98 citations · 352 across the 15 of their papers we have counts for
4 papers · 1 filter
Human-in-the-Loop Interpretability Prior
Isaac Lage, Andrew Slavin Ross, Been Kim +2
We often desire our models to be interpretable as well as accurate. Prior work on optimizing models for interpretability has relied on easy-to-quantify proxies for interpretability…
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation
Menaka Narayanan, Emily Chen, Jeffrey He +3
Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions. However, exactly what ki…
Estimating scale-invariant future in continuous time
Zoran Tiganj, Samuel J. Gershman, Per B. Sederberg +1
Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous tim…
Evaluating Compositionality in Sentence Embeddings
Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller +2
An important challenge for human-like AI is compositional semantics. Recent research has attempted to address this by using deep neural networks to learn vector space embeddings of…