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. A video showcasing our solution can be found at: https://youtu.be/O6krGN-LTGI

The past decade has witnessed remarkable advancements in deep learning, owing to the emergence of various architectures, layers, objectives, and optimization techniques. These consist of a multitude of variations of attention, normalization, skip connections, transformer, and self-supervised learning methods, among others. Our goal is to furnish a comprehensive survey of significant recent contributions in these domains to individuals with a fundamental grasp of deep learning. Our aspiration is that an integrated and comprehensive approach of influential recent works will facilitate the formation of new connections between different areas of deep learning. In our discussion, we discuss multiple patterns that summarize the key strategies for many of the successful innovations over the last decade. We also include a discussion on recent commercially built, closed-source models such as OpenAI's GPT-4 and Google's PaLM 2.

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.

To better understand classifiers such as those based on deep learning models, we propose a `CLAssifier-DECoder' architecture (\emph{ClaDec}). \emph{ClaDec} facilitates the comprehension of the output of an arbitrary layer in a neural network. It uses a decoder that transforms the non-interpretable representation of the given layer to a representation that is more similar to the domain a human is familiar with, such as the training data. For example, in an image recognition problem, one can recognize what information a layer maintains by contrasting reconstructed images of \emph{ClaDec} with those of a conventional auto-encoder(AE) serving as reference. An extended version of \emph{ClaDec} also allows to trade human interpretability and fidelity by customizing explanations to individual needs. We evaluate our approach for image classification using Convolutional NNs. The qualitative evaluation highlights that reconstructed images (of the network to be explained) tend to replace specific objects with more generic object templates and provide smoother reconstructions. We also show that reconstructed visualizations using encodings from a classifier do capture more relevant information for classification than conventional AEs. This holds despite the fact that AEs contain more information on the original input.