Specific molecular recognition by proteins, for example, protease enzymes, is critical for maintaining the robustness of key life processes. The substrate specificity landscape of a protease...

The development of graph neural networks (GNNs) stimulated the interest in GNNs for NP-hard problems, while most works apply GNNs for NP-hard problems by empirical intuition and experimental trials...

Modern sequencing technology has produced a vast quantity of proteomic data, which has been key to the development of various deep learning models within the field. However, there are still...

The behavior of many dynamical systems follow complex, yet still unknown partial differential equations (PDEs). While several machine learning methods have been proposed to learn PDEs directly from...

Understanding the interactions between novel drugs and target proteins is fundamentally important in disease research as discovering drug-protein interactions can be an exceptionally time-consuming...

Deep neural networks represent the gold standard for image classification and many other tasks. However, they usually need large amounts of data to reach superior performance. In this work, we...

In recent years, Capsule Networks (CapsNets) have achieved promising results in tasks in the object recognition task thanks to their invariance characteristics towards pose and lighting. They have...

In recent years, Capsule Networks (CapsNets) have achieved promising results in tasks in the object recognition task thanks to their invariance characteristics towards pose and lighting. They have...

Log files are files that record events, messages, or transactions. Logs are rich containers of data because they can store a sequence of structured textual and numerical data. Many sequential forms...

We present a reinforcement learning (RL) based guidance system for automated theorem proving geared towards Finding Longer Proofs (FLoP). FLoP is a step towards learning to reason by analogy...

Wildfire has become an unavoidable natural disaster that continues to threaten fire-prone communities and the frequency is expected to increase due to climate change. Therefore, predicting a...

Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning and related...

The established approach to protein contact prediction frames the task as one of graph selection, extracting contacts by estimating the parameters of a Potts model. Another approach has recently...

Graph Convolutional Networks (GCNs) have achieved impressive empirical advancement across a wide variety of graph-related applications. Despite their great success, training GCNs on large graphs...

Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex...

Many Question Answering (QA) tasks have been studied in NLP and employed to evaluate the progress of machine intelligence. One kind of QA tasks, such as Machine Reading Comprehension QA, is well...

As an important problem of causal inference, we discuss the estimation of treatment effects under the existence of unobserved confounding. By representing the confounder as a latent variable, we...

A single gene can encode for different protein versions through a process called alternative splicing. Since proteins play major roles in cellular functions, aberrant splicing profiles can result...

Estimating future events is a difficult task. Unlike humans, machine learning ap- proaches are not regularized by a natural understanding of physics. In the wild, a plausible succession of events...

In recent years, there has been a resurgence in research on empirical methods for machine translation. Most of this research has been focused on high-resource, European languages. Despite the fact...

In the recent literature of Graph Neural Networks (GNN), the expressive power of models has been studied through their capability to distinguish if two given graphs are isomorphic or not. Since the...

Time-series learning is the bread and butter of data-driven *clinical decision support*, and the recent explosion in ML research has demonstrated great potential in various healthcare settings. At...

There have been increasing challenges to solve combinatorial optimization problems by machine learning. Khalil et al. (NeurIPS 2017) proposed an end-to-end reinforcement learning framework, which...

Segmented models are widely used to describe non-stationary sequential data with discrete change points. Their estimation usually requires solving a mixed discrete-continuous optimization problem...

Mesh-based simulations are central to modeling complex physical systems in many disciplines across science and engineering. Mesh representations support powerful numerical integration methods and...

A copy number variant (CNV) is a type of genetic mutation where a stretch of DNA is lost or duplicated once or multiple times. CNVs play important roles in the development of diseases and complex...

Quantum Neural Networks (QNNs) have been recently proposed as generalizations of classical neural networks to achieve the quantum speed-up. Despite the potential to outperform classical models...

This paper aims to solve a series of referential problems in sequence decoding caused by data sparsity and corpus scarce in low-resource Neural Machine Translation (NMT), including pronoun missing,...

The success of Convolutional Neural Networks (CNNs) in computer vision is mainly driven by their strong inductive bias, which is strong enough to allow CNNs to solve vision-related tasks with...

Searching for novel molecules with desired chemical properties is crucial in drug discovery. Existing work focuses on developing deep generative models to generate either sequences or chemical...

Predicting the behaviors of Hamiltonian systems has been drawing increasing attention in scientific machine learning. However, the vast majority of the literature was focused on predicting...

In this paper, we present a novel physics-rooted network structure that dramatically facilitates the learning of complex dynamic systems. Our method is inspired by the Vortex Method in fluid...

Recently, graph neural networks (GNN) have been demonstrated effective in various graph-based tasks. To obtain state-of-the-art (SOAT) data-specific GNN architectures, researchers turn to the...

Retrosynthesis—the process of identifying a set of reactants to synthesize a target molecule—is of vital importance to material design and drug discovery. Existing machine learning approaches based...

Understanding molecular dynamics is critical, especially for large flexible molecules like proteins. Protein function, as well as modulations thereof due to ligand binding or changes in environment...

We present a data-driven model for fluid simulation under Lagrangian representation. Our model uses graph to describe fluid field, where physical quantities are encoded as node and edge features....

Our work is concerned with the generation and targeted design of RNA, a type of genetic macromolecule that can adopt complex structures which influence their cellular activities and functions. The...