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Found 9 papers, 2 papers with code
DEFT: Data Efficient Fine-Tuning for Pre-Trained Language Models via Unsupervised Core-Set Selection
Recent advances have led to the availability of many pre-trained language models (PLMs); however, a question that remains is how much data is truly needed to fine-tune PLMs for downstream tasks?
Reprogramming Pretrained Language Models for Antibody Sequence Infilling
Results on antibody design benchmarks show that our model on low-resourced antibody sequence dataset provides highly diverse CDR sequences, up to more than a two-fold increase of diversity over the baselines, without losing structural integrity and naturalness.
Explainable Knowledge Graph Embedding: Inference Reconciliation for Knowledge Inferences Supporting Robot Actions
Results from our algorithmic evaluation affirm our model design choices, and the results of our user studies with non-experts support the need for the proposed inference reconciliation framework.
Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems
Intelligent decision support (IDS) systems leverage artificial intelligence techniques to generate recommendations that guide human users through the decision making phases of a task.
Semantic-Based Explainable AI: Leveraging Semantic Scene Graphs and Pairwise Ranking to Explain Robot Failures
Our framework autonomously captures the semantic information in a scene to produce semantically descriptive explanations for everyday users.
Explainable Activity Recognition for Smart Home Systems
In this work, we build on insights from Explainable Artificial Intelligence (XAI) techniques and introduce an explainable activity recognition framework in which we leverage leading XAI methods to generate natural language explanations that explain what about an activity led to the given classification.
Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery
In order for error explanations to be meaningful, we investigate what types of information within a set of hand-scripted explanations are most helpful to non-experts for failure and solution identification.
Explainable AI for System Failures: Generating Explanations that Improve Human Assistance in Fault Recovery
With the growing capabilities of intelligent systems, the integration of artificial intelligence (AI) and robots in everyday life is increasing.
Leveraging Rationales to Improve Human Task Performance
Machine learning (ML) systems across many application areas are increasingly demonstrating performance that is beyond that of humans.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
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