Search Results for author:
Found 15 papers, 9 papers with code
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.
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.
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.
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.
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.
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.
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.
A First Look into DeFi Oracles
In this paper, we present the first study of DeFi oracles deployed in practice.
Cryptography and Security
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
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.
Probabilistic K-means Clustering via Nonlinear Programming
In theory, we can solve the model by active gradient projection, while inefficiently.
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.
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 #2 on
Drug Discovery
on SIDER
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.
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.
