12 citations · 45 across the 12 of their papers we have counts for
12 papers
Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations
Bingsheng Yao, Prithviraj Sen, Lucian Popa +2
Human-annotated labels and explanations are critical for training explainable NLP models. However, unlike human-annotated labels whose quality is easier to calibrate (e.g., with a…
Accurate Clinical Toxicity Prediction using Multi-task Deep Neural Nets and Contrastive Molecular Explanations
Bhanushee Sharma, Vijil Chenthamarakshan, Amit Dhurandhar +4
Explainable ML for molecular toxicity prediction is a promising approach for efficient drug development and chemical safety. A predictive ML model of toxicity can reduce experiment…
End-to-End Table Question Answering via Retrieval-Augmented Generation
Feifei Pan, Mustafa Canim, Michael Glass +2
Most existing end-to-end Table Question Answering (Table QA) models consist of a two-stage framework with a retriever to select relevant table candidates from a corpus and a reader…
Training Deep Neural Networks with Constrained Learning Parameters
Prasanna Date, Christopher D. Carothers, John E. Mitchell +2
Today's deep learning models are primarily trained on CPUs and GPUs. Although these models tend to have low error, they consume high power and utilize large amount of memory owing…
Explainable Deep RDFS Reasoner
Bassem Makni, Ibrahim Abdelaziz, James Hendler
Recent research efforts aiming to bridge the Neural-Symbolic gap for RDFS reasoning proved empirically that deep learning techniques can be used to learn RDFS inference rules. Howe…
Exploiting Class Learnability in Noisy Data
Matthew Klawonn, Eric Heim, James Hendler
In many domains, collecting sufficient labeled training data for supervised machine learning requires easily accessible but noisy sources, such as crowdsourcing services or tagged…