Data Driven Visualization of Neural Network

inferredcites →https://doi.org/10.1109/visual.2005.1532820openalex-snapshot

Opening the Black Box - Data Driven Visualization of Neural Networks

inferredcites →https://doi.org/10.1111/cgf.12106openalex-snapshot

Small Multiples, Large Singles: A New Approach for Visual Data Exploration

inferredcites →https://doi.org/10.1111/j.1467-8659.2011.01898.xopenalex-snapshot

Visual Analysis of Large Graphs: State‐of‐the‐Art and Future Research Challenges

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.1609/aimag.v36i4.2577openalex-snapshot

Research Priorities for Robust and Beneficial Artificial Intelligence

inferredcites →https://doi.org/10.18653/v1/n16-1082openalex-snapshot

Visualizing and Understanding Neural Models in NLP

inferredcites →https://doi.org/10.18653/v1/w15-4323openalex-snapshot

NCSU_SAS_SAM: Deep Encoding and Reconstruction for Normalization of Noisy Text

inferredcites →https://doi.org/10.3115/v1/d14-1181openalex-snapshot

Convolutional Neural Networks for Sentence Classification

inferredcites →https://doi.org/10.3115/v1/p14-1062openalex-snapshot

A Convolutional Neural Network for Modelling Sentences

inferredcites →https://doi.org/10.3115/v1/w15-1506openalex-snapshot

Relation Extraction: Perspective from Convolutional Neural Networks

inferredcites →https://doi.org/10.48550/arxiv.1506.02078openalex-snapshot

Visualizing and Understanding Recurrent Networks

inferredcites →https://doi.org/10.48550/arxiv.1506.06579openalex-snapshot

Understanding Neural Networks Through Deep Visualization

inferredcites →https://doi.org/10.48550/arxiv.1509.01626openalex-snapshot

Character-level Convolutional Networks for Text Classification

inferredcites →https://doi.org/10.48550/arxiv.1606.07757openalex-snapshot

A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks

inferredcites →https://doi.org/10.48550/arxiv.1611.05469openalex-snapshot

Embedding Projector: Interactive Visualization and Interpretation of Embeddings

inferredcites →https://doi.org/10.48550/arxiv.1708.03788openalex-snapshot

Direct-Manipulation Visualization of Deep Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s41095-020-0191-7openalex-snapshot

A survey of visual analytics techniques for machine learning

inferred← citeshttps://doi.org/10.1109/tase49443.2020.00019openalex-snapshot

Feature-oriented Design of Visual Analytics System for Interpretable Deep Learning based Intrusion Detection

inferred← citeshttps://doi.org/10.1109/tvcg.2020.2969185openalex-snapshot

Analyzing the Noise Robustness of Deep Neural Networks

inferred← citeshttps://doi.org/10.1109/tvcg.2023.3326591openalex-snapshot

Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Research

inferred← citeshttps://doi.org/10.1109/tvcg.2024.3357065openalex-snapshot

Visual Analytics for Machine Learning: A Data Perspective Survey

inferred← citeshttps://doi.org/10.1109/vast.2018.8802509openalex-snapshot

Analyzing the Noise Robustness of Deep Neural Networks

inferred← citeshttps://doi.org/10.1111/cgf.14034openalex-snapshot

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

inferred← citeshttps://doi.org/10.1111/cgf.14733openalex-snapshot

State of the Art of Visual Analytics for eXplainable Deep Learning

inferred← citeshttps://doi.org/10.1145/3486011.3486419openalex-snapshot

Visualization tool for teaching and learning Artificial Neural Networks

inferred← citeshttps://doi.org/10.1145/3532213.3532312openalex-snapshot

PointCNNVis: A visual analysis system for the interpretability of PointCNN

inferred← citeshttps://doi.org/10.48550/arxiv.2103.10229openalex-snapshot

Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.