38 research outputs found
Explaining Neural Networks by Decoding Layer Activations
We present a `CLAssifier-DECoder' architecture (\emph{ClaDec}) which
facilitates the comprehension of the output of an arbitrary layer in a neural
network (NN). It uses a decoder to transform the non-interpretable
representation of the given layer to a representation that is more similar to
the domain a human is familiar with. In an image recognition problem, one can
recognize what information is represented by a layer by contrasting
reconstructed images of \emph{ClaDec} with those of a conventional
auto-encoder(AE) serving as reference. We also extend \emph{ClaDec} to allow
the trade-off between human interpretability and fidelity. We evaluate our
approach for image classification using Convolutional NNs. We show that
reconstructed visualizations using encodings from a classifier capture more
relevant information for classification than conventional AEs. Relevant code is
available at \url{https://github.com/JohnTailor/ClaDec
Large Language Models for Difficulty Estimation of Foreign Language Content with Application to Language Learning
We use large language models to aid learners enhance proficiency in a foreign
language. This is accomplished by identifying content on topics that the user
is interested in, and that closely align with the learner's proficiency level
in that foreign language. Our work centers on French content, but our approach
is readily transferable to other languages. Our solution offers several
distinctive characteristics that differentiate it from existing
language-learning solutions, such as, a) the discovery of content across topics
that the learner cares about, thus increasing motivation, b) a more precise
estimation of the linguistic difficulty of the content than traditional
readability measures, and c) the availability of both textual and video-based
content. The linguistic complexity of video content is derived from the video
captions. It is our aspiration that such technology will enable learners to
remain engaged in the language-learning process by continuously adapting the
topics and the difficulty of the content to align with the learners' evolving
interests and learning objectives
Catheter Ablation of Ventricular Extrasystoles Originating from the Left Coronary Cusp
We describe the case of a 55-year-old man with frequent premature ventricular extrasystoles displaying inferior axis and positive QRS concordance in precordial leads. The arrhythmia was successfully ablated from the left coronary cusp. The electrocardiographic and electrophysiological characteristics of this arrhythmia are discussed
Reflective-net: learning from explanations
Humans possess a remarkable capability to make fast, intuitive decisions, but
also to self-reflect, i.e., to explain to oneself, and to efficiently learn
from explanations by others. This work provides the first steps toward
mimicking this process by capitalizing on the explanations generated based on
existing explanation methods, i.e. Grad-CAM. Learning from explanations
combined with conventional labeled data yields significant improvements for
classification in terms of accuracy and training time
Personalization of Deep Learning
We discuss training techniques, objectives and metrics toward personalization
of deep learning models. In machine learning, personalization addresses the
goal of a trained model to target a particular individual by optimizing one or
more performance metrics, while conforming to certain constraints. To
personalize, we investigate three methods of ``curriculum learning`` and two
approaches for data grouping, i.e., augmenting the data of an individual by
adding similar data identified with an auto-encoder. We show that both
``curriculuum learning'' and ``personalized'' data augmentation lead to
improved performance on data of an individual. Mostly, this comes at the cost
of reduced performance on a more general, broader dataset
Explaining classifiers by constructing familiar concepts
Interpreting a large number of neurons in deep learning is difficult. Our proposed ‘CLAssi- fier-DECoder’ architecture (ClaDec) facilitates the understanding of the output of an arbi- trary layer of neurons or subsets thereof. It uses a decoder that transforms the incompre- hensible representation of the given neurons to a representation that is more similar to the domain a human is familiar with