no code implementations • CVPR 2023 • Bowen Liu, Yu Chen, Rakesh Chowdary Machineni, Shiyu Liu, Hun-Seok Kim
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.
no code implementations • NeurIPS Workshop AI4Scien 2021 • Ralph Ma, Gabriel Hart Stocker Dreiman, Fiorella Ruggiu, Adam Joseph Riesselman, Bowen Liu, Keith James, Mohammad Sultan, Daphne Koller
DNA encoded libraries (DELs) are pooled, combinatorial compound collections where each member is tagged with its own unique DNA barcode.
1 code implementation • 23 Sep 2021 • Bowen Liu, Changwoo Lee, Ang Cao, Hun-Seok Kim
We propose a unified signal compression framework that uses a generative adversarial network (GAN) to compress heterogeneous signals.
no code implementations • 11 Aug 2021 • Liuhui Ding, Dachuan Li, Bowen Liu, Wenxing Lan, Bing Bai, Qi Hao, Weipeng Cao, Ke Pei
Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles.
1 code implementation • CVPR 2021 • Bowen Liu, Yu Chen, Shiyu Liu, Hun-Seok Kim
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.
2 code implementations • CVPR 2021 • Aixuan Li, Jing Zhang, Yunqiu Lv, Bowen Liu, Tong Zhang, Yuchao Dai
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.
1 code implementation • CVPR 2021 • Yunqiu Lv, Jing Zhang, Yuchao Dai, Aixuan Li, Bowen Liu, Nick Barnes, Deng-Ping Fan
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.
no code implementations • 9 May 2020 • Bowen Liu, Pawel Szalachowski, Jianying Zhou
In this paper, we present the first study of DeFi oracles deployed in practice.
Cryptography and Security
16 code implementations • NeurIPS 2020 • Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec
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
1 code implementation • CVPR 2020 • Jing Zhang, Xin Yu, Aixuan Li, Peipei Song, Bowen Liu, Yuchao Dai
In this paper, we propose a weakly-supervised salient object detection model to learn saliency from such annotations.
no code implementations • 10 Jan 2020 • Yujian Li, Bowen Liu, Zhaoying Liu, Ting Zhang
In theory, we can solve the model by active gradient projection, while inefficiently.
1 code implementation • 8 Dec 2019 • Bowen Liu, Ang Cao, Hun-Seok Kim
We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals.
9 code implementations • ICLR 2020 • Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec
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
2 code implementations • NeurIPS 2018 • Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, Jure Leskovec
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.
no code implementations • 6 Jun 2017 • Bowen Liu, Bharath Ramsundar, Prasad Kawthekar, Jade Shi, Joseph Gomes, Quang Luu Nguyen, Stephen Ho, Jack Sloane, Paul Wender, Vijay Pande
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.