Search Results for author:
Found 20 papers, 11 papers with code
Retrosynthetic reaction prediction using neural sequence-to-sequence models
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem.
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.
Strategies for Pre-training Graph Neural Networks
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.
Ranked #3 on
Molecular Property Prediction
on ToxCast
Unified Signal Compression Using Generative Adversarial Networks
We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals.
Probabilistic K-means Clustering via Nonlinear Programming
In theory, we can solve the model by active gradient projection, while inefficiently.
Weakly-Supervised Salient Object Detection via Scribble Annotations
In this paper, we propose a weakly-supervised salient object detection model to learn saliency from such annotations.
Open Graph Benchmark: Datasets for Machine Learning on Graphs
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
A First Look into DeFi Oracles
In this paper, we present the first study of DeFi oracles deployed in practice.
Cryptography and Security
Simultaneously Localize, Segment and Rank the Camouflaged Objects
With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects.
Uncertainty-aware Joint Salient Object and Camouflaged Object Detection
Visual salient object detection (SOD) aims at finding the salient object(s) that attract human attention, while camouflaged object detection (COD) on the contrary intends to discover the camouflaged object(s) that hidden in the surrounding.
Deep Learning in Latent Space for Video Prediction and Compression
The proposed method first learns the efficient lower-dimensional latent space representation of each video frame and then performs inter-frame prediction in that latent domain.
Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles
Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles.
Unified Signal Compression Using a GAN with Iterative Latent Representation Optimization
We propose a unified signal compression framework that uses a generative adversarial network (GAN) to compress heterogeneous signals.
Regression modeling on DNA encoded libraries
DNA encoded libraries (DELs) are pooled, combinatorial compound collections where each member is tagged with its own unique DNA barcode.
MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding
In this paper, we propose multi-mode video compression (MMVC), a block wise mode ensemble deep video compression framework that selects the optimal mode for feature domain prediction adapting to different motion patterns.
TrTr: A Versatile Pre-Trained Large Traffic Model based on Transformer for Capturing Trajectory Diversity in Vehicle Population
In this study, we apply the Transformer architecture to traffic tasks, aiming to learn the diversity of trajectories within vehicle populations.
VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data
In this work, we for the first time, present a video compression-based degradation model to synthesize low-resolution image data in the blind SISR task.
Quantum-Inspired Machine Learning for Molecular Docking
Traditional docking by searching for possible binding sites and conformations is computationally complex and results poorly under blind docking.
Category-Agnostic Pose Estimation for Point Clouds
The goal of object pose estimation is to visually determine the pose of a specific object in the RGB-D input.
VisionCLIP: An Med-AIGC based Ethical Language-Image Foundation Model for Generalizable Retina Image Analysis
Generalist foundation model has ushered in newfound capabilities in medical domain.
