Anchor Loss: Modulating Loss Scale Based on Prediction Difficulty

We propose a novel loss function that dynamically re-scales the cross entropy based on prediction difficulty regarding a sample. Deep neural network architectures in image classification tasks struggle to disambiguate visually similar objects. Likewise, in human pose estimation symmetric body parts often confuse the network with assigning indiscriminative scores to them. This is due to the output prediction, in which only the highest confidence label is selected without taking into consideration a measure of uncertainty. In this work, we define the prediction difficulty as a relative property coming from the confidence score gap between positive and negative labels. More precisely, the proposed loss function penalizes the network to avoid the score of a false prediction being significant. To demonstrate the efficacy of our loss function, we evaluate it on two different domains: image classification and human pose estimation. We find improvements in both applications by achieving higher accuracy compared to the baseline methods

Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning

Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C–N couplings, as well as Pauson–Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model

Graph Neural Networks for the Prediction of Substrate-Specific Organic Reaction Conditions

We present a systematic investigation using graph neural networks (GNNs) to model organic chemical reactions. To do so, we prepared a dataset collection of four ubiquitous reactions from the organic chemistry literature. We evaluate seven different GNN architectures for classification tasks pertaining to the identification of experimental reagents and conditions. We find that models are able to identify specific graph features that affect reaction conditions and lead to accurate predictions. The results herein show great promise in advancing molecular machine learning.Comment: 23 pages, 10 tables, 13 figures, to appear in the ICML 2020 Workshop on Graph Representation Learning and Beyond (GRLB

Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Machine-learned ranking models have been developed for the prediction of substrate-specific cross-coupling reaction conditions. Data sets of published reactions were curated for Suzuki, Negishi, and C–N couplings, as well as Pauson–Khand reactions. String, descriptor, and graph encodings were tested as input representations, and models were trained to predict the set of conditions used in a reaction as a binary vector. Unique reagent dictionaries categorized by expert-crafted reaction roles were constructed for each data set, leading to context-aware predictions. We find that relational graph convolutional networks and gradient-boosting machines are very effective for this learning task, and we disclose a novel reaction-level graph attention operation in the top-performing model

Representation of the Semantic Structures: from Discovery to Applications

The world surrounding us is full of structured entities. Scenes can be structured as the sum of objects arranged in space, objects can be decomposed into parts, and even small molecules are composed of atoms. As humans can organize and structure many concepts into smaller components, structural representation has become a powerful tool for various applications. Computer vision utilizes the part-based representation for classical object detection and categorization tasks, and computational neuroscientists use the structural representation to achieve an interpretable and low-dimensional encoding for behavior analysis. Furthermore, structural encoding of the molecules allows the application of machine learning models to optimize experimental reaction conditions in organic chemistry. To perform the high-level tasks described above, accurate detection of the structural component should be accomplished in advance. In this dissertation, we first propose methods to improve the pose estimation algorithm, where the task is to localize the semantic parts of the target instance from a 2D image. As the collection of a large number of human annotations is a prerequisite for the task to be successful, we aim to design a model that automatically discovers the structure information from the visual inputs without supervision. Lastly, we demonstrate the efficacy of the structural representation by applying it to various scientific applications such as behavior analysis and organic chemistry.</p