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Indexing Analytics to Instances: How Integrating a Dashboard can Support Design Education
With the goal of making the analytics intelligible to instructors, we developed a research artifact integrating a design analytics dashboard with design instances, and the design environment that students use to create them.
AdjointBackMapV2: Precise Reconstruction of Arbitrary CNN Unit's Activation via Adjoint Operators
Adjoint operators have been found to be effective in the exploration of CNN's inner workings [1].
Meaning Versus Information, Prediction Versus Memory, and Question Versus Answer
Brain science and artificial intelligence have made great progress toward the understanding and engineering of the human mind.
AdjointBackMap: Reconstructing Effective Decision Hypersurfaces from CNN Layers Using Adjoint Operators
There are several effective methods in explaining the inner workings of convolutional neural networks (CNNs).
Action Recognition and State Change Prediction in a Recipe Understanding Task Using a Lightweight Neural Network Model
One way to cope with this challenge is to explicitly model a simulator module that applies actions to entities and predicts the resulting outcome (Bosselut et al. 2018).
Plug-in, Trainable Gate for Streamlining Arbitrary Neural Networks
To tackle this problem we introduce a novel concept of a trainable gate function.
How Compact?: Assessing Compactness of Representations through Layer-Wise Pruning
This kind of analysis can help us identify the most compact representation within a deep neural network.
Comparing Sample-wise Learnability Across Deep Neural Network Models
We propose a measure of the learnability of a sample with a given deep neural network (DNN) model.
English Out-of-Vocabulary Lexical Evaluation Task
Unlike previous unknown nouns tagging task, this is the first attempt to focus on out-of-vocabulary (OOV) lexical evaluation tasks that do not require any prior knowledge.
