Molecular Synthesis Planning: Transformer Models and Atom Mapping

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Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Sequence to Sequence Models: Overview and Attention Mechanisms

Explores sequence to sequence models, attention mechanisms, and their role in addressing model limitations and improving interpretability.

Deep Splines: Unifying Framework for Deep Neural Networks

Introduces a functional framework for deep neural networks with adaptive piecewise-linear splines, focusing on biomedical image reconstruction and the challenges of deep splines.

Splines and Machine Learning

Explores supervised learning as an ill-posed problem and the integration of sparse adaptive splines into neural architectures.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Reinforcement Learning Concepts

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Contextual Representations: ELMO and BERT Overview

Covers contextual representations in NLP, focusing on ELMO and BERT architectures and their applications in various tasks.

Self-Supervised Image Representation Learning

Presents BYOL, a self-supervised image representation learning method achieving state-of-the-art results without negative pairs.

Model Analysis

Explores neural model analysis in NLP, covering evaluation, probing, and ablation studies to understand model behavior and interpretability.

Transformers in Vision

Explores the evolution of visual intelligence models, focusing on Transformers and their applications in computer vision and natural language processing.