GUI agents can transform live web interfaces in real-time via DOM manipulations to deliver contextual assistance directly within the application.
Direct-Manipulation Visualization of Deep Networks
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
The recent successes of deep learning have led to a wave of interest from non-experts. Gaining an understanding of this technology, however, is difficult. While the theory is important, it is also helpful for novices to develop an intuitive feel for the effect of different hyperparameters and structural variations. We describe TensorFlow Playground, an interactive, open sourced visualization that allows users to experiment via direct manipulation rather than coding, enabling them to quickly build an intuition about neural nets.
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UNIPO is the first unified interactive visualization tool exposing token-level training dynamics of RL fine-tuning algorithms for LLMs through high-level overviews, step inspectors, and side-by-side comparisons.
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.
Web-Gewu delivers a scalable browser-accessible playground for robot reinforcement learning by offloading simulation and training to edge nodes while using the cloud only as a signaling relay for low-latency P2P streaming.
citing papers explorer
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Beyond Chat and Clicks: GUI Agents for In-Situ Assistance via Live Interface Transformation
GUI agents can transform live web interfaces in real-time via DOM manipulations to deliver contextual assistance directly within the application.
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UNIPO: Unified Interactive Visual Explanation for RL Fine-Tuning Policy Optimization
UNIPO is the first unified interactive visualization tool exposing token-level training dynamics of RL fine-tuning algorithms for LLMs through high-level overviews, step inspectors, and side-by-side comparisons.
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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.