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Found 8 papers, 3 papers with code
LLaVA-Gemma: Accelerating Multimodal Foundation Models with a Compact Language Model
We train a suite of multimodal foundation models (MMFM) using the popular LLaVA framework with the recently released Gemma family of large language models (LLMs).
Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data
Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains.
Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models
To this end, we introduce Cross-GAN Auditing (xGA) that, given an established "reference" GAN and a newly proposed "client" GAN, jointly identifies intelligible attributes that are either common across both GANs, novel to the client GAN, or missing from the client GAN.
GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanations
However, research focusing on counterfactual explanations, specifically for RL agents with visual input, is scarce and does not go beyond identifying defective agents.
Deep Generative Multimedia Children's Literature
Artistic work leveraging Machine Learning techniques is an increasingly popular endeavour for those with a creative lean.
Contrastive Identification of Covariate Shift in Image Data
Identifying covariate shift is crucial for making machine learning systems robust in the real world and for detecting training data biases that are not reflected in test data.
Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning
Our second user study investigates if counterfactual state explanations can help non-expert participants identify a flawed agent; we compare against a baseline approach based on a nearest neighbor explanation which uses images from the actual game.
Counterfactual States for Atari Agents via Generative Deep Learning
In this work, we introduce the concept of a counterfactual state to help humans gain a better understanding of what would need to change (minimally) in an Atari game image for the agent to choose a different action.
